Publications

@article{nokey,

title = {A New Approach to Detect COVID-19 in X-Ray Images of Indonesians},

author = {Anggraini Dwi Sensusiati and Aji Sapta Pramulen and Dewinda Julianensi Rumala and I Ketut Eddy Purnama and Alfian Nur Rosyid and Muhammad Amin and Rofida Lathifah},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=cWaY0cUAAAAJ&sortby=pubdate&citation_for_view=cWaY0cUAAAAJ:WHdLCjDvYFkC},

year  = {2021},

date = {2021-07-01},

journal = {Journal of Hunan University Natural Sciences},

volume = {48},

number = {6},

abstract = {The coronavirus disease 2019 or COVID-19 is a current global pandemic. This disease has a high prevalence in Indonesia, with 307,120 positive cases and 11,253 deaths on October 6, 2020. COVID-19 can be detected in various manners, one of which is through chest X-Ray. This present research applies an approach to COVID-19 detection through X-Ray that features preprocessing, augmentation, ELU activation function application, and optimizer use. The results show that the best performance is generated by applying the ReLU activation function at epoch 76 with a testing accuracy of 96.44%, the sensitivity of 97.4%, specificity of 95.95%, and DICE of 95.77%.},

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@article{nokey,

title = {Inisialisasi Otomatis Metode Level Set untuk Segmentasi Objek Overlapping pada Citra Panorama Gigi},

author = {Safri Adam and Agus Zainal Arifin},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=-5mgvWsAAAAJ&sortby=pubdate&citation_for_view=-5mgvWsAAAAJ:U5uP8zs9lfgC},

year  = {2021},

date = {2021-06-15},

journal = {Jurnal Teknologi Informasi dan Ilmu Komputer},

volume = {8},

number = {3},

pages = {429-438},

abstract = {Penelitian tentang segmentasi gigi individu telah banyak dilakukan dan memperoleh hasil yang baik. Namun, ketika dihadapkan kepada gigi overlap maka hal ini menjadi sebuah tantangan. Untuk memisahkan dua gigi overlap, maka perlu mengekstrak objek overlap terlebih dahulu. Metode level set banyak digunakan untuk melakukan segmentasi objek overlap, namun memiliki kelemahan yaitu perlu didefinisikan inisial awal metode level set secara manual oleh pengguna. Dalam penelitian ini diusulkan strategi inisialisasi otomatis pada metode level set untuk melakukan segmentasi gigi overlap menggunakan Hierarchical Cluster Analysis (HCA) pada citra panorama gigi. Tahapan strategi yang diusulkan terdiri dari preprocessing dimana di dalamnya ada proses perbaikan, rotasi dan cropping citra, dilanjutkan proses inisialisasi otomatis menggunakan algoritma HCA, dan yang terakhir segmentasi …},

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@article{nokey,

title = {Epileptic EEG signal classification using convolutional neural network based on multi-segment of EEG signal},

author = {Irwan Budi Santoso and Yudhi Adrianto and Anggraini Dwi Sensusiati and Diah Puspito Wulandari and I Purnama},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=cWaY0cUAAAAJ&sortby=pubdate&citation_for_view=cWaY0cUAAAAJ:1taIhTC69MYC},

year  = {2021},

date = {2021-04-30},

journal = {International Journal of Intelligent Engineering and Systems},

volume = {14},

number = {3},

pages = {160-176},

abstract = {High performance in the epileptic electroencephalogram (EEG) signal classification is an important step in diagnosing epilepsy. Furthermore, this classification is carried out to determine whether the EEG signal from a person's examination results is categorized as an epileptic signal or not (healthy). Several automated techniques have been proposed to assist neurologists in classifying these signals. In general, these techniques have yielded a high average accuracy in classification, but the performance still needs to be improved. Therefore, we propose a convolutional neural network based on multi-segment of EEG signals to classify epileptic EEG signals. This method is built to overcome data limitations in the convolutional neural network training process and add the ensemble combination process. The multi-segment of EEG signal is formed by splitting the signal without overlapping each channel and converting it into the spectrogram image based on the short-time Fourier transform value. The spectrogram image is then used as input for the convolutional neural network in in-depth training and testing. The convolutional neural network model of the training results is used to classify each EEG signal segment on each test channel before entering the ensemble combination stage for the final classification. To evaluate the performance of our proposed method, we used the Bonn EEG dataset. The dataset consists of five EEG records labelled as A, B, C, D, and E. The experiments on several datasets (AB-C, AB-D, AB-E, AB-CD, AB-CDE, and AB-CD-E) which were arranged from the dataset showed that our proposed method (with segment …},

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@article{nokey,

title = {Alveolar Bone Detection from Dental Cone Beam Computed Tomography using YOLOv3-tiny},

author = {Monica Widiasri and Agus Zainal Arifin and Nanik Suciati and Eha Renwi Astuti and Rarasmaya Indraswari},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=-5mgvWsAAAAJ&sortby=pubdate&citation_for_view=-5mgvWsAAAAJ:1wZ_wKGpLuwC},

year  = {2021},

date = {2021-04-28},

journal = {2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)},

pages = {1-6},

abstract = {Cone Beam Computed Tomography (CBCT) is a medical imaging technique widely used in dentistry including dental implant planning. To determine the size of the dental implant, it is necessary to detect the alveolar bone at the implant site. In this study, we propose automatic detection of alveolar bone from CBCT images of teeth using the YOLOv3-tiny method. The YOLOv3-tiny network architecture consists of a seven-layer convolution networks and six max-pooling layers in the Darknet-53 network with two output branch scale predictions. CBCT images of teeth obtained from 4 patients consisted of 800 coronal slices of 2D grayscale images, containing 830 alveolar bone annotations. Before the training process, the ground truth image annotation was made in the form of a bounding box on the alveolar bone object. The detection results of the YOLOv3-tiny model were compared with the detection results of the …},

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@article{nokey,

title = {Color Mapping for Volume Rendering Using Digital Imaging and Communications in Medicine Images},

author = {Aziz Fajar and Dwi Wahyu Santoso and Zico Ritonda Bahen and Riyanarto Sarno and Chastine Fatichah},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=QOMOtp0AAAAJ&sortby=pubdate&citation_for_view=QOMOtp0AAAAJ:zxqBrjVgvjwC},

year  = {2021},

date = {2021-04-28},

journal = {2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)},

pages = {1-5},

abstract = {This paper introduces an automatic colorization of 3D volumetric medical image based on brain Computed Tomography (CT) scan images. This volumetric 3D images are generated from Digital Imaging and Communications in Medicine (DICOM) images. This research renders the DICOM images to volumetric images using ray marching method. Then automatically colorized each brain parts by computing the transfer function based on the value of grayscale to compared to tissue density. Our proposed method focused on the value which color each parts of the brain as well as the use of ray marching algorithm to show colorized result in 3D.},

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@article{nokey,

title = {The Implementation of Artificial Intelligence in Geometry Validation for Psychomotor Aspect Assessment},

author = {Siska Arifiani and Umi Laili Yuhana and Ridho Rahman Hariadi and Faturochman Pranacahya Andrianto},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=Ja_cWdoAAAAJ&sortby=pubdate&citation_for_view=Ja_cWdoAAAAJ:jL-93Qbq4QoC},

year  = {2021},

date = {2021-04-26},

journal = {International Conference on Educational Assessment and Policy (ICEAP 2020)},

pages = {200-204},

abstract = {Nowadays, many various of geometry validation tools produced to validate the shapes drawn automatically. The tools are useful to create geometry shape, for example: vertices, edges, and faces. This paper proposed a technique in geometry validation using an artificial intelligence (AI). The proposed method is to develop a 3D virtual environment for VR application. To support these circumstances, we use Oculust Rift and leap motion control. First, user will create random shapes in a system and the system do the validation. Second, after the validation, the system will show a correct information if the shape is correct. However, the system will show an incorrect information, if the shape is incorrect. Each point of shape that drawn will be marked and will be checked by the system. Last, Leap Motion Controller will match the hand gesture and the shape result. A virtual world will be show in Oculus Rift. This is necessary for the user in order to create a real feeling in the virtual world and do the interaction using leap motion control. This research aims also to do an evaluation of psychomotor aspect for the user. Several students in elementary school already tested and have a high interest, since the application is very useful and interactive.},

