Publications

@article{nokey,

title = {A Feature-Based Fragile Watermarking of Color Image for Secure E-Government Restoration},

author = {Lusia Rakhmawati and Wirawan Wirawan and Suwadi Suwadi and Titiek Suryani and E Endroyono},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=emnm-rAAAAAJ&sortby=pubdate&citation_for_view=emnm-rAAAAAJ:YOwf2qJgpHMC},

year  = {2019},

date = {2019-11-01},

journal = {Proceeding of the Electrical Engineering Computer Science and Informatics},

volume = {5},

number = {1},

pages = {776-780},

abstract = {this research developed a method using fragile watermarking technique for color images to achieve secure e-government tamper detection with recovery capability. Before performing the watermark insertion process, the RGB image is converted first into YCbCr image. The watermark component is selected from the image feature that approximates the original image, in which the chrominance value features as a watermark component. For a better detection process, 3-tuple watermark, check bits, parity bits, and recovery bits are selected. The average block in each 2 x 2 pixels is selected as 8 restoration bits of each component, the embedding process work on the pixels by modifying the pixels value of three Least Significant Bit (LSB). The secret key for secure tamper detection and recovery, transmitted along with the watermarked image, and the algorithm mixture is used to extract information at the receiving end. The results show remarkably effective to restore tampered image.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {New concept of universal coding using one step reversible low contrast mapping (1RLCM)},

author = {H Mesra and H Tjandrasa and C Fatichah},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=LrW81jIAAAAJ&citation_for_view=LrW81jIAAAAJ:2osOgNQ5qMEC},

year  = {2019},

date = {2019-10-01},

urldate = {2019-10-01},

journal = {Journal of Physics: Conference},

volume = {1341},

pages = {042008},

abstract = {Universal coding was developed for compressing data that the probability distribution of a symbol is unknown. Universal coding methods encode a symbol using some bits as code based on the coding algorithm. This research introduces a new concept of universal coding by encoding two symbols using Reversible Low Contrast Mapping (RLCM) transform. The proposed method transforms a pair non negative integer to a non-negative integer and a binary number. This research also introduces a compression scheme namely Average Encoding (AE) to compress a sequence of data using the proposed coding method. This method generally yields a higher compression ratio than the Elias Delta, Elias Gamma, and Fibonacci coding when it is tested using the results of differential encoding of some testing images.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Spatial Condition in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs},

author = {Wawan Gunawan and Agus Zainal Arifin and Undang Rosidin and Nina Kadaritna},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=-5mgvWsAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=-5mgvWsAAAAJ:C-Rn0OCouf8C},

year  = {2019},

date = {2019-10-01},

journal = {IJCCS (Indonesian Journal of Computing and Cybernetics Systems)},

volume = {13},

number = {4},

pages = {369-378},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images},

author = {Umi Salamah and Riyanarto Sarno and Agus Zainal Arifin and Anto Satriyo Nugroho and Ismail Ekoprayitno Rozi and Puji Budi Setia Asih},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=fr&user=vAtvBJYAAAAJ&citation_for_view=vAtvBJYAAAAJ:ULOm3_A8WrAC},

year  = {2019},

date = {2019-09-02},

urldate = {2019-09-02},

journal = {International Journal on Advanced Science, Engineering and Information Technology (Q3)},

volume = {9},

number = { 4},

pages = {1450-1459},

abstract = {Parasite Detection on thick blood smears is a critical step in Malaria diagnosis. Most of the thick blood smear microscopic images have the following characteristics: high noise, a similar intensity between background and foreground, and the presence of artifacts. This situation makes the detection process becomes complicated. In this paper, we proposed a robust segmentation technique for malaria parasite detection of microscopic images obtained from various endemic places in Indonesia. The proposed method includes pre-processing, blood component segmentation using intensity slicing and morphological operation, blood component classification utilising rule based on properties of parasite candidates, and parasite candidate formation. The performance was evaluated on 30 thick blood smear microscopic images. The experimental results showed that the proposed segmentation method was robust to the different condition of image and histogram. It reduced the misclassification error and relative foreground error by 2.6% and 45.5%, respectively. Properties addition to blood component classification increased the system precision. Average of precision, recall, and F-measure of the proposed method were all 86%. It is proven that the proposed method is appropriate to be used for malaria parasites detection.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Automatic Segmentation of Mandibular Cortical Bone on Cone-beam CT Images Based on Histogram Thresholding and Polynomial Fitting},

