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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Majalah Ilmiah Teknologi Elektro Jurnal Teknik ITS IPTEK The Journal for Technology and Science Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer agriTECH Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) EMITTER International Journal of Engineering Technology Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science Jurnal Sains Dan Teknologi (SAINTEKBU) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Inotera Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) CCIT (Creative Communication and Innovative Technology) Journal JAVA Journal of Electrical and Electronics Engineering JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Makara Journal of Technology Jurnal Rekayasa elektrika Majalah Ilmiah Teknologi Elektro
Mauridhi Hery Purnomo
EEPIS
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Other

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Pengoperasian Penyimpan Energi Udara Tekan Skala Kecil Mode Discharged pada Efisiensi Maksimum Andi Setiawan; Ardyono Priyadi; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 2: Mei 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

This paper presents the results of research on the development of small-scale compressed air storage operating at its maximum efficiency. In contrast to what was done in previous studies using a buck converter on the output side of the generator, here a boost converter is used. The use of a boost converter allows the turbines to operate at a reasonably low speed and can still produce the desired voltage for the load, which is 12 volts. This strategy is chosen based on the characteristics of the turbine which indicates that the turbine will operate with better efficiency if operated at lower speeds. In this study, the search for maximum efficiency points is done by perturb and observe (P&O) method. The simulation results show that this strategy is effective for obtaining operating points with maximum efficiency if the load power is small relative to the nominal power of the turbine-generator, which is below 50%.
Convolutional Neural Network untuk Pendeteksian Patah Tulang Femur pada Citra Ultrasonik B–Mode Rika Rokhana; Joko Priambodo; Tita Karlita; I Made Gede Sunarya; Eko Mulyanto Yuniarno; I Ketut Eddy Purnama; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 1: Februari 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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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 two-dimensional (2D) B–mode images. Fracture detection is done using five variations of the Convolutional Neural Network (CNN) architectural design, i.e., 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.
Deteksi Region of Interest Tulang pada Citra B-mode secara Otomatis Menggunakan Region Proposal Networks Tita Karlita; I Made Gede Sunarya; Joko Priambodo; Rika Rokhana; Eko Mulyanto Yuniarno; I Ketut Eddy Purnama; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 1: Februari 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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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 b-mode 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.
Modifikasi Fitur dengan Differential Asymmetry untuk Meningkatkan Akurasi Klasifikasi EEG Motor Imagery Yulianto Tejo Putranto; Tri Arief Sardjono; Mochamad Hariadi; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 1: Februari 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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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%.
Deteksi Gestur Lengan Dinamis pada Lingkungan Virtual Tiga Dimensi Koleksi Warisan Budaya Adri Gabriel Sooai; Atyanta. N. Rumaksari; Khamid Khamid; Nurul Zainal Fanani; Surya Sumpeno; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

Virtual reality technology can be used to support museum exhibitions. Implementation could be in various platforms. There are many implementation options, for example in smartphones, tablet, and desktop computers. Most objects of museum collections are very fragile. Minimizing the direct touch on a collection object is one of the benefits of this technology. This study aims to prepare gestures suitable for the exploration of virtual objects of cultural heritage collection. Five sets of gestures have been prepared, namely lifting, picking, holding, sweeping from both directions, left and right. Dynamic arm gestures are recorded using the forearm sensor. The recorded data contains coordinates of gestures in form of x, y, z, raw, pitch, and yaw. Gaussian mixture models are used in selecting features to produce good accuracy in the classification process.Two functions are used, namely probability density function and cumulative distribution function for the feature selection process. In this study, two experiments were used to train the gesture model. The accuracy of the two experiments is shown in the form of a confusion matrix. Each of the confusion matrices show excellent results of 99.8% for SVM and k-NN. Furthermore, modeling results are tested using new data. The testing shows 89.25% result for SVM classifier and 90.09% for k-NN. Four other dynamic arm gestures have a very satisfactory rate of 100% for the two classifiers. The five gestures can be used in the development of virtual reality applications.
Ekstraksi Ciri Produktivitas Dinamis untuk Prediksi Topik Pakar dengan Model Discrete Choice Diana Purwitasari; Chastine Fatichah; Surya Sumpeno; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