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@article{nokey,

title = {Unsupervised Corpus Callosum Extraction for T2-FLAIR MRI Images},

author = {Evan Kusuma Susanto and Fadhel Akhmad Hizham and Riyanarto Sarno and Aziz Fajar},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=QOMOtp0AAAAJ&sortby=pubdate&citation_for_view=QOMOtp0AAAAJ:rlwtDmSc194C},

year  = {2021},

date = {2021-04-09},

journal = {2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT)},

pages = {116-121},

abstract = {Many algorithms and methods have been published regarding Corpus Callosum extraction from MRI images. However, all of these approaches use T1-weighted images or existing human brain atlas as their basis. We propose a new algorithm that can work with T2-FLAIR image. The basis of our method is from the observation that corpus callosum is located directly above a cavity inside the brain in almost every MRI images. We highlight the corpus callosum by first extracting the cavity inside the brain and then performing a morphological dilation upwards to get the rough location of the corpus callosum. Our evaluation shows that this method achieves an average of 76.90% in DSC when used to extract corpus callosum from 30 different images and 3 different subjects.},

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@article{nokey,

title = {Classification of Male and Female Sweat Odor in the Morning Using Electronic Nose},

author = {Irzal Ahmad Sabilla and Rifki Aulia Irawan and Riyanarto Sarno and Asra Al Fauzi and Dedy Rahman Wijaya and Rudy Gunawan},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=QOMOtp0AAAAJ&sortby=pubdate&citation_for_view=QOMOtp0AAAAJ:XyWThvt29VcC},

year  = {2021},

date = {2021-04-09},

journal = {2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT)},

number = {320-324},

abstract = {Biologically, there are two genders that are declared, which is male and female. Both genders have the same hormones but at different levels. The difference in the level of hormones causes both genders can be distinguished from several aspects, and one of them is from their sweat. In this study, we are using Electronic Nose (E-Nose) device for classify the gender between male and female based on their sweat odor and the time that is taken to get the sample is in the morning. E-Nose is a device that can identify various kinds of scents. The results obtained from this tool are signal waves that can be identified, compared, and processed. E-Nose has also been used in various fields. one of them is in the health sector. In this research the E-Nose consists of several sensors TGS, 822, 2612, 2620, 823, 826, 2603, 2600, 813, and SHT-15 connected to an Arduino. The data obtained were sampled and splitting 80 …},

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@article{nokey,

title = {Unsupervised Method for 3D Brain Magnetic Resonance Image Segmentation},

author = {Adi Setyo Nugroho and Aziz Fajar and Riyanarto Sarno and Chastine Fatichah and Achmad Fahmi and Sri Andreani Utomo and Francisca Notopuro},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=QOMOtp0AAAAJ&sortby=pubdate&citation_for_view=QOMOtp0AAAAJ:r655XaDZu5IC},

year  = {2021},

date = {2021-04-08},

journal = {2021 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)},

pages = {90-94},

abstract = {Research at Digital Imaging and Communication in Medicine (DICOM) is very useful research in the field of health. In brain images, the problem encountered is when you want to divide or segment each part of the brain. In previous studies, some of research are still segmenting from 2-dimensional images, where the results will be different for each image slice. Therefore, in this research, we conducted the Magnetic Resonance Image (MRI) segmentation of the brain from the 3-dimensional plane to prevent the information contained in the images from being lost. In the early stages, MRI images will be converted to NifTi format to obtain 3-dimensional volume. The pre-processing is added as a modification from previous research, such as, convert image to grayscale, bias field correction, and skull stripping method to remove the skull (non-brain tissue) so that only brain tissue remains from the human brain. The …},

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@article{nokey,

title = {Classification of Human Gender from Sweat Odor using Electronic Nose with Machine Learning Methods},

author = {Irzal Ahmad Sabilla and Zakiya Azizah Cahyaningtyas and Riyanarto Sarno and Asra Al Fauzi and Dedy Rahman Wijaya and Rudy Gunawan},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=QOMOtp0AAAAJ&sortby=pubdate&citation_for_view=QOMOtp0AAAAJ:XK2cf6JOk9AC},

year  = {2021},

date = {2021-04-08},

journal = {2021 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)},

pages = {109-115},

abstract = {Both human biological genders have the same hormone but at different levels. The difference in hormone levels makes the two genders distinguishable from several aspects. One of the things that are influenced by hormones is sweat. The odor of sweat is related to the apocrine glands found in human armpits. This experiment studied the classification of both genders based on daytime sweat in adult human armpits. The sampling method used an electronic nose (E-nose) system to collect the armpit sweat odor. The E-nose system sensor array consisted of seven sensors: TGS 822, TGS 2612, TGS 2620, TGS 826, TGS 2603, TGS 2600, and TGS 813. These sensors generate resistance ratio (Rs/Ro) values which are learned by the machine learning methods for classification and disease potential based on the volatile organic compound (VOC) in sweat. The study shows the male samples have higher amine gas …},

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@article{nokey,

title = {Slice Reconstruction on 3D Medical Image using Optical Flow Approach},

author = {Muhammad Ibadurrahman Arrasyid Supriyanto and Aziz Fajar and Riyanarto Sarno and Chastine Fatichah and Achmad Fahmi and Sri Andreani Utomo and Francisca Notopuro},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=_Dd7x80AAAAJ&sortby=pubdate&citation_for_view=_Dd7x80AAAAJ:ClCfbGk0d_YC},

year  = {2021},

date = {2021-04-08},

journal = {2021 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)},

pages = {242-246},

abstract = {High-resolution, high-quality imagery forms the basis of accurate 3D reconstructions. 3D reconstruction is obtained from a 3-dimensional stacking array or image sequence. In some cases the resulting slice image has a low resolution so that some parts miss information when reconstructed into 3D. The reconstruction of the new slice carried out by the researcher using the 3D interpolation technique has a disadvantage, namely that when the calculation is carried out using the structure similarity evaluation metrics, it is still unsatisfactory, therefore the following research will try to reconstruct a new slice using the optical flow approach to calculate the displacement vector field between the two adjacent slices and also we will evaluate the comparative suitability of various interpolation techniques using root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM). experimental …},

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@article{nokey,

title = {Classify Epileptic EEG Signals Using Extreme Support Vector Machine for Ictal and Muscle Artifact Detection},

author = {Baiq Siska Febriani Astuti and Santi Wulan Purnami and R Mohamad Atok and Wardah Rahmatul Islamiyah and Diah Puspito Wulandari and Anda Iviana Juniani},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=XI-Pg2QAAAAJ&sortby=pubdate&citation_for_view=XI-Pg2QAAAAJ:_kc_bZDykSQC},

year  = {2021},

date = {2021-03-01},

journal = {International Journal of Machine Learning and Computing},

volume = {11},

abstract = {EEG signals aids in diagnosing various wave signals recorded by the activities of the brain. It also produces unavoidable artifacts, in the recording process. The purpose of this study therefore is to detect ictal and artefact signals, with the aim of reducing interpretation errors especially those related to the muscle which are quite difficult to distinguish. The data used are EEG signal recording results obtained from Rumah Sakit Universitas Airlangga. It consisted of two classes, namely ictal and muscle artefact. The signal decomposition method used is a wavelet transform, known as DWT. While the extraction feature utilized, consist of quartile, maximum, minimum, mean and standard deviation. This study also utilized the SVM with linear, polynomial, RBF and ELM (ESVM) kernels. Research results shows that the ESVM classification time is faster than the SVM and other kernels. However, the values of accuracy, sensitivity, specificity and AUC are not better.},

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@article{nokey,

title = {Pemantauan Perhatian Publik terhadap Pandemi COVID-19 melalui Klasifikasi Teks dengan Deep Learning},

author = {Novrindah Alvi Hasanah and Nanik Suciati and Diana Purwitasari},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ekvoWE4AAAAJ&sortby=pubdate&citation_for_view=ekvoWE4AAAAJ:Mojj43d5GZwC},

year  = {2021},

date = {2021-02-28},

journal = {Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi)},

volume = {5},

number = {1},

pages = {193-202},

abstract = {Monitoring public concern in the surrounding environment to certain events is done to address changes in public behavior individually and socially. The results of monitoring public attention can be used as a benchmark for related parties in making the right policies and strategies to deal with changes in public behavior as a result of the COVID-19 pandemic. Monitoring public attention can be done using Twitter social media data because the users of the media are quite high, so that they can represent the aspirations of the general public. However, Twitter data contains varied topics, so a classification process is required to obtain data related to COVID-19. Classification is done by using word embedding variations (Word2Vec and fastText) and deep learning variations (CNN, RNN, and LSTM) to get the classification results with the best accuracy. The percentage of COVID-19 data based on the best accuracy is calculated to determine how high the public's attention is to the COVID-19 pandemic. Experiments were carried out with three scenarios, which were differentiated by the number of data trains. The classification results with the best accuracy are obtained by the combination of fasText and LSTM which shows the highest accuracy of 97.86% and the lowest of 93.63%. The results of monitoring public attention to the time vulnerability between June and October show that the highest public attention to COVID-19 is in June.},