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

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=TEBpO74AAAAJ&citation_for_view=TEBpO74AAAAJ:eQOLeE2rZwMC},

year  = {2019},

date = {2019-09-01},

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

volume = { 12},

number = {4},

pages = {130-141},

abstract = {Automatic segmentation of mandibular cortical bone is challenging due to the appearance of teeth that have similar intensity with the bone tissue and the variety of bone intensity. In this paper we propose a new method for automatic segmentation of mandibular cortical bone on cone-beam computed tomography (CBCT) images. The bone tissue is segmented by using Gaussian mixture model for histogram thresholding. The mandibular inferior cortical bone is obtained by incorporating several polynomial models to fit the structure of cortical bone on coronal slices. The buccal and lingual cortical plate is separated by using histogram thresholding for teeth elimination and polynomial fitting for shape extraction. After performing 3D reconstruction, the volumetric cortical bone is obtained. The proposed method gives average accuracy, sensitivity, and specificity value of 96.82%, 85.96%, 97.60%, respectively. This shows that the proposed method is promising for automatic and accurate segmentation of mandibular cortical bone on CBCT images.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Deteksi Region of Interest Tulang pada Citra B-mode secara Otomatis Menggunakan Region Proposal Networks},

author = {Tita Karlita and I Made Gede Sunarya and Joko Priambodo and Rika Rokhana 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&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=_bttBQEAAAAJ:AYInfyleIOsC},

year  = {2019},

date = {2019-09-01},

journal = {Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)},

volume = {8},

number = {2},

pages = {2301-4156},

abstract = {Bone imaging using ultrasound is a safe technique since it does not involve ionizing radiation and non-invasive. However, bone detection and localization to find its region of interest (RoI) is a challenging task because b-mode ultrasound images are characterized by high level of noise and reverberation artifacts. The image quality is user-dependent and the boundary between tissues is blurry, which makes it challenging to interpret images. In this paper, the deep learning approach using Region Proposal Networks was implemented to detect bone’s RoI in bmode images. The Faster Region-based Convolutional Neural Network model was fine-tuned to detect and determine the bone location in b-mode images automatically. To evaluate the results, in-vivo experiments were carried out using human arm specimens. A total of 1,066 b-mode bone images from six different subjects were used in the training phase and testing phase. The proposed method was successful in determining the bone RoI with the value of the mAP, the accuracy of detection, and the accuracy of localization of 0.87, 98.33%, and 95.99% respectively.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Indonesian Sign Language Recognition using Kinect and Dynamic Time Warping},

author = {W Nurul Khotimah and Tiara Anggita and Nanik Suciati},

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

year  = {2019},

date = {2019-07-01},

journal = {Indonesian Journal of Electrical Engineering and Computer Science (Q3)},

volume = {15},

pages = {495-503},

abstract = {Sign Language Recognition System (SLRS) is a system to recognise sign language and then translate them into text. This system can be developed by using a sensor-based technique. Some studies have implemented various feature extraction and classification methods to recognise sign language in the different country. However, their systems were user dependent (the accuracy was high when the trained and the tested user were the same people, but it was getting worse when the tested user was different to the trained user). Therefore, in this study, we proposed a feature extraction method which is invariant to a user. We used the distance between two users’ skeleton instead of using the users’ skeleton positions because the skeleton distance is independent to the user posture. Finally, forty-five features were extracted in this proposed method. Further, we classified the features by using a classification method that is suitable with sign language gestures characteristic (time-dependent sequence data). The classification method is Dynamic Time Wrapping. For the experiment, we used twenty Indonesian sign languages from different semantic groups (greetings, questions, pronouns, places, family and others) and different gesture characteristic (static gesture and dynamic gesture). Then the system was tested by a different user with the user who did the training. The result was promising, this proposed method produced high accuracy, reach 91% which shows that this proposed method is user independent.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Multi-projection deep learning network for segmentation of 3D medical images},

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

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=TEBpO74AAAAJ&citation_for_view=TEBpO74AAAAJ:_FxGoFyzp5QC},

year  = {2019},

date = {2019-07-01},

journal = {Pattern Recognition Letters (Q1)},

volume = {125},

pages = {791-797},

abstract = {Segmentation of three-dimensional (3D) medical images using deep learning is a challenging task due to the lack of a 3D medical image dataset and their ground truth, resource memory limitations, and imbalanced dataset problem. In this paper, we propose advanced deep learning network for segmentation of 3D medical images. The proposed Multi-projection Network can preserve resource memory by applying two-dimensional (2D) kernels while still obtaining the 3D information from the image by incorporating slices from different planar projections of the 3D image to achieve good segmentation results. The proposed network uses a weighted cost function to address the imbalanced dataset problem and introduces an adaptive weight that considers the probability of each class in the image. The experimental results showed that the proposed Multi-projection Network can produce the highest sensitivity (true …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Design and Development of a Mechanical Linear Scanning Device for the Three-Dimensional Ultrasound Imaging System},