Recommendation of active or productive experts is indispensable in supporting collaborations. Activities of publication and citation indicate expert productivity. An expert can be inferred to have an interest in a subject through productivity in that particular topic. Since an expert can change interests over time, the contribution of this paper is a Discrete Choice Model (DCM) based on topic productivities to predict the primary interests of the experts. DCM uses features extracted from bibliographic data of citation relation and title-abstract texts. Before extracting productivity features and dynamicity features to represent interest changes, title clustering with KMeans++ is used to identify research topics. There are six productivity features and five dynamicity values for each productivity feature to demonstrate the expert behavior. Therefore, a clustered topic as a research interest is represented as an expert choice with 30 extracted features in the proposed method. The experiments used multinomial logistic regression for DCM and a log-likelihood indicator for the fitted models of the features. The resulted DCM models showed that productive behavior of the experts by doing many publications and receiving many citations effected to the precision of topic prediction by 80%. Some features were better for predicting primary interests of the expert. It was demonstrated with a lower precision value of 60% by using features that represent the expert behavior of only doing publication or only getting citation.
Peningkatan Akurasi Pengenalan Emosi pada Sinyal Electroencephalograpy Menggunakan Multiclass Fisher Evi Septiana Pane; Adhi Dharma Wibawa; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

EEG signals have a significant correlation to emotions when compared to other external appearances such as face and voice. Due to the low accuracy of emotional recognition through EEG signals, this study proposes a dimensional reduction method for EEG data to address that problem using Multiclass Fisher Discriminant Analysis (MC-FDA). In this study, the experiment was applied on public EEG dataset with three classes of emotions, namely positive, negative, and neutral. Differential entropy features were extracted from the decomposed EEG signals in five frequency band of the delta, theta, alpha, beta, and gamma. The accuracy of emotion recognition was measured using two prevalent classifiers on EEG identification, such as LDA and SVM. To demonstrate the superiority of the MC-FDA method, the PCA dimension reduction method was applied as a comparison. Classification accuracy results from all experiment scenario showed the advantages of the MC-FDA compared to the PCA.The best emotion classification accuracy was obtained from trials on all data from twelve electrodes using the MC-FDA and LDA methods, namely 93.3%. These results show a mean increase in accuracy of 3.5 points from the original feature vector dataset.
Pengenalan Viseme Dinamis Bahasa Indonesia Menggunakan Convolutional Neural Network Aris Nasuha; Tri Arief Sardjono; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 3: Agustus 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

There has been very little researches on automatic lip reading in Indonesian language, especially the ones based on dynamic visemes. To improve the accuracy of a recognition process, for certain problems, choosing suitable classifiers or combining of some methods may be required. This study aims to classify five dynamic visemes of Indonesian language using a CNN (Convolutional Neural Network) and to compare the results with an MLP (Multi Layer Perceptron). Varying some parameters theoretically improving the recognition accuracy was attempted to obtain the best result. The data includes videos on pronunciation of daily words in Indonesian language by 28 subjects recorded in frontal view. The best recognition result gives 96.44% of validation accuracy using the CNN classifier with three convolution layers.
Klasifikasi Nyeri pada Video Ekspresi Wajah Bayi Menggunakan DCNN Autoencoder dan LSTM Yosi Kristian; I Ketut Eddy Purnama; Effendy Hadi Sutanto; Lukman Zaman; Esther Irawati Setiawan; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 3: Agustus 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

Babies are still unable to inform the pain theyexperience, therefore, babies cry when experiencing pain. With the rapid development of computer vision technologies, in the last few years, many researchers have tried to recognize pain from babies expressions using machine learning and image processing. In this paper, a research using Deep Convolution Neural Network (DCNN) Autoencoder and Long-Short Term Memory (LSTM) Network is conducted to detect cry and pain level from baby facial expression on video. DCNN Autoencoder isused to extract latent features from a single frame of baby face. Sequences of extracted latent features are then fed to LSTM sothe pain level and cry can be recognized. Face detection and face landmark detection is also used to frontalize baby facial imagebefore it i s processed by DCNN Autoencoder. From the testing on DCNN autoencoder, the result shows that the best architecture used three convolutional layers and three transposed convolutional layers. As for the LSTM classifier, the best model is using four frame sequences.
Optimasi Support Vector Machine untuk Memprediksi Adanya Mutasi pada DNA Hepatitis C Virus Berlian Al Kindhi; Tri Arief Sardjono; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 3: Agustus 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