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@article{nokey,

title = {Phase-based thresholding schemes for segmentation of fetal thigh cross-sectional region in ultrasound images},

author = {Fajar Astuti Hermawati and Handayani Tjandrasa and Nanik Suciati},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=DvVoH5AAAAAJ&sortby=pubdate&citation_for_view=DvVoH5AAAAAJ:4MWp96NkSFoC},

year  = {2021},

date = {2021-02-17},

journal = {Journal of King Saud University-Computer and Information Sciences},

abstract = {Measurement of thigh circumference has been used as an indication of nutritional adequacy and fetal growth in the womb. Fractional thigh volume is one of the variables for estimating fetal weight that is also calculated using the fetal thigh circumference. This cross-section of the fetal thigh is an intricate part of detecting because it has a less clear boundary and sometimes attached to other tissue in the ultrasound image. This study aims to establish a detection scheme and segmentation of the fetal thigh cross-sectional area automatically on ultrasound images. We propose a segmentation framework that integrates two phase-based thresholding schemes and a gap connecting step. In the first scheme, we combine the phase congruency of the local phase features and the fuzzy entropy method. The second thresholding scheme is a combination of the phase symmetry method and the saliency visual attention model …},

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@article{nokey,

title = {Identifying Degree-of-Concern on COVID-19 topics with text classification of Twitters},

author = {Novrindah Alvi Hasanah and Nanik Suciati and Diana Purwitasari},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ekvoWE4AAAAJ&sortby=pubdate&citation_for_view=ekvoWE4AAAAJ:WA5NYHcadZ8C},

year  = {2021},

date = {2021-02-16},

journal = {Register: Jurnal Ilmiah Teknologi Sistem Informasi},

volume = {7},

number = {1},

pages = {50-62},

abstract = {The COVID-19 pandemic has various impacts on changing people’s behavior socially and individually. This study identifies the Degree-of-Concern topic of COVID-19 through citizen conversations on Twitter. It aims to help related parties make policies for developing appropriate emergency response strategies in dealing with changes in people’s behavior due to the pandemic. The object of research is 12,000 data from verified Twitter accounts in Surabaya. The varied nature of Twitter needs to be classified to address specific COVID-19 topics. The first stage of classification is to separate Twitter data into COVID-19 and non-COVID-19. The second stage is to classify the COVID-19 data into seven classes: warnings and suggestions, notification of information, donations, emotional support, seeking help, criticism, and hoaxes. Classification is carried out using a combination of word embedding (Word2Vec and fastText) and deep learning methods (CNN, RNN, and LSTM). The trial was carried out with three scenarios with different numbers of train data for each scenario. The classification results show the highest accuracy is 97.3% and 99.4% for the first and second stage classification obtained from the combination of fastText and LSTM. The results show that the classification of the COVID-19 topic can be used to identify Degree-of-Concern properly. The results of the Degree-of-Concern identification based on the classification can be used as a basis for related parties in making policies to formulate appropriate emergency response strategies in dealing with changes in public behavior due to a pandemic.},

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@article{nokey,

title = {Eliminasi Non-Topic Menggunakan Pemodelan Topik untuk Peringkasan Otomatis Data Tweet dengan Konteks Covid-19},

author = {Putri Damayanti and Diana Purwitasari and Nanik Suciati},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ekvoWE4AAAAJ&sortby=pubdate&citation_for_view=ekvoWE4AAAAJ:HE397vMXCloC},

year  = {2021},

date = {2021-02-04},

urldate = {2021-02-04},

journal = {Jurnal Teknologi Informasi dan Ilmu Komputer},

volume = {8},

number = {1},

pages = {199-208},

abstract = {Akun twitter, seperti Suara Surabaya, dapat membantu menyebarkan informasi tentang COVID-19 meskipun ada bahasan lainnya seperti kecelakaan, kemacetan atau topik lain. Peringkasan teks dapat diimplementasikan pada kasus pembacaan data twitter karena banyaknya jumlah tweet yang tersedia, sehingga akan mempermudah dalam memperoleh informasi penting terkini terkait COVID-19. Jumlah variasi bahasan pada teks tweet mengakibatkan hasil ringkasan yang kurang baik. Oleh karena itu dibutuhkan adanya eliminasi tweet yang tidak berkaitan dengan konteks sebelum dilakukan peringkasan. Kontribusi penelitian ini adalah adanya metode pemodelan topik sebagai bagian tahapan dalam serangkaian proses eliminasi data. Metode pemodelan topik sebagai salah satu teknik eliminasi data dapat digunakan dalam berbagai kasus namun pada penelitian ini difokuskan pada COVID-19. Tujuannya adalah untuk mempermudah masyarakat memperoleh informasi terkini secara ringkas. Tahapan yang dilakukan adalah pra-pemrosesan, eliminasi data menggunakan pemodelan topik dan peringkasan otomatis. Penelitian ini menggunakan kombinasi beberapa metode word embedding, pemodelan topik dan peringkasan otomatis sebagai pembanding. Ringkasan diuji menggunakan metode ROUGE dari setiap kombinasi untuk ditemukan kombinasi terbaik dari penelitian ini. Hasil pengujian menunjukkan kombinasi metode Word2Vec, LSI dan TextRank memiliki nilai ROUGE terbaik yaitu 0.67. Sedangkan kombinasi metode TFIDF, LDA dan Okapi BM25 memiliki nilai ROUGE terendah yaitu 0.35.},

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@article{nokey,

title = {Fashion Meets Computer Vision: A Survey},

author = {Wen-Huang Cheng and Sijie Song and Chieh-Yun Chen and Shintami Chusnul Hidayati and Jiaying Liu},

url = {https://arxiv.org/abs/2003.13988},

year  = {2021},

date = {2021-01-28},

journal = {arXiv preprint arXiv:2003.13988},

abstract = {Fashion is the way we present ourselves to the world and has become one of the world's largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years. Given the rapid development, this paper provides a comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fashion: (1) Fashion detection includes landmark detection, fashion parsing, and item retrieval, (2) Fashion analysis contains attribute recognition, style learning, and popularity prediction, (3) Fashion synthesis involves style transfer, pose transformation, and physical simulation, and (4) Fashion recommendation comprises fashion compatibility, outfit matching, and hairstyle suggestion. For each task, the benchmark datasets and the evaluation protocols are summarized. Furthermore, we highlight promising directions for future research.},

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@article{nokey,

title = {Peningkatan Kontras Citra Mamografi Digital dengan Menggunakan CLAHE dan Contrast Stretching},

author = {Windy Deftia Mertiana and Tri Arief Sardjono and Nada Fitrieyatul Hikmah},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=id&user=5f0utZEAAAAJ&sortby=pubdate&citation_for_view=5f0utZEAAAAJ:_FxGoFyzp5QC},

year  = {2021},

date = {2021-01-25},

journal = {Jurnal Teknik ITS},

volume = {9},

number = {2},

pages = {A222-A227},

abstract = {Kanker menjadi salah satu penyebab utama angka kematian terbesar di dunia dan kanker payudara menjadi jenis kanker dengan prevalensi paling tinggi dialami oleh perempuan. Pendeteksian dini menggunakan screening mamografi menjadi langkah efektif untuk mengetahui keberadaan kanker payudara walaupun benjolan dalam bentuk gumpalan sering kali muncul dengan tipikal kontras rendah dan seringkali buram. Oleh karena itu, dibutuhkan dokter dan radiologis untuk mendiagnosis kanker payudara melalui citra mamografi. Namun, tidak menutup kemungkinan terdapat ketidakakuratan proses diagnosis mengingat keterbatasan visual dan objektivitas dari manusia itu sendiri. Beberapa tahun terakhir penelitan mengenai pendeteksian kanker payudara melalui citra mamografi banyak dilakukan. Untuk itu, pada penelitian ini dikembangkan sebuah metode untuk meningkatkan kualitas citra mamografi dan membantu proses pendeteksian kanker payudara berbasis tekstur. Perbaikan kualitas citra berbasis indirect contrast enhancement dilakukan dengan menggunakan metode Contrast Limited Adaptive Histogram Equalization dan contrast stretching yang dilakukan secara cascade. Melalui pengujian yang dilakukan terhadap 120 citra, didapatkan rata-rata nilai MSE dan PSNR citra yang telah melalui proses peningkatan kontras dengan metode CLAHE sebesar 65, 92 dan 29, 95dB. Sedangkan nilai rata-rata MSE dan PSNR citra hasil contrast stretching sebesar 57, 47 dan 30, 62dB. Sehingga dapat dikatakan metode yang diusulkan akan sangat membatu proses segmentasi pada penelitian selanjutnya.},