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

url = {https://ijeei.org/docs-1493143405d47f8fc05114.pdf},

year  = {2019},

date = {2019-06-27},

journal = {International Journal on Electrical Engineering & Informatics (Q3)},

volume = {11},

number = {2},

abstract = {Three-dimensional reconstruction from a set of a two-dimensional ultrasound image is  useful for visualizing anatomy structures of an internal organ. This paper presents a mechanical linear scanning device for a three-dimensional ultrasound imaging system to acquire sequences of two-dimensional ultrasound image along with its position and orientation in three-dimensional space. The device included a linear sliding track driven by a stepper motor with a dedicated housing of a probe, a water tank with an object holder inside, and software installed on a standard computer. A mechanical sliding track enabled the two-dimensional conventional ultrasound probe moved in a regular linear manner. The mechanical linear scanning allowed fixed and predefined translation steps in z-axis direction such that rotation error could be avoided. In 

addition, mathematical formulations were presented to relocate every pixel’s location of the ultrasound image to the reconstruction volume in three-dimensional space. In the experiments, the device’s installation in a real environment was demonstrated, and the translation motion in 

space was investigated. Evaluation of the scanning results using the proposed device was performed using a bone as a phantom. The experimental result showed that the proposed device produced high quality scanning results and created high quality bone surface. Thus, it can be expected would be useful to the three-dimension reconstruction applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A Systematic Evaluation of Shallow Convolutional Neural Network on CIFAR Dataset},

author = {Reza Fuad Rachmadi and I Purnama and Mauridhi Hery Purnomo and Mochamad Hariadi},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=25v5QvgAAAAJ&citation_for_view=25v5QvgAAAAJ:ZHo1McVdvXMC},

year  = {2019},

date = {2019-06-01},

journal = {IAENG International Journal of Computer Science (Q2)},

volume = {46},

abstract = {Abstract Convolutional Neural Network (CNN) classifier is a very popular classifier used to solve many problems, including image classification and object recognition. The CNN classifier usually improved by designing a deeper and bigger classifier which needs more memory and computational power to run the classifier. In this paper, we analyze and optimize the use of small and shallow CNN classifier on CIFAR dataset. Karpathy ConvNetJS CIFAR10 model was used as a base network of our classifier and extended by adding max-min pooling method. The max-min pooling is used to explore the negative and positive response of the convolution process which in theory will be trained the classifier more effectively. We choose several different configurations to analyze the effectiveness of the classifier by combining the training algorithm, batch normalization configuration, weights initialization methods, dropout …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Accuracy on Bovine Bone Fracture Detection of Two Dimensional B–Mode Ultrasound Images using Polynomial–Intensity Gradient},

author = {Rika Rokhana and Eko Mulyanto Yuniarno and I Ketut Eddy Purnama and Kayo Yoshimoto and Hideya Takahashi and Mauridhi Hery Purnomo},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=6SBE6WYAAAAJ&citation_for_view=6SBE6WYAAAAJ:ufrVoPGSRksC},

year  = {2019},

date = {2019-06-01},

journal = {IAENG International Journal of Computer Science (Q2)},

volume = {46},

abstract = {The detection of bone fracture uses X-rays or CT-scans device typically. These instruments have a negative effect of radiation and need high security for both patients and medical technicians. In this paper, we proposed a framework using the high–order polynomial approach and intensity gradient of two–dimensional B–mode ultrasound images for bone fracture detection. According to the ultrasound probe position, bone scanning process produce curved and flat contour surface. The local phase symmetry and morphology operation is used to extract the bone surface feature from the speckles and other noise. Then, a high order polynomial equation is used to obtain the center mass in the bone area. Two methods, Polynomial Tangent Perpendicular Line (PTPL) and Axis Perpendicular Line method are applied to determine the intensity gradient between adjacent columns based on the center of the mass bone area. These methods are tested to the bovine bone with no–fracture bone, and bone with transverse, oblique and comminuted fractures. Both PTPL and APL methods had 100% accuracy in the detection of fracture occurrence. For estimation width of fracture, the PTPL was more accurate than APL method. In the curved contour bone surface, the PTPL method has 1.14% error with the mean absolute error (MAE) of 0.016 mm. While the APL method has 2.63% error with the MAE of 0.04 mm. Meanwhile, in the flat contour bone surface, the PTPL method has 2.41% error with the MAE of 0.03 mm while the APL method has 3.21% error with the MAE of 0.04 mm.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Segmenting Tooth Components in Dental X-Ray Images Using Gaussian Kernel-Based Conditional Spatial Fuzzy C-Means Clustering Algorithm},

