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All Journal Bulletin of Electrical Engineering and Informatics Riau Journal of Computer Science JITK (Jurnal Ilmu Pengetahuan dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Mantik Jurnal Informatika dan Rekayasa Perangkat Lunak REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Abdi Dharma
Universitas Prima Indonesia
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences

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ECG-BASED ARRHYTHMIA DETECTION USING THE NARROW NEURAL NETWORK CLASSIFIER Angelia Ayu Chandra; Cecilia Sunnia; Kenrick Alvaro Wijaya; Abdi Dharma; Arjon Turnip; Mardi Turnip
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7121

Abstract

Electrocardiograms (ECG) are important for detecting arrhythmias. Conventional models such as CNN and LSTM are accurate but require large amounts of computation, making them difficult to use on wearable devices and for real-time monitoring. This study evaluates the Narrow Neural Network Classifier (NNNC) as a lightweight and efficient alternative. The dataset consists of 21 subjects with 881 ECG samples, categorized based on walking, sitting, and running activities, and processed through bandpass filtering, normalization, and P-QRS- T wave segmentation. The data is divided into training (70%), validation (15%), and test (15%) sets. The NNNC has 11 convolutional layers, a ReLU activation function, a Softmax output, and 120,000 parameters. The model was trained using the Adam optimizer, a batch size of 32, and a learning rate of 0.001 for 100 epochs and compared with SVM, CNN, and LSTM using accuracy, precision, recall, F1-score, and ROC-AUC. The results show that NNNC achieves an accuracy of 98.9%, a precision of 99.2%, a recall of 99.2%, and an F1-score of 99.2%, higher than SVM and comparable to CNN/LSTM, with lower computational consumption. The model is capable of reliably detecting early arrhythmias. These findings support the potential of NNNC for ECG-based automatic diagnostic systems, including real-time implementation on wearable devices, although further research is needed for large-scale validation
Pengembangan Sistem Informasi Manajemen Penjualan Aksesoris Motor Berbasis Web Julio Putra Tarigan; Abdi Dharma; Siti Aisyah; Delima Sitanggang; Yosua Morales Saragi; Mardi Turnip
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp216-219

Abstract

In this era of globalization, computerized systems have been used by many parties, both agencies, organizations and educational institutions. A computerized system is an information system that will design transaction data into useful information and aims to help make efficient decisions. However, at this time, PT. Surya Mandiri Motor does not yet use a computerized system, so errors often occur in recording, calculating transaction data, there are difficulties in searching for data and difficulties in making reports. This sales information system can be a solution that can simplify data processing so that the sales transaction process will be faster, more precise and accurate. This system was built using the PHP programming language and MySQL database.
Analysis of brain activity to methamphetamine stimulus using electroencephalography technology with Naive Bayes algorithm Suci Rahmalia Putri; Amanda Khalishah Hasibuan; Cindy Ananda Sinaga; Ernest Natanael Manullang; Arjon Turnip; Abdi Dharma; Mardi Turnip
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10385

Abstract

The increasing use of methamphetamine among young generations has led to significant alterations in brain function, affecting both behavior and mental health. However, scientific understanding of the neural activity changes induced by methamphetamine remains limited. This study aims to analyze brainwave patterns using electroencephalography (EEG) and classify addiction response levels through the Naive Bayes algorithm. The experimental procedure involved presenting each subject with visual stimuli related to methamphetamine while recording their brain activity using EEG for three minutes. The extracted EEG features were then analyzed with the Naive Bayes classifier. The results demonstrated a classification accuracy of 97.9%. The proposed method successfully categorized brain activity patterns into five levels of response: non-addicted, mildly addicted, moderately active, addicted, and highly addicted. These findings indicate that the Naive Bayes algorithm is effective in distinguishing subtle variations in brainwave patterns associated with different levels of methamphetamine addiction response.
Analysis of arrhythmia detection and classification using electrocardiogram signals with decision tree algorithm Putri Juniarti Marpaung; Nia Anggreni Matondang; Rince Margaretta Siregar; Angelica Ester Novelia Siahaan; Abdi Dharma; Arjon Turnip; Mardi Turnip
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10317

Abstract

Heart disease remains the primary cause of death globally, with arrhythmia diagnosis often limited by restricted access to medical personnel and the complexity of electrocardiogram (ECG) interpretation. Accurate arrhythmia classification is essential to prevent cardiovascular complications. The proposed method successfully categorized classify ECG signals into five categories: normal, abnormal, potentially arrhythmia, moderate arrhythmia risk, and highly potentially arrhythmia. Data were collected from 30 subjects under three activity scenarios: sitting, walking, and running. The proposed model achieved an accuracy of 99.4%, demonstrating strong potential for real-time monitoring applications. Performance evaluation was conducted using accuracy, precision, recall, and F1-score for each class. Although the dataset size remains relatively small, the findings highlight the effectiveness of decision tree as an efficient and interpretable classification method. Future research will involve validation using large-scale public databases like the arrhythmia database at MIT-BIH and comparisons with advanced methods including convolutional neural network (CNN), transformer-based models, and explainable artificial intelligent (XAI) frameworks.
Co-Authors A. Henpra Yogi Sitanggang Aditya Calderon Naibaho Alhoi Andrew Jefferson Alvin Alvin Amanda Khalishah Hasibuan Andika Dian Permana Andrian Angelia Ayu Chandra Angelica Ester Novelia Siahaan Arjon Turnip Arjon Turnip Bodhi Lhoardi Cecilia Sunnia Christensen Christensen Cindy Ananda Sinaga Darwis Darwis Delima Sitanggang, Delima Dinda Afrianti Elvandric Lase Endy Pranoto Eric Wibowo Ernest Natanael Manullang FAHRUL ROZY Fangatulo Dodo Telaumbanua Felix Elbert Gani Frenky Lutan Frenky Tanoto Handoko Handoko Hartato Bindan Hendra Handoko Syahputra Pasaribu, Hendra Handoko Johanes Pardede Joseph Siahaan Joshua Baen Tupa Julio Putra Tarigan Kenrick Alvaro Wijaya Lolo Mulatua Bancin Lymin Lymin Magdalena Magdalena Mardi Turnip Mardi Turnip, Mardi Michael Michael Muhammad Uwais Akbar Nia Anggreni Matondang Peringatan Hulu Putri Juniarti Marpaung Rikky Romeo Lumbantong Rince Margaretta Siregar Salim, Edward Suwandy Sibarani, Lelawati Siti Aisyah Steele Suci Rahmalia Putri Sumita Wardani Togar Zulfiter Nadeak Wonderson Wonderson Yosua Morales Saragi