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}

@article{nokey,

title = {Dress with Style: Learning Style from Joint Deep Embedding of Clothing Styles and Body Shapes},

author = {Shintami Chusnul Hidayati and Ting Wei Goh and Ji-Sheng Gary Chan and Cheng-Chun Hsu and John See and Wong Lai Kuan and Kai-Lung Hua and Yu Tsao and Wen-Huang Cheng},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=hEnPIToAAAAJ&citation_for_view=hEnPIToAAAAJ:9ZlFYXVOiuMC},

year  = {2021},

date = {2021-01-08},

journal = {IEEE Transactions on Multimedia},

volume = {23},

pages = {365-377},

abstract = {Body shape is about proportion, and fashion style is all about dressing those proportions to look their very best. Figuring out the styles to suit a body shape can be a daunting task for many people. It is, therefore, essential to develop a framework for learning the compatibility of body shapes and clothing styles. Though fashion designers and fashion stylists have analyzed the correlation between human body shapes and fashion styles for a long time, this issue did not receive much attention in multimedia science. In this paper, we present a novel style recommender, on the basis of the user's body attributes. The rich amount of fashion styling knowledge from social big data is exploited for this purpose. We first construct a joint embedding of clothing styles and human body measurements with deep multimodal representation learning on a reference dataset that has been sorted to meet the fashion rules. We then …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {The Use of Pre and Post Processing to Enhance Mandible Segmentation using Active Contours on Dental Panoramic Radiography Images},

author = {Nur Nafi'iyah and Chastine Fatichah and Eha Renwi Astuti and Darlis Herumurti},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=_Dd7x80AAAAJ&sortby=pubdate&citation_for_view=_Dd7x80AAAAJ:Dip1O2bNi0gC},

year  = {2020},

date = {2020-12-10},

journal = {2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)},

pages = {661-666},

abstract = {Mandibular segmentation is indispensable to support the automation of the gender detection system based on the dental panoramic radiography image. However, the dental panoramic radiography image has low image contrast, the gray intensity value inhomogeneous, and the gray intensity value between the teeth and mandibular bone is almost indistinguishable. So, a good segmentation method is required to separate the mandible and teeth properly. This study aims to analyze the effect of the use of preprocessing and post-processing to enhance mandible segmentation on dental panoramic radiography images properly. In the preprocessing, we use contrast enhancement and Gaussian filters to make the mandibular area more prominent. Meanwhile, in the post-processing, we use erosion and opening morphology to remove the tooth area attached to the mandible. The mandibular segmentation uses the …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Detection of COVID-19 on Chest X-Ray Images using Inverted Residuals Structure-Based Convolutional Neural Networks},

author = {Tita Karlita and Eko Mulyanto Yuniarno and I Ketut Eddy Purnama and Mauridhi Hery Purnomo},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=_bttBQEAAAAJ&sortby=pubdate&citation_for_view=_bttBQEAAAAJ:XAp-VaTZjjwC},

year  = {2020},

date = {2020-11-24},

journal = {2020 3rd International Conference on Information and Communications Technology (ICOIACT)},

pages = {371-376},

abstract = {China officially reported the COVID-19 coronavirus's existence to the World Health Organization (WHO) on December 31, 2019. Since then, it has spread and has infected millions of people around the world. COVID-19 is a highly contagious disease and it can cause severe respiratory distress. In severe cases it can result in failure of the function of organs simultaneously. Recent studies have shown that chest X-rays of patients suffering from COVID-19 show the specific characteristics of those infected with the virus. This paper presents a method to detect the presence of COVID-19 on chest X-ray images based on inverted residuals structure implemented in MobileNetV2 as a base model. We also explore the performance of using a Fully connected layer with dropout and using the Global Average Pooling layer as top layers of the base model to classify each image into COVID-19 or NonCOVID-19. Our proposed …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Open Set Deep Networks Based on Extreme Value Theory (EVT) for Open Set Recognition in Skin Disease Classification},

author = {Yordan Yasin and Dewinda Julianensi Rumala and Mauridhi Hery Purnomo and Anak Agung Putri Ratna and Afif Nurul Hidayati and Ingrid Nurtanio and Reza Fuad Rachmadi and I Ketut Eddy Purnama},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=_bttBQEAAAAJ&sortby=pubdate&citation_for_view=_bttBQEAAAAJ:TGkaJS32XoUC},

year  = {2020},

date = {2020-11-17},

journal = {2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},

pages = {332-337},

abstract = {A computerized skin disease classification system generally works on closed-set data, meaning images from unknown classes will still be classified as one of the known classes. In the Teledermatology system, skin disease classes are usually defined before the training process. However, in the real world application, it may receive images that belong to a new class or disease. To avoid misclassification, we have implemented the Extreme Value Theory of Weibull distribution function for out of distribution detection and incorporated the OpenMax layer to the deep networks for open-set recognition in skin disease classification. The system can classify seven classes of common skin disease in Indonesia with an accuracy of 71.64% using Inception v3 for closed-set data, while it achieved an accuracy of 83.33% for open-set recognition. The result indicates that the proposed method in this study has reached the purpose …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {An Evaluation Performance of Kernel on Support Vector Machine to Classify The Skin Tumors in Dermoscopy Image},

author = {Andhryn Celica Dewi Rahajeng and Mohammad Nuh and Nada Fitrieyatul Hikmah},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=id&user=5f0utZEAAAAJ&sortby=pubdate&citation_for_view=5f0utZEAAAAJ:ufrVoPGSRksC},

year  = {2020},

date = {2020-11-17},

journal = {2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},

pages = {76-81},

abstract = {Skin cancer has recently become one of the types of cancer that often appears and could become deadly. Mortality from skin cancer patient could be reduced if the detection and treatment is early and appropriate. Segmentation of skin lesions is usually on images that have classified melanocytic, whereas skin lesions that are classified as nonmelanocytic are equally important. Support vector machine (SVM) are used to differentiate skin lesions in dermoscopic images. The results of the classification, achieving best performance with accuracy of 85%, sensitivity of 86%, specification of 84%, and precision of 88% using radial basis function kernel. RBF kernel is giving best performance for this type of data. For validation model, this study using k-Fold Cross Validation. The optimal value are k=7 and k=8 with an accuracy of 83%. This study gives an idea to deal with disease which related to skin cancer using image …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Data Balancing Techniques Evaluation on Convolutional Neural Network to Classify The Diabetic Retinopathy of Fundus Image},

author = {Vina Alvionita and Mohammad Nuh and Nada Fitrieyatul Hikmah},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=id&user=5f0utZEAAAAJ&sortby=pubdate&citation_for_view=5f0utZEAAAAJ:WF5omc3nYNoC},

year  = {2020},

date = {2020-11-17},

journal = {2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},

pages = {354-359},

abstract = {Diabetic retinopathy (DR) is a common complication diabetic patients that causes impaired vision, and may even lead to blindness. Several studies on the DR diagnosis based on Computer-aided Diagnosis (CAD) had been conducted. The method used various feature extraction modules and a particular classifier. However, this method required a long step. In a different circumstance, deep neural networks had been successfully applied in various fields and showing good performance. For this reason, we proposed a classification system for DR based on Convolutional Neural Networks (CNN). In this study, we used retina images dataset from the Asia-Pacific Tele-Ophthalmology Society (APTOS) to train CNN under three different conditions. Sequentially is imbalanced, balanced by undersampling, and balanced by oversampling. The best results were obtained in the third condition, with an accuracy of 73.64 …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Arrhythmia Classification on Electrocardiogram Signal Using Convolution Neural Network Based on Frequency Spectrum},

author = {Arief Kurniawan and Firdaus Nanda Pradanggapasti and Reza Fuad Rachmadi and Eko Setijadi and Eko Mulyanto Yuniarno and Mochamad Yusuf and I Ketut Eddy Purnama},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=cWaY0cUAAAAJ&sortby=pubdate&citation_for_view=cWaY0cUAAAAJ:zCSUwVk65WsC},

