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All Journal Riau Journal of Computer Science Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JITK (Jurnal Ilmu Pengetahuan dan Komputer) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Journal of Computer Networks, Architecture and High Performance Computing Journal of Artificial Intelligence and Engineering Applications (JAIEA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Mardi Turnip, Mardi
Universitas Prima Indonesia
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media

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ECG-BASED ARRHYTHMIA DETECTION USING THE NARROW NEURAL NETWORK CLASSIFIER Chandra, Angelia Ayu; Sunnia, Cecilia; Wijaya, Kenrick Alvaro; Dharma, Abdi; Turnip, Arjon; Turnip, Mardi
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
APPLICATION OF RANDOM FOREST ALGORITHM FOR ARRHYTHMIA DETECTION BASED ON ELECTROCARDIOGRAM DATA Situmorang, Fransido; William, David; Patterson, Jennifer; Ardila, Niki; Turnip, Mardi
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.v11i2.7136

Abstract

Arrhythmia is a common cardiac disorder that requires early detection to prevent serious complications. This study applied the Random Forest algorithm to enhance electrocardiogram (ECG) analysis and enable accurate arrhythmia classification. Unlike prior studies that focused primarily on resting ECG signals, this research incorporated dynamic data collected from 26 participants performing three physical activities for three minutes each, capturing physiological variations across multiple activity states. The Random Forest model was constructed and evaluated using ECG-derived temporal and morphological features to detect potential arrhythmias. Experimental results showed that the model achieved an accuracy of 97.4%, with precision, recall, and F1-score each reaching 98%, and an AUC of 0.97. However, several limitations remain, including the relatively small and homogeneous sample, as well as the short recording duration. Nonetheless, the proposed approach demonstrates strong potential to support early cardiac screening and real-time monitoring, particularly in portable and resource-limited healthcare applications
Co-Authors -, aditya perdana -, Evta Indra -, Ruben Abdi Dharma Ade Irma Suryani Aditya Perdana aditya perdana - ADVENT TORAS MARBUN Albert Sagala, Albert Amri , Ahmad Alfauzan Ananda, Debby Andreas Theo Pilus Alista Teles Siahaan Ardila, Niki Arjon Turnip Astri Milleniar Marbun Banjarnahor, Jepri Bolon, Debby Novriyanti Br Tp. Bunawolo, Methina Cahyadi, Andika Carissa, Joan Stacia Chandra, Angelia Ayu Cindy Cynthia Dafa', Mu'ammar Debby Novriyanti Br Tp.Bolon Dedy Ristanto Hulu Delima Sitanggang, Delima Denny Irvan Sinuhaji Ester Ayu S. Marpaung Evta Indra Felix Widarko Hulu, Dedy Ristanto Hulu, Yosefa Intan Susanti Simarmata Joan Stacia Carissa Johan Libby JOICE ANGELINA PURBA JURMIDA PULUNGAN Kelvin M. Arif Almahdi Manao, Sonatafati MARBUN, ADVENT TORAS Marlince N.K Nababan Nababan, Marlince N.K Ndruru, Jonathan Haris P. Oktarino, Ade Owen Owen Panjaitan, Haposan Daniel Patterson, Jennifer Perangin-angin, Despaleri Priambodo, Ganang Reza PULUNGAN, JURMIDA PURBA, JOICE ANGELINA Roshan, Rohit Salmiati Salsabillah Saragi, Yosua Morales Saut Parsaoran Tamba Sigalingging, Josepta Sihaloho, Theresia Delima Simbolon, Naftalia Sinuhaji, Denny Irvan Sitanggang, Wahyu Adventus Andreas Siti Aisyah Sitompul, Daniel Ryan Hamonangan Sitorus, Dedi Setiadi Situmorang, Andreas Situmorang, Fransido Solly Aryza Sonia Novel Lase Sukhbir Singh Sunnia, Cecilia Tarigan, Julio Putra Tarigan, Richard Fernando Timi Tampubolon Venta Br.Tarigan, Emma Wijaya, Benny Wijaya, Kenrick Alvaro William, David Winarti Pasaribu Wong, Yano Sabar M Yenny Yenny Yoga Tri Nugraha