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

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=-5mgvWsAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=-5mgvWsAAAAJ:BW2nPTmhBn4C},

year  = {2019},

date = {2019-06-01},

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

volume = {12},

number = {3},

pages = {108-117},

abstract = {Telah diperiksa dan divalidasi dengan baik, dan sampai pernyataan ini dibuat sebagai karya ilmiah original/plagiat*, sehingga kami turut bertanggung jawab bahwa karya ilmiah tersebut telah memenuhi syarat kaidah ilmiah, norma akademik, dan norma hukum, sesuai dengan Peraturan Menteri Pendidikan Nasional Nomor 17 Tahun 2010 tanggal 16 Agustus 2010 tentang Pencegahan dan Pananggulangan Plagiat di Perguruan Tinggi. Namun demikian, apabila di kemudian hari ternyata terbukti bahwa karya ilmiah tersebut merupakan karya Ilmiah Plagiat, maka akan menjadi tanggung jawab mutlak penulis tersebut di atas, baik secara perdata maupun pidana. Demikian surat pernyataan ini saya buat untuk dipergunakan sebagaimana mestinya},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Convolutional neural network untuk pendeteksian patah tulang femur pada citra ultrasonik b–mode},

author = {Rika Rokhana and Joko Priambodo and Tita Karlita and I Made Gede Sunarya 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&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=_bttBQEAAAAJ:isU91gLudPYC},

year  = {2019},

date = {2019-03-02},

journal = {Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)},

volume = {8},

number = {1},

pages = {59},

abstract = {The bone fracture detection using X–rays or CT–scan produces accurate images but has harmful effect radiation. This paper presented the use of ultrasonic waves (US) as an alternative to substitute those two instruments. This study used femur bovine and chicken bones in conditions with and without meat. The fractures are artificially made on transverse and oblique patterns. The scanning US probe produces twodimensional (2D) B–mode images. Fracture detection is done using five variations of the Convolutional Neural Network (CNN) architectural design, ie, CNN1–CNN5. The results showed that the CNN4 is the best design of bone contour recognition and bone fracture classification compared to the other tested designs, with 95.3% accuracy, 95% sensitivity, and 96% specificity. The comparison with the Support Vector Machine (SVM) and k-NN classification methods indicate that CNN has superior performance in accuracy, sensitivity, and specificity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Converting image into bas reliefs using image processing techniques},

author = {Nanik Suciati and Maserati Teja Baihaqi and Handayani Tjandrasa and Agus Zainal Arifin and Chastine Fatichah and Anny Yuniarti and Dini Adni Navastara and Arya Yudhi Wijaya and Wijayanti Nurul Khotimah and Henning Titi Ciptaningtyas},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=m56Hd_wAAAAJ&citation_for_view=m56Hd_wAAAAJ:HDshCWvjkbEC},

year  = {2019},

date = {2019-03-01},

journal = {Journal of Physics: Conference Series},

volume = {1196},

number = {1},

pages = {012037},

abstract = {In this study, we develop an application to convert an image into bas reliefs, a kind of 3D sculpture on a flat surface, by using image processing techniques. First, the color image is converted into gray image, and then is used as a base surface in xy-coordinate. Two different methods for determining height in z-coordinate provides two different carving styles. In the first method, the height is specified by using gray value of the blurred image, while the second specifies the height by using gray value of the dilation of the edge image. Frame effect is then created by adding a flat plane around the carving. The experiment shows that the application can transfer visual contents of the image into the bas reliefs and also can generate artistic reliefs model easily.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Recent development in electronic nose data processing for beef quality assessment},

author = {Riyanarto Sarno and Dedy Rahman Wijaya},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=xBxSZaAAAAAJ&citation_for_view=xBxSZaAAAAAJ:ULOm3_A8WrAC},

year  = {2019},

date = {2019-02-01},

journal = {Telkomnika (Q2)},

volume = {17},

number = {1},

pages = {337-348},

abstract = {Beef is kind of perishable food that easily to decay. Hence, a rapid system for beef quality assessment is needed to guarantee the quality of beef. In the last few years, electronic nose (e-nose) is developed for beef spoilage detection. In this paper, we discuss the challenges of e-nose application to beef quality assessment, especially in e-nose data processing. We also provide a summary of our previous studies that explains several methods to deal with gas sensor noise, sensor array optimization problem, beef quality classification, and prediction of the microbial population in beef sample. This paper might be useful for researchers and practitioners to understand the challenges and methods of e-nose data processing for beef quality assessment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Modifikasi Fitur dengan Differential Asymmetry untuk Meningkatkan Akurasi Klasifikasi EEG Motor Imagery},

author = {Yulianto Tejo Putranto and Tri Arief Sardjono and Mochamad Hariadi 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:C33y2ycGS3YC},

year  = {2019},

date = {2019-02-01},

journal = {Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)},

volume = {8},

number = {1},

abstract = {Brain-Computer Interface (BCI) technology has enabled people with motor disabilities to interact with their environment. The electroencephalograph (EEG) signals related to a motor imagery movement were used as a control signal. In this paper, EEG motor imagery signals from the 2-class data have been processed into features and classified. The power and standard deviation of EEG signals, mean of absolute wavelet coefficients, and the average power of the wavelet coefficients were used as features. The purpose of this paper is to apply the differential asymmetry of these features as new features to improve the system accuracy. As a classifier, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Tree were used. The result shows that for dataset I the use of differential asymmetry as feature can increase the system accuracy up to 47.8%, from 52.20% to 100%, with Tree as a classifier. For dataset II, it can increase accuracy by 8.46%, from 54.42% to 62.48%.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Detection of overlapping teeth on dental panoramic radiograph},