year  = {2020},

date = {2020-11-17},

journal = {2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},

pages = {29-33},

abstract = {Heart disease is the leading cause of death in the world. To find out heart disease early, it can be detected by examining the presence or absence of arrhythmias. Arrhythmia is an abnormal heart beat rhythm, can beat too fast, too slow, or beat with irregular patterns, so that the arrhythmia has many types. To diagnose arrhythmias, one method that can be used is by analyzing ECG (Electrocardiogram) signals. Currently, doctors and medical personnel analyze ECG signals manually. Because the number of cardiologist paramedics is far less than the number of patients, patients need hardware or software to analyze the heart independently. With the development of technology in this era, there is a technology called Deep Learning. Deep Learning is a development of Machine Learning. In this paper, we proposed one method of Deep Learning, namely Convolutional Neural Network (CNN), is used to classify 5 types of …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Carotid Artery Segmentation on Ultrasound Image using Deep Learning based on Non-Local Means-based Speckle Filtering},

author = {Aji Sapta Pramulen and Eko Mulyanto Yuniarno and J Nugroho and I Made Gede Sunarya and I Ketut Eddy Purnama},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=cWaY0cUAAAAJ&sortby=pubdate&citation_for_view=cWaY0cUAAAAJ:jgBuDB5drN8C},

year  = {2020},

date = {2020-11-17},

journal = {2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},

pages = {360-365},

abstract = {Cardiovascular disease (CVD) causes significant deaths worldwide, of which 17.3 million deaths per year are due to CVD. The use of Ultrasound is necessary to see the abnormalities. The study will segment Carotid Artery segmentation on the Ultrasound image by using the U-Net-based architecture of non-local means-based speckle filtering (NLMBSF). The images will use NLMBSF to reduce speckles, and the data set will be divided into two parts, namely the dataset, which using NLMBSF and not NLMBSF. After that, doing training to create a U-net model, the training data model results will be searched with the best Accuracy. The obtained result of the study is an accuracy value of 97.74%, dice value is 87.22%, and a loss of 0.0107 on data that does not use NLMBSF. Still, it got different data results using NLMBSF, namely 97.6% accuracy, dice value is 84.06% and 0.0138 value loss.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Time Series Analysis for Understanding Local Policy Impact of COVID-19 Cases in East Java},

author = {Diana Purwitasari and Agus Budi Raharjo and Izzat Aulia Akbar and Faizal Johan Atletiko and Wiwik Anggraeni and Muhammad Ardian and Niko Azhari Hidayat and Hendro Suprayogi and Muhammad Amin},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=fn1DiJoAAAAJ&sortby=pubdate&citation_for_view=fn1DiJoAAAAJ:a0OBvERweLwC},

year  = {2020},

date = {2020-11-17},

journal = {2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},

pages = {52-57},

abstract = {Daily new cases of COVID-19 as a series of data points ordered in time is one representation of time series data. Our works expect to understand the data characteristics that is related to existing government policies. This paper aims to highlight the impact report and analyses of some COVID-19 policies in East Java province districts. The study is focused on the policy execution before and during the new normal situation of COVID-19 using time series data of new cases as possible and easily observable results. Aside from time series analysis, some visual analysis is performed as well. The experiments focused on some questions related to the policy effectiveness: finding patterns of daily new cases to instigate the need for other local policies and understanding any precedence on occurred cases for nearby districts. Although the second question is not confirmable, the first question verifies more tightened social …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Activation Functions Evaluation to Improve Performance of Convolutional Neural Network in Brain Disease Classification Based on Magnetic Resonance Images},

author = {Dewinda Julianensi Rumala and Eko Mulyanto Yuniarno and Reza Fuad Rachmadi and Supeno Mardi Susiki Nugroho and Hapsari Peni Agustin Tjahyaningtijas and Yudhi Adrianto and Anggraini Dwi Sensusiati and I Ketut Eddy Purnama},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=DYcR1o0AAAAJ&sortby=pubdate&citation_for_view=DYcR1o0AAAAJ:4Yq6kJLCcecC},

year  = {2020},

date = {2020-11-17},

journal = {2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},

pages = {402-407},

abstract = {Early detection and treatment of brain disease are essential. However, brain disease diagnosis used to be challenging, on the other hand imaging techniques such as MRI make it easier. For the past years, many researchers have used several methods of Machine Learning and Deep Learning to diagnose brain abnormalities without any human help. Convolutional Neural Network is the best method to extract features of images automatically. In this study, a Deep Learning model of Convolutional Neural Network algorithm is applied to classify brain MR Images into normal and abnormal classes. The constructed network architecture was evaluated based on several activation functions and numbers of epoch. The experiment results achieved a significant performance with the best accuracy of 99.12% and dice score of 98.17% using ELU activation function at epoch 50. This result indicates that the proposed method …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Synchronization of Vertical Electrooculography Sensor (EOGV) Data on Eye Image Data as Blink Data Validator},

author = {Ahmad Zaini and Yoyon Kusnendar Suprapto and Eko Mulyanto Yuniarno},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=DYcR1o0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=DYcR1o0AAAAJ:2vr6o8x5NLkC},

year  = {2020},

date = {2020-11-17},

journal = {2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},

pages = {180-184},

abstract = {This paper presents research on synchronizing the data of the Electrooculography (EOG) sensor with eye image data when someone blinks. In this study, we carry out the validation of the vertical blink data synchronization from the EOGV sensor to the blinking eye image. The research uses two modalities to get data validity, whether someone blinks or not. There is ambiguity in the two modalities that have different data rates, and both might also have different false data that should not be recognized as signals or blinking eyelid data the same. Data modality matching and data interpolation are implemented to get complete data following the data duration and to get the pair of both modalities. We use linear regression to obtain a formulation of the synchronous relationship between the EOG blinking data sequence and the number of blinking eyelid image frame data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Identification of Epilepsy Phase Based on Time Domain Feature Using ECG Signals},

author = {Wardah R Islamiyah and Diah P Wulandari and Nadhira N Sarasati and Yoyon K Suprapto and Santi W Purnami and Anda I Juniani},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=XI-Pg2QAAAAJ&sortby=pubdate&citation_for_view=XI-Pg2QAAAAJ:qxL8FJ1GzNcC},

year  = {2020},

date = {2020-11-17},

journal = {2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},

pages = {1-6},

abstract = {Epilepsy is a neural disease caused by brain signal abnormalities. There are three phases in epilepsy, the pre-ictal, ictal, and post-ictal phase. To distinguish those phases, usually EEG signal is used. However, there is a study mentioning the connection between epilepsy and heart signals, so there is a probability to distinguish those phases using ECG. This study is made for distinguish the three phases in epilepsy and the normal condition of epilepsy patient using K Nearest Neighbors (KNN) algorithm. Dataset used in this study was from PhysioNet, obtained from long-term EEG and ECG record of epileptic patient without history of cardiac disease. With the ability to do the identification of epilepsy phase, it is expected to help doctors and medical staffs to differ epileptic ECG signals for every different phases in epilepsy, and to prove the hypothesis whether the three phases in epilepsy can be distinguished from the …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Optimal EMG Feature Selection for Classification of Normal and Post-Stroke from Hand-Reaching Movement},

author = {I Ketut Adi Purnawan and Adhi Dharma Wibawa and Rizal Ardhi Rahmadani and Mauridhi Hery Purnomo},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=_bttBQEAAAAJ&sortby=pubdate&citation_for_view=_bttBQEAAAAJ:vnF2_uLGgtgC},

year  = {2020},

date = {2020-10-22},

urldate = {2020-10-22},

journal = {2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)},

pages = {1-6},

abstract = {Electromyography (EMG) signals have been used extensively in research related to muscle functioning rehabilitation of post-stroke patients. EMG signal is non-linear, non-stationary, and similar in domains of time and frequency hence needs an automated system to classify the signal acquired from normal and post-stroke patient. An accurate EMG signal classification system relies on the number of features extracted from datasets. More features involved mean a better classification performance but causing a heavier computational needed. In this paper, we selected the most optimal EMG signal feature to minimize the feature used. The criteria of selected optimal feature is should have the highest accuracy compared to the other features. In order to weigh the accuracy, we employed the tree-based classifier. The optimal features were selected among 13 time-domain (TD) of normal and post-stroke patient EMG hand …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Automatic Tooth and Background Segmentation in Dental X-ray Using U-Net Convolution Network},

author = {Arna Fariza and Agus Zainal Arifin and Eha Renwi Astuti},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=-5mgvWsAAAAJ&sortby=pubdate&citation_for_view=-5mgvWsAAAAJ:w7CBUyPWg-0C},

year  = {2020},

date = {2020-10-21},

journal = {2020 6th International Conference on Science in Information Technology (ICSITech)},