Hepatitis C Virus (HCV) is a virus which capable of infecting RNA that can lead to changes in the DNA sequence. This change of DNA arrangement is called genetic mutation. Every mutation occurs in HCV, it will be called a new subtype. Over time, HCV subtypes increase, and will continue to grow as the HCV mutation cycle progresses faster. Therefore, a way to find a mutation in millions of sequences in the gene bank is needed. This study tested six types of Support Vector Machine (SVM) methods to determine the best SVM kernel performance in the application of HCV DNA sequence detection in isolatedDNA. The tested SVM kernel was linear, quadratic, cubic, fine Gaussian, median Gaussian, and coarse Gaussian. The data set is 1000 isolated DNA consisting of 500 isolated Homo Sapiens and 500 isolated HCV. Firstly, the data set will go through the pattern search process using the Edit Levenshtein Distance method, then the result of the processing will be the variable x in SVM. The target or variable y on SVM is the positive or negative value of the isolated against HCV. The results show that among the six types of SVM methods being tested, the method of fine Gaussian SVM has the lowest performance of 77.4%. The SVM method is tested by performing optimizations on the determination of the hyperplane. The test results proved that the SVM method is able to analyze the presence of HCV mutations in isolated DNA with an accuracy of 99.8%.
Co-Authors Abdillah, Abid Famasya Adhi Dharma Wibawa Adhi Dharma Wibawa Adhi Dharma Wibawa, Adhi Dharma Adhi Kusmantoro Adi Soeprijanto Adi Soeprijanto Adi Soepriyanto Adi Sutanto Adri Gabriel Sooai Adriel Ferdianto Afandi, Acxel Derian Affan, Lazuardi Yaqub Agung Dewa Bagus Soetiono Agung Mega Iswara Agung Wicaksono Agus Dharma Agustinus Bimo Gumelar Ahmad Muslich Al Kindhi, Berlian Alamsyah Alamsyah - Alfiyan Alfiyan, Alfiyan Ali Sofyan Kholimi Amirullah Amirullah Amrul Faruq Ananto Mukti Wibowo Andi Setiawan Andreas Agung Kristanto, Andreas Agung Ardyono Pribadi Ardyono Priyadi Ardyono Priyadi Arham Arham, Arham Arif Muntasa Arifin Arifin Arik Kurniawati Aris Nasuha Aris Widayati Arman Jaya Arraziqi, Dwi Aryo Nugroho Atris Suyantohadi Atris Suyantohadi Atyanta Nika Rumaksari Atyanta. N. Rumaksari Bambang Purwahyudi Bambang Sujanarko Bambang Suprianto . Bandung Arry Sanjoyo Basuki, Setio Berlian Al Kindhi Bernaridho Hutabarat, Bernaridho Budi Setiyono Budiarti, Rizqi Putri Nourma Cahyadi, Billy Kelvianto Chastine Fatichah Choirina, Priska Darma Setiawan Putra Dedid Cahya Happyanto Dewi Nurdiyah Diah Puspito Wulandari Diana Purwitasari Djoko Purwanto Dwi F. Suyatno Eddy Satriyanto Effendy Hadi Sutanto Eka Dwi Nurcahya Eko M. Yuniarno Eko Mulyanto Eko Mulyanto Yuniarno Eko Mulyanto Yuniarno Elly Purwanti Endang Setyati Endang Sri Rahayu Endi Permata Era Purwanto Esther Irawati Setiawan Evi Septiana Pane Evi