author = {Agus Zainal Arifin, Safri Adam and Avin Maulana Mohammad and Fatoni Anggris and Rarasmaya Indraswari 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:bEWYMUwI8FkC},

year  = {2019},

date = {2019-01-01},

journal = {Int. J. Intell. Eng. Syst},

volume = {12},

pages = {71-80},

abstract = {Segmentation of single tooth in dental panoramic images is an important process to extract its features and information. However, it might be challenging when the segmentation process faces an overlapping teeth image. In this research, we introduce a new strategy for detecting overlapping area on dental panoramic radiographs automatically. This research proposes automatic thresholding to obtain marking points for the overlapping area and an automatic selection of overlapping area candidates by using the area orientation and the similarity of neighborhood intensity. The experimental results on 44 images show that our proposed strategy can detect overlapping teeth on the dental panoramic radiograph with accuracy, sensitivity, and specificity of 75%, 66.67%, and 85%, respectively. The evaluation conducted on 24 overlapping teeth images shows that the segmentation results of overlapping teeth area have an average misclassification error of 0.31%.},

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tppubtype = {article}

}

@article{nokey,

title = {3D Region Merging for Segmentation of Teeth on Cone-Beam Computed Tomography Images},

author = {Rarasmaya Indraswari and Takio Kurita and Agus Zainal Arifin and Nanik Suciati and Eha Renwi Astuti and Dini Adni Navastara},

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

year  = {2018},

date = {2018-12-05},

journal = {2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS)},

pages = {341-345},

abstract = {Segmentation of teeth in Cone-Beam Computed Tomography (CBCT) images is challenging problem due to its noise and the similar grayscale intensity of bone and teeth element. In this paper we proposed a new method based on three-dimensional (3D) region merging and histogram thresholding for automatic segmentation of teeth on CBCT images. The proposed 3D region merging algorithm can recognized the teeth element that have similar intensity with the bone element based on the three-dimensional (3D) information of the neighboring slices of the CBCT image. Merging the teeth region will lead to more homogenous grayscale intensity distribution inside the teeth. Then histogram thresholding that utilized the characteristic of CBCT images is performed to binarize the grayscale images and obtain the teeth object. The average accuracy, sensitivity, and specificity of the proposed method are 97.75%, 80.22 …},

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pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Classification of EEG signal for detecting cybersickness through time domain feature extraction using Naïve bayes},

author = {Moch Asyroful Mawalid and Alfi Zuhriya Khoirunnisa and Mauridhi Hery Purnomo and Adhi Dharma Wibawa},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=_bttBQEAAAAJ&cstart=100&pagesize=100&sortby=pubdate&citation_for_view=_bttBQEAAAAJ:mel-f30kHHgC},

year  = {2018},

date = {2018-11-26},

journal = {2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM)},

pages = {29-34},

abstract = {Recently the rapid developments in entertainment such as 3D movies and video games, causing the phenomenon of cybersickness to be a very serious topic among health experts. Cybersickness occurs when the human exposure in virtual environment so that it can cause negative effect like headache, fatigue, eyestrain and vomiting. It can disturb the physical and physiological of the human if it is not minimized properly. Many studies have been done to investigate cybersickness using several methods. One of the most common method is using Electroencephalograph (EEG). However, previously there were not many studies that explored time domain feature extraction in investigating cybersickness. In this paper, Nine healthy participants (7 male and 2 female) were measured using EEG during playing 3D video game. Time domain feature extraction, such as statistical features (e.g., mean, variation, standard deviation …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Channel selection of EEG emotion recognition using stepwise discriminant analysis},

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

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=_bttBQEAAAAJ&cstart=100&pagesize=100&sortby=pubdate&citation_for_view=_bttBQEAAAAJ:YB4bud6kWLwC},

year  = {2018},

date = {2018-11-26},

journal = {2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM)},

pages = {14-19},

abstract = {EEG has been used by many applications recently, not only in the field of medicine but also telemarketing, games, and cybernetics. Measuring brain signal by involving EEG is complicated and delicate work because it involves many channels, frequency bands, and features. An efficient and effective method in EEG measurement is then becoming crucial among the scientists. This paper proposed a channel selection study for emotion recognition based on the EEG signal by using Stepwise Discriminant Analysis (SDA). SDA is the extension of statistical tool for discriminant analysis that include stepwise technique. In this paper, the data was obtained from the public emotion EEG dataset which was recorded using 62 channels of EEG devices for three target emotions (i.e., positive, negative and neutral). In order to handle high dimensionality in EEG signals, we extracted differential entropy feature from five frequency …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Single Image Vehicle Classification using Pseudo Long Short-Term Memory Classifier},