pages = {144-149},

abstract = {Tooth and background segmentation in dental X-ray is used to produce an area of a tooth by removing areas of tissue and other neighboring teeth. This presents challenges due to a large number of superimposed (overlapping) images of teeth between the adjacent teeth and the difficulty of determining the area of the tooth with other tissues automatically. This study proposes a new approach for the automatic segmentation of dental X-ray images using the U-Net convolution network. The stages used in the training process consist of data augmentation, pre-processing with Contrast Limited Adequate Histogram Equalization (CLAHE) and gamma adjustment, and training with the U-Net architecture. While the testing process consists of pre-processing, prediction, and removing small areas in the background. The experimental results show the average accuracy of the proposed U-Net convolutional network …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A novel method for determining the Femoral-Tibial Angle of Knee Osteoarthritis on X-ray radiographs: data from the Osteoarthritis Initiative},

author = {Rima Tri Wahyuningrum and I Ketut Eddy Purnama and Gijsbertus Jacob Verkerke and Peter MA van Ooijen and Mauridhi Hery Purnomo},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=_bttBQEAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=_bttBQEAAAAJ:yTLRzDEmwhEC},

year  = {2020},

date = {2020-08-01},

journal = {Heliyon},

volume = {6},

number = {8},

pages = {e04433},

abstract = {Femoral-tibial alignment is a prominent risk factor for Knee Osteoarthritis (KOA) incidence and progression. One way of assessing alignment is by determining the Femoral-Tibial Angle (FTA). Several studies have investigated FTA determination; however, methods of assessment of FTA still present challenges. This paper introduces a new method for semi-automatic measurement of FTA as part of KOA research. Our novel approach combines preprocessing of X-ray images and the use of Active Shape Model (ASM) as the femoral and tibial segmentation method, followed by a thinning process. The result of the thinning process is used to predict FTA automatically by measuring the angle between the intersection of the two vectors of branching points on the femoral and tibial areas. The proposed method is trained on 10 x-ray images and tested on 50 different x-ray images of the Osteoarthritis Initiative (OAI) dataset …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Diagnostik Kelelahan dengan Sinyal Electrocardiogram (ECG) untuk Kontrol Kecepatan Treadmill Berbasis Fuzzy Logic},

author = {Suci Intan Prativy and Siti Halimah Baki and Nada Fitrieyatul Hikmah},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=id&user=5f0utZEAAAAJ&sortby=pubdate&citation_for_view=5f0utZEAAAAJ:YsMSGLbcyi4C},

year  = {2020},

date = {2020-07-29},

journal = {Jurnal Teknik ITS},

volume = {9},

number = {1},

pages = {F37-F41},

abstract = {Electrocardiography (ECG) merupakan alat yang digunakan untuk merekam data aktivitas elektris otot jantung antara lain Heart rate Variability dan durasi sinyal QRS. ECG merupakan indikator yang paling umum digunakan dan dapat menjawab masalah fisiologis manusia dalam keadaan statis dan dinamis, contohnya olahraga. Saat berolahraga ada fase-fase yang harus dilakukan yaitu fase pemanasan, fase proses olahraga itu sendiri, serta fase pendinginan. Namun, sering kali fase-fase ini dilewatkan. Berdasarkan anjuran dari produsen treadmill KETTLER RUN 11 serta dengan bantuan Karvonen Formula dan persen Cardivascular Load (% CVL), diciptakan sistem untuk menentukan beban latihan saat berolahraga lari berdasarkan kebutuhan tiap-tiap individu. Karvonen Formula merupakan formula yang dipergunakan untuk menghitung nilai heart rate minimal dan maksimal seseorang, dimana dalam perhitungannya dibutuhkan informasi usia, jenis kelamin, nilai resting heart rate, serta level intensitas olahraga yang diinginkan dari individu yang akan dihitung nilai heart rate minimal dan maksimalnya. Parameter% CVL digunakan untuk mengetahui kapan seseorang harus berhenti berolahraga berdasarkan heart rate maksimal, heart rate minimum dan heart rate saat berolahraga. Pada saat sistem dijalankan, tiga buah elektroda yang terpasang pada dada disambungkan ke ECG untuk kegiatan monitoring. Data yang diambil menunjukan kenaikan heart rate stabil ketika menjalankan fase pemanasan dan fase proses. Sedangkan pada fase pendinginan mengalami penurunan yang juga stabil. Berdasarkan hasil penelitian ini dengan …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Investigating EEG Pattern During Designed-Hand Movement Tasks in Stroke Patients},

author = {Made Dwi Novitasari and Adhi Dharma Wibawa and Mauridhi Hery Purnomo and Wardah Rahmatul Islamiyah and Ali Fatoni},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=_bttBQEAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=_bttBQEAAAAJ:TesyEGJKHF4C},

year  = {2020},

date = {2020-07-22},

journal = {2020 International Seminar on Intelligent Technology and Its Applications (ISITIA)},

pages = {141-147},

abstract = {Stroke is a catastrophic disease with the second-highest mortality rate in the world. It is also the leading cause of disability in many countries. A stroke rehabilitation program is crucial for the recovery process of post-stroke patients. It must be supported by measurable monitoring. Rehabilitation monitoring is currently still carried out through visual and manual observation, so the measurement results have not been well presented and subjective. Monitoring using EEG can provide solutions to these needs. During the monitoring process, significant parameters of EEG need to be explored. This study aims to find the most stable parameters that could be applied as a basis for measuring progress in stroke rehabilitation monitoring. The parameters are searched by calculating the difference between the value of the features of healthy hand movements with affected hand movements in the same individual stroke patients …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Epileptic EEG Signal Classification Using Convolutional Neural Networks Based on Optimum Window Length and FFT's Length},

author = {Irwan Budi Santoso and I Ketut Eddy Purnama},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=cWaY0cUAAAAJ&sortby=pubdate&citation_for_view=cWaY0cUAAAAJ:CaZNVDsoPx4C},

year  = {2020},

date = {2020-07-17},

journal = {Proceedings of the 8th International Conference on Computer and Communications Management},

pages = {87-91},

abstract = {This paper presents the method for the epilepsy classification based on electroencephalogram (EEG) signals. This method uses the spectrogram image of EEG signals and convolutional neural networks (CNN). The spectrogram image is obtained by mapping the spectrogram value of the results of the short-time Fourier Transform (STFT) to the RGB color map. The best spectrogram for CNN is determined based on the length of fast Fourier transform (FFT) and window length in the windowing technique. The proposed method is evaluated with the epileptic EEG signal datasets. The experimental results show CNN works optimally with spectrogram images from STFT on the window length of 128 and the length of FFT of 128. In these conditions, the performance of CNN in the classification of epilepsy is better than others and able to compete with existing methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Automatic Segmentation of Dental Cone Beam Computed Tomography Image Based on Level Set Method Using Morphology Operators and Polynomial Fitting},

author = {Fahmi Syuhada Syuhada and Agus Zainal Arifin},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=-5mgvWsAAAAJ&sortby=pubdate&citation_for_view=-5mgvWsAAAAJ:Ul_CLA4dPeMC},

year  = {2020},

date = {2020-06-30},

journal = {Journal of Computer Science and Informatics Engineering (J-Cosine)},

volume = {4},

number = {1},

pages = {45-52},

abstract = {Automatic Segmentation of dental cone beam computed tomography (CBCT) images is challenging due to the intensity of the teeth that have low level intensity. In this paper we proposes a new method for automatic teeth segmentation in slices of CBCT images based on level let method using morphology operators and polynomial fitting. Morphology operators are used to construct the Region of Interest (ROI) area of dental objects in the image slice. ROI is used to focus the analysis process on areas of dental objects which generally have a polynomial pattern distribution. Polynomial fitting is obtained to estimation arc of teeth structure in CBCT images. Level Set is implemented to evolve the ROI to obtain the contours of dental objects. Comparison between proposed method result and the ground truth images shows that the method gives best average accuracy, sensitivity, and specificity value of 99.02%, 95.32%, 99.09%, respectively. This value that the proposed method is promising for accurate segmentation of the entire tooth form on CBCT images.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Line Operator as Preprocessing Method for CNN-based Osteoporosis Detection in Dental Panoramic Radiograph},

author = {Ilham Gurat Adillion and Yoshiteru Ishida and Agus Zainal Arifin},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=-5mgvWsAAAAJ&sortby=pubdate&citation_for_view=-5mgvWsAAAAJ:MNNNGtAgD4EC},

year  = {2020},

date = {2020-06-05},

journal = {Proceedings of the 2020 The 6th International Conference on Frontiers of Educational Technologies},

