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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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Estimator Parameter Tegangan Jaringan Tiga Fasa Berbasis D-SOGI PLL Iwan Setiawan; Mochammad Facta; Ardyono Priyadi; Mauridhi Hery Purnomo
Jurnal Teknologi Elektro Vol 16 No 2 (2017): (May - Agustus) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2017.v16i02p15

Abstract

Phase locked loop (PLL) adalah sebuah sistem umpan balik yang memegang peran penting dalam sistem-sistem konverter terkoneksi jaringan listrik. Fungsi utama PLL adalah mendapatkan beragam informasi parameter jaringan yaitu seperti phase dan magnitude tegangan. Informasi-informasi tersebut selanjutnya digunakan sebagai dasar proses sinkronisasi peralatan dengan jaringan listrik. Tujuan utama paper ini adalah memodelkan sekaligus membandingkan unjuk kerja salah satu jenis PLL yang dikenal dengan nama Dual Second Order Generalized Integrator Phase-Locked Loop dengan SRF-PLL yaitu sebuah PLL yang relatif standar. Berdasarkan hasil simulasi, unjuk kerja D-SOGI PLL dalam keadaan tunaknya lebih unggul dibandingkan SRF-PLL terutama untuk kondisi jaringan listrik tiga phase tidak seimbang.
The Significance of Dynamic COVID-19 Dashboard in Formulating School Reopening Strategies Muqtadiroh, Feby Artwodini; Yuniarno, Eko Mulyanto; Nugroho, Supeno Mardi Susiki; Pahlawan, Muhammad Reza; Rachmayanti, Riris Diana; Usagawa, Tsuyoshi; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.76017

Abstract

Experiments conducted with the COVID-19 dataset have predominantly concentrated on predicting cases fluctuating and classifying lung-related diseases. Nevertheless, the consequences of the COVID-19 pandemic have also spread to the education sector. To safeguard educational stability in response to the remote learning policy, we leverage authentic COVID-19 datasets alongside school information across 154 sub-areas in Surabaya City, Indonesia. Our focus is predicting the dynamic within these sub-areas where schools are located. The outcomes of this study, by incorporating the recurrent neural network of long- and short-term memory (RNN-LSTM) architecture and refined hyperparameters, effectively enhanced the predictive model's performance. The findings are showcased on a dashboard, visually representing the transmission of COVID-19 in schools across each sub-area. This information serves as a basis for informed decisions on the safe reopening of schools, aiming to mitigate the decline in education quality during the challenging pandemic.
Adaptive Threshold Filtering to Reduce Noise in Elderly Activity Classification Using Bi-LSTM Rahayu, Endang Sri; Yuniarno, Eko Mulyanto; Purnama, I Ketut Eddy; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.76064

Abstract

As the global population ages, there is an increasing need to provide better care and support for older individuals. Deep learning support to accurately predict elderly activities is very important to develop. This research discusses a new model integrating filtering techniques using adaptive thresholds with Bidirectional - Long Short-Term Memory (Bi-LSTM) networks. The problem of activity prediction accuracy, mainly due to noise or irrational measurements in the dataset, is solved with adaptive thresholds. Adaptive characteristics at the threshold are needed because each individual has different activity patterns. Experiments using the HAR70+ dataset describe the activity patterns of 15 elderly subjects and the gesture patterns of 7 activities. Based on body movement patterns, the elderly can be classified as using walking aids. The proposed model design obtains an accuracy of 94.71% with a loss of 0.1984.
Parallel Hybrid Particle Swarm-Grey Wolf Algorithms for Optimal Load-Shedding in An Isolated Network Sujono; Priyadi, Ardyono; Pujiantara, Margo; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.76093