author = {Reza Fuad Rachmadi and Keiichi Uchimura and Gou Koutaki and Kohichi Ogata},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=25v5QvgAAAAJ&citation_for_view=25v5QvgAAAAJ:70eg2SAEIzsC},

year  = {2018},

date = {2018-10-01},

urldate = {2018-10-01},

journal = {Journal of Visual Communication and Image Representation (Q2)},

volume = {56},

pages = {265-274},

abstract = {In this paper, we propose a pseudo long short-term memory (LSTM) classifier for single image vehicle classification. The proposed pseudo-LSTM (P-LSTM) uses spatially divided images rather than time-series images. In other words, the proposed method considers the divided images to be time-series frames. The divided images are formed by cropping input images using two-level spatial pyramid region configuration. Parallel convolutional networks are used to extract the spatial pyramid features of the divided images. To explore the correlations between the spatial pyramid features, we attached an LSTM classifier to the end of the parallel convolutional network and treated each convolutional network as an independent timestamp. Although LSTM classifiers are typically used for time-dependent data, our experiments demonstrated that they can also be used for non-time-dependent data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Hierarchical Clustering Linkage for Region Merging in Interactive Image Segmentation on Dental Cone Beam Computed Tomography},

author = {Agus Zainal Arifin and Siska Arifiani and Arna Fariza and Dini Adni Navastara and Rarasmaya Indraswari},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=RHRa-DoAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=RHRa-DoAAAAJ:hC7cP41nSMkC},

year  = {2018},

date = {2018-09-03},

journal = {2018 International Conference on Applied Information Technology and Innovation (ICAITI)},

pages = {124-128},

abstract = {Interactive image segmentation has a better result than the automatic and manual image segmentation because a user can help the image segmentation algorithm by marking the sample of background and object in the image. The algorithm will merge the regions in the image based on the user marking. In interactive image segmentation, the calculation of the distance between regions and the sequence of the merging process is important to obtain an accurate segmentation result. In this paper, we proposed a new region merging strategy using hierarchical clustering based on interclass and intra-class variances for each region and neighborhood relationship. This research aims to improve the region merging strategy and it is expected to result better than the previous research that did not implement the hierarchical clustering. The process to segment an image concludes splitting the image into several regions …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Noise Cancellation in Gamelan Signal by Using Least Mean Square Based Adaptive Filter},

author = {Diah Puspito Wulandari and Yoyon Kusnendar Suprapto},

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

year  = {2018},

date = {2018-09-01},

journal = {International Journal of Simulation--Systems, Science & Technology (Q4)},

volume = {19},

abstract = {Gamelan is one of Indonesian traditional music instrument that has been worldwide. Noise reduction of identical instrument is a key challenge for instrument recognition, music processing and instrument analysis. Many theoretical analysis and experiments have been carried out to show that the optimal filtering technique can reduce the level of noise that is present in the instrument signal. In this paper, we conducted a study for noise removal on gamelan instruments using least-mean-square (LMS). Using the original signal mixed with noise, the result that enlarging the rate of convergence, filter order, and iteration can improve the LMS function in noise removal in the instrument gamelan. The performance of the designed adaptive filter is evaluated based on the mean square error by varying the additive white Gaussian noise levels. We found that the performance of Least Mean Square is satisfactory and is viable to be applied in gamelan signal.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Hybrid Speckle Noise Reduction Method for Abdominal Circumference Segmentation of Fetal Ultrasound Images.},

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

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=_hdd4OgAAAAJ&citation_for_view=_hdd4OgAAAAJ:XiSMed-E-HIC},

year  = {2018},

date = {2018-06-01},

journal = {International Journal of Electrical & Computer Engineering (Q2)},

volume = {8},

number = {3},

abstract = {Fetal biometric size such as abdominal circumference (AC) is used to predict fetal weight or gestational age in ultrasound images. The automatic biometric measurement can improve efficiency in the ultrasonography examination workflow. The unclear boundaries of the abdomen image and the speckle noise presence are the challenges for the automated AC measurement techniques. The main problem to improve the accuracy of the automatic AC segmentation is how to remove noise while retaining the boundary features of objects. In this paper, we proposed a hybrid ultrasound image denoising framework which was a combination of spatial-based filtering method and multiresolution based method. In this technique, an ultrasound image was decomposed into subbands using wavelet transform. A thresholding technique and the anisotropic diffusion method were applied to the detail subbands, at the same time the bilateral filtering modified the approximation subband. The proposed denoising approach had the best performance in the edge preservation level and could improve the accuracy of the abdominal circumference segmentation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Sentence Extraction Based on Sentence Distribution and Part of Speech Tagging for Multi-document Summarization},

author = {Agus Zainal Arifin and Moch Zawaruddin Abdullah and Ahmad Wahyu Rosyadi and Desepta Isna Ulumi and Aminul Wahib and Rizka Wakhidatus Sholikah},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=sv&user=Zs3q9wcAAAAJ&citation_for_view=Zs3q9wcAAAAJ:eQOLeE2rZwMC},