pages = {103-107},

abstract = {Osteoporosis is a disease that can be detected via the trabecular bone pattern in Dental Panoramic Radiograph (DPR). Trabecular bone pattern is difficult to see by the naked eye due to the low contrast and low resolution of DPR. This can affect the performance of osteoporosis disease detection using Convolutional Neural Network (CNN). In this paper we propose the use of Line Operator (LO) on DPR images as a preprocessing method to enhance trabecular bone pattern for CNN-based osteoporosis detection. LO is a method that can enhance line-like structures in medical images such as retina and DPR dataset. To study the effect of LO on CNN-based osteoporosis detection, the performance of non-preprocessed images, LO-preprocessed images and LO+ histogram equalization pre-processed images was compared. Results showed that LO-preprocessed images give best osteoporosis detection accuracy of 0 …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Automatic segmentation on glioblastoma brain tumor magnetic resonance imaging using modified u-net},

author = {Hapsari Peni Agustin Tjahyaningtijas and Andi Kurniawan Nugroho and Cucun Very Angkoso and I Ketut Edy Purnama and Mauridhi Hery Purnomo},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=cWaY0cUAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=cWaY0cUAAAAJ:4X0JR2_MtJMC},

year  = {2020},

date = {2020-06-02},

journal = {EMITTER International Journal of Engineering Technology},

volume = {8},

number = {1},

pages = {161-177},

abstract = {Glioblastoma is listed as a malignant brain tumor. Due to its heterogeneous composition in one area of the tumor, the area of tumor is difficult to segment from healthy tissue. On the other side, the segmentation of brain tumor MRI imaging is also erroneous and takes time because of the large MRI image data. An automated segmentation approach based on fully convolutional architecture was developed to overcome the problem. One of fully convolutional network that used is U-Net framework. U-Net architecture is evaluated base on the number of epochs and drop-out values to achieve the most suitable architecture for the automatic segmentation of glioblastoma brain tumors. Through experimental findings, the most fitting architectural model is mU-Net architecture with an epoch number of 90 and a drop out layer value of 0.5. The results of the segmentation performance are shown by a dice value of 0.909 which is greater than that of the previous research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Deep learning based facial expressions recognition system for assisting visually impaired persons},

author = {Hendra Kusuma and Muhammad Attamimi and Hasby Fahrudin},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wsdqOiwAAAAJ&sortby=pubdate&citation_for_view=wsdqOiwAAAAJ:dhFuZR0502QC},

year  = {2020},

date = {2020-06-01},

journal = {Bulletin of Electrical Engineering and Informatics},

volume = {9},

number = {3},

pages = {1208-1219},

abstract = {In general, a good interaction including communication can be achieved when verbal and non-verbal information such as body movements, gestures, facial expressions, can be processed in two directions between the speaker and listener. Especially the facial expression is one of the indicators of the inner state of the speaker and/or the listener during the communication. Therefore, recognizing the facial expressions is necessary and becomes the important ability in communication. Such ability will be a challenge for the visually impaired persons. This fact motivated us to develop a facial recognition system. Our system is based on deep learning algorithm. We implemented the proposed system on a wearable device which enables the visually impaired persons to recognize facial expressions during the communication. We have conducted several experiments involving the visually impaired persons to validate our proposed system and the promising results were achieved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {3D Reconstruction of Carotid Artery in B-mode Ultrasound Image using Modified Template Matching Based on Ellipse Feature},

author = {I Made Gede Sunarya and Eko Mulyanto Yuniarno and Tri Arief Sardjono and Ismoyo Sunu and Peter MA van Ooijen and I Ketut Eddy Purnama},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=cb466OAAAAAJ&citation_for_view=cb466OAAAAAJ:4TOpqqG69KYC},

year  = {2020},

date = {2020-05-03},

journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization (Q3)},

volume = {8},

number = {3},

pages = {301-312},

abstract = {Detection of vascular areas using B-mode ultrasound is required for automated applications such as registration and navigation in medical operations. The limitations of Ultrasound imaging are the requirement of sonographer’s skills, expertise, and also knowledge when making data acquisition. It also influences the quality of the images. Carotid atherosclerosis can be treated with carotid artery stenting. The starting point of needle injection cannot be determined with certainty. The position of the arteries is in the body, therefore, determining the starting point of needle injection is done by estimation only and cannot be certainly determined. To be able to determine it, the first step needed is to determine the location of the carotid artery. We propose a 3D reconstruction of carotid artery using a modified template matching based on ellipse feature to determine it. It is processed using the procedure of data acquisition …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Least Square Support Vector Machine for Interictal Detection Based on EEG of Epilepsy Patients at Airlangga University Hospital Surabaya-Indonesia},

author = {Santi Wulan Purnami and Triajeng Nuraisyah and Wardah Rahmatul Islamiyah and Diah P Wulandari and Anda I Juniani},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=XI-Pg2QAAAAJ&sortby=pubdate&citation_for_view=XI-Pg2QAAAAJ:Wp0gIr-vW9MC},

year  = {2020},

date = {2020-04-18},

journal = {The International Conference on Artificial Intelligence and Applied Mathematics in Engineering},

pages = {198-210},

abstract = {Epilepsy is a chronic disease characterized by recurrent seizures. Epileptic seizures occur due to central nervous system (neurological) disorders. Around 50 million people worldwide suffer from epilepsy. The diagnosis of epilepsy can be done through an electroencephalogram (EEG). There are two important periods to consider in EEG recording, the interictal period (clinically no seizures) and ictal (clinically seizures). Meanwhile, visual inspection of EEG signals to detect interictal and ictal periods often involves an element of subjectivity and it requires experience. So that automatic detection of interictal periods with classification method is badly needed. In this study, Least Square Support Vector Machine (LS SVM) method for classification of interictal and ictal was used. Data preprocessing process was carried out using Discrete Wavelet Transform (DWT). The result showed that the classification using LS …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Automatic Ventral Intermediate Nucleus Localization Based on Anterior Commissure and Posterior Commissure},

author = {Riyanarto Sarno and Kelly Rossa Sungkono and Mohammad Ardika Rifqi and Achmad Fahmi and Agus Turchan and Abdul Hafid Bajamal},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=id&user=7qhHXooAAAAJ&citation_for_view=7qhHXooAAAAJ:Y0pCki6q_DkC},

year  = {2020},

date = {2020-04-09},

journal = {Preprint from Research Square},

abstract = {The ventral intermediate nucleus (Vim), as the motor thalamic nuclei section, is a generally used target in brain lesion surgery or stimulation for decreasing tremors in people with Parkinson’s disease. Determining the exact position of Vim is challenging because Vim cannot be visualized clearly in commonly used magnetic resonance imaging (MR). Indirect methods, ie, Coordinate-based targeting and Guiot’s, utilize anterior commissure and posterior commissure to detect the location of Vim. In practice, neurosurgeons manually implement these methods in existing neurosurgical planning software, so the accuracy of the targeting depends on their memory and foresight. Afterward, Coordinate-based targeting and Guiot’s locate Vim based on anterior commissure (AC) and posterior commissure (PC), so neurosurgeons must correctly determine AC and PC. This paper proposes automatic indirect methods and measures the accuracy of indirect methods in MRIs with correct and incorrect orientations of AC-PC planes. An objective of analyzing indirect methods in MRIs with incorrect orientations of AC-PC planes is to discover the most resilient indirect method with inaccuracy AC-PC planes. To develop automatic indirect methods, the first step is redefining the plane passing through three defined points, ie, AC, PC, and midline reference, by a quaternion. Secondly, Coordinate-based targeting and Guiot’s are implemented to determine the Vim targeting location automatically. This paper converts the rules of those methods in voxels because the rules use millimeters while the three-dimensional MRIs use voxels. The experiment shows that Vim locations …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {SEPARATION OF OVERLAPPING OBJECT SEGMENTATION USING LEVEL SET WITH AUTOMATIC INITALIZATION ON DENTAL PANORAMIC RADIOGRAPH},

author = {Safri Adam and Agus Zainal Arifin},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=-5mgvWsAAAAJ&sortby=pubdate&citation_for_view=-5mgvWsAAAAJ:u_mOZUIutIEC},