Septiana Pane, Evi Septiana F.X. Ferdinandus Fahmi Amiq Fanani, Nurul Zainal Farah Zakiyah Rahmanti Fath, Nifty Feby Artwodini Muqtadiroh Fendik Eko P Fujisawa, Kimiya Gigih Prabowo Glanny M.Christiaan Mangindaan Gregorius Satio Budhi Gunawan Gunawan Gunawan Gunawan H. Hammad, Jehad A. Hans Juwiantho Hardianto Wibowo Hasti Afianti Hendra Kusuma Hermawan, Norma Herti Miawarni Hidayatillah, Rumaisah Hindarto Husna, Farida Amila Hutama Harsono, Nathanael I Ketut Eddy Purnama I Ketut Edy Purnama I Made Gede Sunarya I Made Ginarsa I Nyoman Budiastra Ima Kurniastuti Imam Robandi Iman Fahruzi Indah Agustien Sirajudin Indar Sugiarto Ingrid Nurtanio Isa Hafidz Iwan Setiawan Jehad A. H. Hammad Joan Santoso Joko Pitono Joko Priambodo Juanita, Safitri Ketut Eddy Purnama Khairuddin Karim Khamid Khamid Khamid Khamid Kristian, Yosi Lailatul Husniah Laksana, Eka Purwa Lie Jasa Lilik Anifah Lukman Zaman Lystianingrum, Vita Makoto Chiba Margareta Rinastiti Margo Pujiantara Marselin Jamlaay Marsetio Pramono Meidhy Panginda Saputra Moch Hariadi Moch. Hariadi Moch. Iskandar Riansyah Mochamad Ashari Mochamad Hariadi Mochammad Facta Mochammad Hariadi Moh. Aries Syufagi Mohammad Arie Reza Muhamad Ashari Muhamad Haddin Muhammad Nur Alamsyah Muhammad Reza Pahlawan Muhammad Rivai Muhtadin Mukhammad Aris Muldi Yuhendri Mulyanto, Edy Nazarrudin, Ahmad Ricky Nova Eka Budiyanta Nova Rijati Nugroho, Supeno Nugroho, Supeno Mardi S. Nur Kasan, Nur Nurul Fadillah Nurul Zainal Fanani Oddy Virgantara Putra Ontoseno Penangsang Pratama, Afis Asryullah Priambodo, Joko Prima Kristalina Purnawan, I Ketut Adi Purwadi Agus Darwito Putra Wisnu AS R Dimas Adityo Rachmad Setiawan Radi Radi Rafly Azmi Ulya, Amik Rahmat Rahmat Rahmat Syam Raihan, Muhammad Ratna Ika Putri Rika Rokhana Rima Tri Wahyuningrum Rima Tri Wahyuningrum Riris Diana Rachmayanti Rokhana, Rika Rumaisah Hidayatillah Ruri Suko Basuki Rusmono Yulianto Saidah Saidah Saputra, Daniel Gamaliel Sartana, Bruri Trya SATO Yukihiko Setiawan, Esther Setijadi, Eko Sidharta, Bayu Adjie Sihombing, Drigo Alexander Sirait, Rummi Santi Rama Siti Rochimah Soebagio Soebagio Soebagio Soebagio Soebagio Soebagio Soebagio Soebagio Soetiono, Agung Dewa Bagus Subagio subagio Subuh Isnur Haryudo Sugiyanto - Sujono Sujono Sujono Sulistyono, Marcelinus Yosep Teguh Sumadi, Fauzi Dwi Setiawan Supeno M. S. Nugroho Supeno Mardi Supeno Mardi S. Nugroho Supeno Mardi Susiki Nugroho, Supeno Mardi Surya Sumpeno Sutedjo Sutedjo Syafaah, Lailis Syaiful Imron Tita Karlita Tita Karlita Tri Arief Sardjono Tsuyoshi Usagawa, Tsuyoshi Ulla Delfana Rosiani Umar Umar Vita Lystianingrum Widodo Budiharto Wijayanti . Wiratmoko Yuwono Wiwik Anggraeni Wridhasari Hayuningtyas Yani Prabowo Yodik Iwan Herlambang Yosi Kristian Yoyon Kusnendar Suprapto Yuhana, Umi Laili Yulianto Tejo Putranto Yuni Yamasari Yuniarno, Eko M. Yusron rijal Zaimah Permatasari Zaman, Lukman