year  = {2018},

date = {2018-04-01},

journal = {Telkomnika (Q2)},

volume = {16},

number = { 2},

pages = {843-851},

abstract = {Automatic multi-document summarization needs to find representative sentences not only by sentence distribution to select the most important sentence but also by how informative a term is in a sentence. Sentence distrib ution is suitab le for ob taining important sentences by determining frequent and well-spread words in the corpus but ignores the grammatical information that indicates instructive content. The presence or ab sence of informative content in a sentence can be indicated by grammatical information which is carried by part of speech (POS) labels. In this paper, we propose a new sentence weighting method by incorporating sentence distribution and POS tagging for multi-document summarization. Similarity-based Histogram Clustering (SHC) is used to cluster sentences in the data set. Cluster ordering is based on cluster importance to determine the important clusters. Sentence extraction based on sentence distribution and POS tagging is introduced to extract the representative sentences from the ordered clusters. The results of the experiment on the Document Understanding Conferences (DUC) 2004 are compared with those of the Sentence Distribution Method. Our proposed method achieved better results with an increasing rate of 5.41% on ROUGE-1 and 0.62% on ROUGE-2.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Fuzzy Region Merging Using Fuzzy Similarity Measurement on Image Segmentation},

author = {Wawan Gunawan and Agus Zainal Arifin and Rarasmaya Indraswari and Dini Adni Navastara},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=TEBpO74AAAAJ&citation_for_view=TEBpO74AAAAJ:9yKSN-GCB0IC},

year  = {2017},

date = {2017-12-01},

journal = {International Journal of Electrical & Computer Engineering (Q2)},

volume = {7},

number = {6},

pages = {3402},

abstract = {Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Region merging strategy using statistical analysis for interactive image segmentation on dental panoramic radiographs},

author = {Agus Zainal Arifin and Rarasmaya Indraswari and Nanik Suciati and Eha Renwi Astuti and Dini Adni Navastara},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=TEBpO74AAAAJ&citation_for_view=TEBpO74AAAAJ:d1gkVwhDpl0C},

year  = {2017},

date = {2017-09-01},

journal = {Int. Rev. Comput. Softw (Q4)},

volume = {12},

number = {1},

pages = {63},

abstract = {In low contrast images such as dental panoramic radiographs, the optimum parameters for automatic image segmentation is not easily determined. Semi-automatic image segmentation which is interactively guided by user is one alternative that could provide a good segmentation results. In this paper we proposed a novel strategy of region merging in interactive image segmentation using discriminant analysis on dental panoramic radiographs. A new similarity measurement among regions is introduced. This measurement merges regions which have minimal inter-class variance either with object or background cluster. Since the representative sample regions are selected by user, the similarity between merged regions with the corresponded samples could be preserved. Experimental results show that the proposed region merging strategy give a high segmentation accuracy both for low contrast and natural images.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Development of automated flow measurement system for cell‐based odor sensor},

author = {Yuji Sukekawa and Totok Mujiono and Takamichi Nakamoto and Hidefumi Mitsuno and Yuko Nakajima and Ryohei Kanzaki and Nobuo Misawa},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=id&user=V-bOulMAAAAJ&citation_for_view=V-bOulMAAAAJ:3fE2CSJIrl8C},

year  = {2017},

date = {2017-09-01},

journal = {Electronics and Communications in Japan},

volume = {100},

number = {9},

pages = {41-49},

abstract = {Odor sensing system with high sensitivity and selectivity is expected to be realized by using biological mechanisms such as olfactory receptor. We have already developed a cell‐based odor sensing system which acquires odorant concentration as a fluorescent intensity change. In the measurement, however, an artifact is likely to happen due to lack of stable and reproducible methods for delivering odorants to biological cells. Therefore, we developed and evaluated the automated flow measurement system which can simultaneously control pump, solenoid valve, and camera. Our system realized automation of solution supply and fluorescent measurement. As a result, our system could reduce artifacts and repeatedly performed measurements with the same sensor cells.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Region merging strategy using statistical analysis for interactive image segmentation on dental panoramic radiographs},

author = {Agus Zainal Arifin and Rarasmaya Indraswari and Nanik Suciati and Eha Renwi Astuti and Dini Adni Navastara},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=TEBpO74AAAAJ&citation_for_view=TEBpO74AAAAJ:d1gkVwhDpl0C},