year  = {2020},

date = {2020-03-14},

journal = {Jurnal Ilmu Komputer dan Informasi},

volume = {13},

number = {1},

pages = {25-34},

abstract = {Untuk melakukan ekstraksi fitur pada objek gigi, maka diperlukan segmentasi terlebih dahulu. Proses segmentasi memisahkan antara bagian gigi (objek) dengan bagian selain gigi (background). proses untuk melakukan segmentasi gigi secara individu telah dilakukan oleh penelitian terkini dan memperoleh hasil yang bagus. Namun ketika dihadapkan dengan gigi yang overlap (tumpang tindih), hal ini menjadi tantangan. Segmentasi pada gigi yang overlap menggunakan algoritma terkini masih menghasilkan satu objek. Hal ini disebabkan karena dua gigi yang saling tumpuk seolah-olah menjadi satu. Untuk memisahkan dua gigi yang overlap, maka perlu mengekstrak objek overlap terlebih dahulu. Metode level set banyak digunakan untuk melakukan segmentasi objek, namun memiliki kelemahan yaitu perlu didefinisikan inisial awal secara manual oleh pengguna. Dalam penelitian ini diusulkan strategi inisialisasi otomatis pada metode level set untuk melakukan segmentasi gigi yang overlap menggunakan Hierarchical Cluster Analysis pada citra panorama gigi. Strategi yang diusulkan mampu melakukan inisialisasi objek overlap dengan akurasi sebesar 73%. Evaluasi untuk mengukur kualitas hasil segmentasi gigi yang overlap menggunakan missclassification error (ME) dan relative foreground area error (RAE). ME dan RAE dihitung berdasarkan hasil rata-rata dari segmentasi gigi individu dan memperoleh ME sebesar 16, 41% dan RAE 52, 14%. strategi yang diusulkan diharapkan dapat membantu melakukan segmentasi terhadap citra gigi yang overlap untuk penilaian estimasi usia manusia melalui citra gigi dalam bidang odontologi …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Time-based α+ miner for modelling business processes using temporal pattern},

author = {Yutika Amelia Effendi and Riyanarto Sarno},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=QOMOtp0AAAAJ&cstart=200&pagesize=100&citation_for_view=QOMOtp0AAAAJ:JQPmwQThujIC},

year  = {2020},

date = {2020-02-01},

journal = {Telkomnika (Q2)},

volume = {18},

number = {1},

pages = {114-123},

abstract = {Business processes are implemented in an organization. When a business process is run, it generates event log. One type of event log is double timestamp event log. Double timestamp has the start and complete time of each activity executed in the business process and has a close relationship with temporal pattern. In this paper, seven types of temporal pattern between activities were presented as extended version of relations used in the double timestamp event log. Since the event log was not always executed in sequential way, therefore using temporal pattern, event log was divided into several small groups to mine the business process both sequential and parallel. Both temporal pattern and Time-based α+ Miner algorithm were used to mine process model, determined sequential and parallel relations and then evaluated the process model using fitness value. This paper was focused on the advantages of temporal pattern implemented in Time-based α+ Miner algorithm to mine business process. The results also clearly stated that the proposed method could present better result rather than that of original α+ Miner algorithm.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Parallel process discovery using a new Time-Based Alpha++ Miner},

author = {Yutika Amelia Effendi and Riyanarto Sarno},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=id&user=Z74eq5kAAAAJ&citation_for_view=Z74eq5kAAAAJ:7T2F9Uy0os0C},

year  = {2020},

date = {2020-01-20},

urldate = {2020-01-20},

journal = {IIUM Engineering Journal (Q4)},

volume = {21},

number = {1},

pages = {126-141},

abstract = {A lot of services in business processes lead information systems to build huge amounts of event logs that are difficult to observe. The event log will be analysed using a process discovery technique to mine the process model by implementing some well-known algorithms such as deterministic algorithms and heuristic algorithms. All of the algorithms have their own benefits and limitations in analysing and discovering the event log into process models. This research proposed a new Time-based Alpha++ Miner with an improvement of the Alpha++ Miner and Modified Time-based Alpha Miner algorithm. The proposed miner is able to consider noise traces, loop, and non-free choice when modelling a process model where both of original algorithms cannot override those issues. A new Time-based Alpha++ Miner utilizing Time Interval Pattern can mine the process model using new rules defined by the time interval pattern using a double-time stamp event log and define sequence and parallel (AND, OR, and XOR) relation. The original miners are only able to discover sequence and parallel (AND and XOR) relation. To know the differences between the original Alpha++ Miner and the new one including the process model and its relations, the evaluation using fitness and precision was done in this research. The results presented that the process model obtained by a new Time-based Alpha++ Miner was better than that of the original Alpha++ Miner algorithm in terms of parallel OR, handling noise, fitness value, and precision value.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Automatic image slice marking propagation on segmentation of dental CBCT},

author = {Agus Zainal Arifin and Evan Tanuwijaya and Baskoro Nugroho and Arif Mudi Priyatno and Rarasmaya Indraswari and Eha Renwi Astuti and Dini Adni Navastara},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=RHRa-DoAAAAJ&sortby=pubdate&citation_for_view=RHRa-DoAAAAJ:4JMBOYKVnBMC},

year  = {2019},

date = {2019-12-01},

journal = {TELKOMNIKA},

volume = {17},

pages = {3218-3225},

abstract = {Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly used to help doctors provide more detailed information for further examination. Teeth segmentation on CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have related information, the semi-automatic image segmentation method, that needs manual marking from the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will be propagated as the marker for the segmentation of the next slices. The experimental results show that the proposed method is successful in segmenting the teeth on CBCT images with the value of Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Disease classification based on dermoscopic skin images using convolutional neural network in teledermatology system},

author = {I Ketut Eddy Purnama and Arta Kusuma Hernanda and Anak Agung Putri Ratna and Ingrid Nurtanio and Afif Nurul Hidayati and Mauridhi Hery Purnomo and Supeno Mardi Susiki Nugroho and Reza Fuad Rachmadi},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=25v5QvgAAAAJ&sortby=pubdate&citation_for_view=25v5QvgAAAAJ:fPk4N6BV_jEC},

year  = {2019},

date = {2019-11-19},

journal = {2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},

pages = {1-5},

abstract = {We have proposed a system of classification and detection of skin diseases that can be applied to Teledermatology. This system will classify skin diseases on dermoscopic images using the Deep Learning algorithm, Convolutional Neural Network (CNN). Dermoscopic image data in this study from MNIST HAM10000 dataset which amounts to 10,015 images and published by International Skin Image Collaboration (ISIC). The dataset is divided into seven class of skin diseases which fall into the category of skin cancer. The image classification process will use two pre-trained CNN models, MobileNet v1 and Inception V3. The model results from the learning process will be applied to a web-classifier. The comparison of predictive accuracy shows that the web-classifier using the CNN Inception V3 model has an accuracy value of 72% while the web-classifier that uses the MobileNet v1 model has an accuracy value of …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Improving the Accuracy of EEG Emotion Recognition by Combining Valence Lateralization and Ensemble Learning with Tuning Parameters},

author = {Evi Septiana Pane and Adhi Dharma Wibawa and Mauridhi Hery Purnomo},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=id&user=6WHefP4AAAAJ&citation_for_view=6WHefP4AAAAJ:LkGwnXOMwfcC},

year  = {2019},

date = {2019-11-01},

journal = {Cognitive Processing (Q2)},

volume = {20},

number = {4},

pages = {405-417},

abstract = {For emotion recognition using EEG signals, the challenge is improving accuracy. This study proposes strategies that concentrate on incorporating emotion lateralization and ensemble learning approach to enhance the accuracy of EEG-based emotion recognition. In this paper, we obtained EEG signals from an EEG-based public emotion dataset with four classes (i.e. happy, sad, angry and relaxed). The EEG signal is acquired from pair asymmetry channels from left and right hemispheres. EEG features were extracted using a hybrid features extraction from three domains, namely time, frequency and wavelet. To demonstrate the lateralization, we performed a set of four experimental scenarios, i.e. without lateralization, right-/left-dominance lateralization, valence lateralization and others lateralization. For emotion classification, we use random forest (RF), which is known as the best classifier in ensemble learning …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Ina-BWR: Indonesian bigram word rule for multi-label student complaints},

author = {Tora Fahrudin and Joko Lianto Buliali and Chastine Fatichah},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=A4Fg7LoAAAAJ&citation_for_view=A4Fg7LoAAAAJ:kNdYIx-mwKoC},

year  = {2019},

date = {2019-11-01},

journal = {Egyptian Informatics Journal (Q2)},

volume = {20},

pages = {151-161},

abstract = {Handling multi-label student complaints is one of interesting research topics. One of techniques used for handling multi-label student complaints is Bag of Word (BoW) method. In this research bigram word rule and preprocess are proposed to increase the accuracy of multi-label classification results. To show the effectiveness of the proposed method, data from Telkom University student data and additional relevant data by using hashtag are used as testing data. We develop Indonesian Bigram Word Rule for Multi-label Student Complaints (Ina-BWR) to identify multi-label student problems based on Bigram Word Rule. Ina-BWR consists of three processes such as preprocessing informal text, identifying complaint and object from text. Additional preprocessing techniques are conducted to formalize the text such as parsing a hashtag, correcting affixes word, correcting a conjunction word, parsing suffix people pronoun …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}