Article Per Year (5 Year)
p-Index From 2021 - 2026
4.133
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics 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 Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

Published : 35 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 35 Documents
Search

 1   2   3   4 
Analysis of brain activity to methamphetamine stimulus using electroencephalography technology with Naive Bayes algorithm Putri, Suci Rahmalia; Hasibuan, Amanda Khalishah; Sinaga, Cindy Ananda; Manullang, Ernest Natanael; Turnip, Arjon; Dharma, Abdi; Turnip, Mardi
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.
APPLICATION OF RANDOM FOREST ALGORITHM FOR ARRHYTHMIA DETECTION BASED ON ELECTROCARDIOGRAM DATA Fransido Situmorang; David William; Jennifer Patterson; Niki Ardila; 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.v11i2.7136

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
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.
Identifikasi Tingkat Kematangan Buah pada Tanaman Kelapa Sawit Menggunakan Algoritma Convolutional Neural Network dan Pendekatan Deep Learning William Owen Wijaya; Dhanny Rukmana Manday; Agrifa Insani Napitupulu; Mardi Turnip; Saroha Manurung
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).pp232-240

Abstract

Palm oil quality is largely determined by the free fatty acid (FFA) content, which is influenced by the ripeness of the fruit. Traditionally, determining the ripeness level of palm oil fruit relies on visual inspection by experts, which is time-consuming and dependent on individual skill. To address this, a system has been developed using the Convolutional Neural Network (CNN) method to automate the ripeness classification process. This study focuses on classifying palm oil fruit into three categories: ripe, unripe, and overripe, using a dataset of 1,380 images with 460 images per class. The dataset was split into 80% training data and 20% validation data. The CNN architecture employed was MobileNetV2, known for its simplicity and low computational complexity. Images were resized to 224 x 224 pixels, and two optimizers—Adam and RMSProp—were compared with learning rates of 0.001 and 0.0001 over 30 epochs. The best results were achieved using the Adam optimizer with a learning rate of 0.001, yielding a training accuracy of 91% and a test accuracy of 87%. This shows promising potential for automated palm oil fruit ripeness detection.
Analysis of arrhythmia detection and classification using electrocardiogram signals with decision tree algorithm Marpaung, Putri Juniarti; Anggreni Matondang, Nia; Margaretta Siregar, Rince; Ester Novelia Siahaan, Angelica; Dharma, Abdi; Turnip, Arjon; Turnip, Mardi
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 -, aditya perdana -, Evta Indra -, Ruben Abdi Dharma Abdi Dharma Ade Irma Suryani Aditya Perdana aditya perdana - ADVENT TORAS MARBUN Agrifa Insani Napitupulu Albert Sagala, Albert Amri , Ahmad Alfauzan Ananda, Debby Andreas Theo Pilus Alista Teles Siahaan Anggreni Matondang, Nia 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 David William Debby Novriyanti Br Tp.Bolon Dedy Ristanto Hulu Delima Sitanggang, Delima Denny Irvan Sinuhaji Dhanny Rukmana Manday Ester Ayu S. Marpaung Ester Novelia Siahaan, Angelica Evta Indra Felix Widarko Fransido Situmorang Hasibuan, Amanda Khalishah Hulu, Dedy Ristanto Hulu, Yosefa Intan Susanti Simarmata Jennifer Patterson Joan Stacia Carissa Johan Libby JOICE ANGELINA PURBA Julio Putra Tarigan JURMIDA PULUNGAN Kelvin M. Arif Almahdi Manao, Sonatafati Manullang, Ernest Natanael MARBUN, ADVENT TORAS Margaretta Siregar, Rince Marlince N.K Nababan Marpaung, Putri Juniarti Nababan, Marlince N.K Ndruru, Jonathan Haris P. Niki Ardila Oktarino, Ade Owen Owen Panjaitan, Haposan Daniel Patterson, Jennifer Perangin-angin, Despaleri Priambodo, Ganang Reza PULUNGAN, JURMIDA PURBA, JOICE ANGELINA Putri, Suci Rahmalia Roshan, Rohit Salmiati Salsabillah Saragi, Yosua Morales Saroha Manurung Saut Parsaoran Tamba Sigalingging, Josepta Sihaloho, Theresia Delima Simbolon, Naftalia Sinaga, Cindy Ananda Sinuhaji, Denny Irvan Sitanggang, Wahyu Adventus Andreas Siti Aisyah 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 Owen Wijaya William, David Winarti Pasaribu Wong, Yano Sabar M Yenny Yenny Yoga Tri Nugraha Yosua Morales Saragi