year  = {2017},

date = {2017-09-01},

journal = {Int. Rev. Comput. Softw (Q4)},

volume = {12},

number = {1},

pages = {63},

abstract = {In low contrast images such as dental panoramic radiographs, the optimum parameters for automatic image segmentation is not easily determined. Semi-automatic image segmentation which is interactively guided by user is one alternative that could provide a good segmentation results. In this paper we proposed a novel strategy of region merging in interactive image segmentation using discriminant analysis on dental panoramic radiographs. A new similarity measurement among regions is introduced. This measurement merges regions which have minimal inter-class variance either with object or background cluster. Since the representative sample regions are selected by user, the similarity between merged regions with the corresponded samples could be preserved. Experimental results show that the proposed region merging strategy give a high segmentation accuracy both for low contrast and natural images.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Learning and recognition of clothing genres from full-body images},

author = {Shintami C Hidayati and Chuang-Wen You and Wen-Huang Cheng and Kai-Lung Hua},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=hEnPIToAAAAJ&citation_for_view=hEnPIToAAAAJ:5nxA0vEk-isC},

year  = {2017},

date = {2017-06-19},

journal = {IEEE transactions on cybernetics},

volume = {48},

number = {5},

pages = {1647-1659},

abstract = {According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory. In addition, the corresponding salient visual features of each style element were identified and formulated with variables that can be computationally derived with various computer vision algorithms. To evaluate the performance of our algorithm, a dataset containing 3250 full-body …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Biomedical engineering research in the social network analysis era: stance classification for analysis of hoax medical news in social media},

author = {Mauridhi Hery Purnomo and Surya Sumpeno and Esther Irawati Setiawan and Diana Purwitasari},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=ekvoWE4AAAAJ&citation_for_view=ekvoWE4AAAAJ:UxriW0iASnsC},

year  = {2017},

date = {2017-01-01},

journal = {Procedia Computer Science},

volume = {116},

pages = {3-9},

abstract = {Biomedical engineering research trend can be healthcare models with unobtrusive smart systems for monitoring vital signs and physical activity. Detecting infant facial cry because of inability to communicate pain, recognizing facial emotion to understand dysfunction mechanisms through micro expression or transform captured human expression with motion device into three-dimensional objects are some of the applied systems. Nowadays, collaborated with biomedical research, mining and analyzing social network can improve public and private health care sectors as well such as research health news shared on social media about pharmaceutical drugs, pandemics, or viral outbreaks. Due to the vast amount of shared news, there is an urgency to select and filter information to prevent the spread of hoax or fake news. We explored in depth some steps to classify hoaxes written as news articles.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A framework for human spine imaging using a freehand 3D ultrasound system},

author = {Ketut E Purnama and Michael Wilkinson and Albert G Veldhuizen and Peter van Ooijen and Jaap Lubbers and Johannes GM Burgerhof and Tri A Sardjono and Gijbertus J Verkerke},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=cWaY0cUAAAAJ&citation_for_view=cWaY0cUAAAAJ:zA6iFVUQeVQC},

year  = {2010},

date = {2010-01-01},

journal = {Technology and Health Care (Q3)},

volume = {18},

number = {1},

pages = {1-17},

abstract = {The use of 3D ultrasound imaging to follow the progression of scoliosis, ie, a 3D deformation of the spine, is described. Unlike other current examination modalities, in particular based on X-ray, its non-detrimental effect enables it to be used frequently to follow the progression of scoliosis which sometimes may develop rapidly. Furthermore, 3D ultrasound imaging provides information in 3D directly in contrast to projection methods. This paper describes a feasibility study of an ultrasound system to provide a 3D image of the human spine, and presents a framework of procedures to perform this task. The framework consist of an ultrasound image acquisition procedure to image a large part of the human spine by means of a freehand 3D ultrasound system and a volume reconstruction procedure which was performed in four stages: bin-filling, hole-filling, volume segment alignment, and volume segment compounding …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Identification of color uniformity defect on the electronic displays by learning the human perception records},

author = {Mauridhi Hery Purnomo and Toshio Asano and Eiji Shimizu},

url = {https://scholar.google.co.id/citations?view_op=view_citation&hl=en&user=_bttBQEAAAAJ&cstart=100&pagesize=100&citation_for_view=_bttBQEAAAAJ:ULOm3_A8WrAC},

year  = {1998},

date = {1998-07-01},

journal = {IEEJ Transactions on Electronics Information and Systems (Q3)},

volume = {118},

number = {7-8},

pages = {1164-1169},

abstract = {This article explores a proposed method for identifying and classifying the color uniformity defect on the electronic displays. A neural network learning approach utilizing the backpropagation learning algorithm is employed to search the dissimilarity of the color display condition. The color uniformity defect image perception among several conditions of some observations by human eyesight is used to supervise the training data of the neural networks. A supervisor of the training data is obtained by human expert eyes evaluate the white uniformity grade and compare with the standard grade, then labeling the perception grade into some certainty values. The trained network is used to identify and classify the grades level of the color uniformity defect on the electronic displays. For the experimental purpose, a simulation program is developed to generate the color uniformity defect on the monitor display. We make a comparison with a classical regression analysis method for validation of the proposed method.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}