Abstract

In distribution networks integrated with distributed generation (DG), disconnection from the main grid reduces the power supply significantly. The power imbalance between DG generation and load degrades network stability. This paper proposes a hybrid parallel Particle Swarm Optimization - Grey Wolf Optimizer (PSGWO) algorithm for load shedding optimization. This optimization aims to reduce the DG power not absorbed by the remaining loads and maintain the voltage within the specified limits. The performance of PSGWO is tested on an IEEE 33 bus radial distribution system, considering loading levels of 80% to 140% of the baseload. At a 100% loading level, PSGWO showed the best performance, with a load shedding of 2.2297 MW and a voltage deviation of 0.0049. These values are the smallest compared to the results of the standard PSO and GWO algorithms. The PSGWO algorithm remains superior and converges faster than standard PSO and GWO at all loading levels.
An Improved Utility-Based Artificial Intelligence to Capture NPC Behaviour in Fighting Games Using Genetic Algorithm Nugroho, Supeno; Affan, Lazuardi Yaqub; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.82040

Abstract

In computer fighting games , the ability of players to play with Non-Player Characters (NPC) is essential. A poorly designed NPC causes poor player engagement due to predictable behaviour, thus leads to unsatisfactory playing experience. We propose utility-based AI selected by genetic algorithm to determine the utility functions of each NPC action. We applied ELO ratings (usually used in chess game) to determine fitness function. Utility-based artificial intelligence can deliver human-like NPC with varied decision-making and can employ many forms of function to calculate the AI utility value. Tests on chromosomes in each generation were also carried out to obtain different responses. The Pearson Correlation coefficient is used to obtain an analysis of the influence of each assessment variable. The simulation results verify the validity of our analysis and show that our scheme influences the satisfaction level of game users
Smart Home for Supporting Elderly Based On Ultrawideband Positioning System Muhtadin; Nazarrudin, Ahmad Ricky; Purnama, I Ketut Eddy; Fatichah, Chastine; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.84186

Abstract

In 2017, the rate of dependency among the elderly was reported to be at 13.28%, which was problematic, due to the limited number of caregivers to assist them at all times. To address this issue, a robotic service and vital sign-based system were developed, but it was found to be insufficient for monitoring the activities of the elderly. Therefore, this study aimed to address the high dependency rates of elderly individuals who required constant support and care to survive by designing an ultrawideband-based positioning system. The system consisted of five sub-systems, including an indoor positioning system, a database system, a data processing system, an actuator system, and an application user interface. The system testing phase revealed several important findings, including that the position coordinates of the elderly were accurately read with differences of only 98.884 mm and 279.94 under Line of Sight and Non-Line of Sight conditions, respectively. Furthermore, the initial error rate of 164.39% was successfully reduced to only 1.096% by applying the average filter method in the data processing system. The actuator system also showed an impressive accuracy rate of 98% success, while the Android-based application user interface received a high user experience rate of 92.3%. Overall, these findings suggested that the ultrawideband-based positioning system had significant potential to support smart homes for the elderly and improve their quality of life.
Early Detection Depression Based On Action Unit and Eye Gaze Features Using a Multi-Input CNN-WoPL Framework Sugiyanto, Sugiyanto; Purnama, I Ketut Eddy; Yuniarno, Eko Mulyanto; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.84674

Abstract

Depression is a common mental disorder with significant life impact, including a high risk of suicide. Patients with depression attempt suicide five times more often than the general population. Self-reporting, subjective judgement and clinician expertise influence conventional diagnostic methods. For timely intervention and effective treatment, early and accurate diagnosis of depression is essential. This study proposes a framework called Multi-Input CNN-WoPL, a CNN-based method without a pooling layer that combines two features - action units and gaze - to improve accuracy and robustness in automatic depression detection. Pooling layer reduces spatial dimension of feature map, resulting in loss of information related to expression data, affecting depression detection result. The performance of the proposed method results in an accuracy of 0.994 and F1 score = 0.993, the F1 score value close to 1.0 indicates that the proposed method has good precision, recall and performance.
Correlation Analysis Approach Between Features and Motor Movement Stimulus for Stroke Severity Classification of EEG Signal Based on Time Domain, Frequency Domain, and Signal Decomposition Domain Sulistyono, Marcelinus Yosep Teguh; Pane, Evi Septiana; Yuniarno, Eko Mulyanto; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.85550

