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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

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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 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.
Application of the ANFIS Method to Predict Satisfaction with Facilities and Infrastructure Mardi Turnip; Ganang Reza Priambodo; Theresia Delima Sihaloho; Jonathan Haris P. Ndruru; Josepta Sigalingging; Salsabillah; Haposan Daniel Panjaitan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.283

Abstract

Facilities and infrastructure are all movable or immovable objects or objects that are used to support every aspect of human life. Students, lecturers and office workers at least spend about half of their active hours at work. Therefore it is very important to pay attention to the high level of comfort, security, completeness in a building. There fore we need a way to predict satisfaction with facilities and infrastructure. To provide solutions to existing problems, the authors create applications that can predict the satisfaction of facilities and infrastructure. In this article, a satisfaction prediction approach based on a data-driven technique, representing system behavior using the Takagi-Sugeno model is developed. The Adaptive Neuro Fuzzy Inference System method is used to build a predictive model. The research was conducted by interview, observation and literature study. Data were taken from 92 respondents consisting of lecturers, students, and staff/employees in the research area. The test results using this method showed satisfactory results, indicating a success rate with an accuracy of 97.2%.
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 Amanda Khalishah Hasibuan Amri , Ahmad Alfauzan Ananda, Debby Andreas Theo Pilus Alista Teles Siahaan Angelica Ester Novelia Siahaan Ardila, Niki Arjon Turnip Arjon Turnip Arjon Turnip Astri Milleniar Marbun Banjarnahor, Jepri Benny Wijaya Bolon, Debby Novriyanti Br Tp. Bunawolo, Methina Cahyadi, Andika Carissa, Joan Stacia Chandra, Angelia Ayu Cindy Ananda Sinaga Cindy Cynthia David William Debby Novriyanti Br Tp.Bolon Dedy Ristanto Hulu Delima Sitanggang, Delima Denny Irvan Sinuhaji Dhanny Rukmana Manday Ernest Natanael Manullang Ester Ayu S. Marpaung Evta Indra Felix Widarko Fransido Situmorang Ganang Reza Priambodo Haposan Daniel Panjaitan Hulu, Dedy Ristanto Hulu, Yosefa Intan Susanti Simarmata Jennifer Patterson Joan Stacia Carissa Johan Libby JOICE ANGELINA PURBA Jonathan Haris P. Ndruru Josepta Sigalingging Julio Putra Tarigan JURMIDA PULUNGAN Kelvin M. Arif Almahdi Manao, Sonatafati MARBUN, ADVENT TORAS Marlince N.K Nababan Nababan, Marlince N.K Nia Anggreni Matondang Niki Ardila Oktarino, Ade Owen Owen Patterson, Jennifer Perangin-angin, Despaleri PULUNGAN, JURMIDA PURBA, JOICE ANGELINA Putri Juniarti Marpaung Rince Margaretta Siregar Roshan, Rohit Salmiati Salsabillah Saragi, Yosua Morales Saroha Manurung Saut Parsaoran Tamba Simbolon, Naftalia Sinuhaji, Denny Irvan Sitanggang, Wahyu Adventus Andreas Siti Aisyah Siti Aisyah Sitompul, Daniel Ryan Hamonangan Sitorus, Dedi Setiadi Situmorang, Andreas Situmorang, Fransido Sonia Novel Lase Suci Rahmalia Putri Sukhbir Singh Sunnia, Cecilia Tarigan, Julio Putra Tarigan, Richard Fernando Theresia Delima Sihaloho Timi Tampubolon Venta Br.Tarigan, Emma Wijaya, Kenrick Alvaro William Owen Wijaya William, David Winarti Pasaribu Wong, Yano Sabar M Yenny Yenny Yoga Tri Nugraha Yosua Morales Saragi