Abstract

The healing process of a stroke necessitates tools for measuring relevant parameters to facilitate monitoring, evaluation, and medical rehabilitation. Accurate parameter measures can be observed in stroke patients' severity to ascertain suitable interventions by identifying components pertinent to monitoring, evaluation, and medical rehabilitation. The components are derived from the observation collection process utilizing an EEG device, accompanied by a motor stimulus, to ensure the acquisition of EEG signals for monitoring, evaluation, and medical rehabilitation while preventing any loss of information during data collection. The acquired information encounters challenges due to the signal's unstable, nonlinear, and non-stationary characteristics, necessitating efforts to stabilize, render stationary, and linearize it through suitable signal processing and feature extraction techniques to achieve a pertinent feature composition. The subsequent difficulty is achieving the objectives of medical monitoring, evaluation, and rehabilitation, necessitating the correlation between EEG signal characteristics and motor movement stimuli, ensuring that the process adheres to appropriate parameter identification and scheduling per the established plan. In response to this difficulty, a correlation analysis methodology is established, incorporating normalcy tests, significance tests, and correlation analysis to ensure that the relevant factors for identifying stroke severity categorization patterns are precisely identified beforehand. The correlation analysis strategy employs raw data situations, preprocessing, feature extraction, feature selection, and correlation analysis for classification purposes. Our experimental findings indicate that the correlation analysis approach for assessing stroke severity classification patterns is evident in the Hajorth Complexity feature, utilizing the Shoulder motor movement stimulus and the SVM classification type, achieving an accuracy significant value of 98%. These findings confirm the efficacy of correlation analysis between EEG signal features and motor movement stimuli in identifying the optimal parameters within a reduced dimensional space to assess stroke severity effectively.
Optimizing Diabetic Neuropathy Severity Classification Using Electromyography Signals Through Synthetic Oversampling Techniques Purnawan, I Ketut Adi; Wibawa, Adhi Dharma; Kurniawati, Arik; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.85675

Abstract

Electromyography signals are electrical signals generated by muscle activity and are very useful for analyzing the health conditions of muscles and nerves. Data imbalance is a prevalent issue in EMG signal data, especially when addressing patients with varied health conditions and restricted data availability. A major difficulty for machine learning models is class imbalance in datasets, which frequently leads to biased predictions favoring the dominant class and neglecting the minority classes. The data augmentation method employs the Synthetic Minority Over Sampling Technique (SMOTE) and Random Over Sampling (ROS) to address data imbalances and enhance the performance of classification models for underrepresented classes. This study employs an oversampling technique to enhance the efficacy of the XG Boost model. SMOTE exhibits better efficacy relative to competing methods; the application of appropriate oversampling techniques allows models to integrate patterns from both majority and often neglected minority data.
Multimodel Prediction Score Based on Academic Procrastination Behavior in E-Learning Sartana, Bruri Trya; Nugroho, Supeno Mardi Susiki; Yuhana, Umi Laili; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.85880

Abstract

This research investigates the impact of academic procrastination on student performance in online learning environments and explores a multimodel approach for grade prediction. Academic procrastination is a well-documented issue that negatively affects learning outcomes, often leading to lower academic performance and increased dropout rates in self-paced learning platforms. This study analyzes behavioral data from 377 students, extracted from Moodle activity logs, which record real-time student interactions with learning materials. To address the gap in understanding procrastination patterns through activity logs, key procrastination-related features were derived from timestamps of task access, submission, and engagement duration. Using K-Means clustering with the Elbow method, students were categorized into three procrastination clusters: low procrastination with high academic performance, high procrastination with low performance, and moderate procrastination with average performance. Seven machine learning models were evaluated for predicting student grades, with Random Forest (RF) achieving the highest accuracy (R² = 0.812, MAE = 6.248, RMSE = 8.456). These findings highlight the potential of using activity logs to analyze procrastination patterns and predict student performance, allowing educators to develop early intervention strategies that support at-risk students and improve learning outcomes.
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