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All Journal Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Ilmiah Computer Science International Journal of Artificial Intelligence in Medical Issues
M Ikbal Siami
Institut Teknologi Dan Bisnis STIKOM Ambon
Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Public Health

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Penerapan VIKOR Method (VIšekriterijumsko KOmpromisno Rangiranje Method) Dalam Rekomendasi Pemilihan Laptop Gaming Merriam Modeong; M. Ikbal Siami
Jurnal Ilmiah Computer Science Vol. 1 No. 2 (2023): Volume 1 Number 2 January 2023
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v1i2.6

Abstract

The VIKOR method (VIšekriterijumsko KOmpromisno Rangiranje Method) is one of the techniques in the field of multi-criteria analysis used to make decisions in situations where there are several criteria that must be considered simultaneously. This method helps in decision making when there are many alternatives that must be evaluated based on several different criteria. The research conducted aims to provide recommendations to users in the selection of gaming laptops by applying a decision support system model using the VIšekriterijumsko KOmpromisno Rangiranje Method (VIKOR) method so that it becomes an input in the selection of gaming laptops. The final calculation results in the VIKOR method provide recommendations for Rank 1, namely Predator Helios 18 Laptop, Rank 2, namely ASUS ROG Zephyrus G14 Laptop, Rank 3, ASUS ROG Strix SCAR 18 Laptop, and Rank 4, Nitro 5 Laptop. The results of application testing using the black box testing method obtained a percentage result of 100% in accordance with the functions that have been created
A Comparative Study of Machine Learning Models for Stress Level Classification Using Social Media and Lifestyle Data M. Ikbal Siami; Aris Wahyu Murdiyanto; Sumiyatun
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v4i1.437

Abstract

The increasing use of social media and digital platforms has raised concerns regarding its potential relationship with sleep patterns, lifestyle behaviors, productivity, and psychological well-being. Stress is a common health-related issue that may be influenced by daily behavioral patterns, including screen time, social media usage, sleep duration, physical activity, and work or study habits. This study aims to develop and evaluate machine learning models for predicting stress levels based on non-invasive digital behavior and lifestyle indicators. The dataset used in this study consisted of 11,000 records with three stress level categories: Low, Medium, and High. The predictor variables included age, daily screen time, social media usage duration, sleep hours, exercise duration, study or work hours, productivity score, and the most frequently used social media platform. Several machine learning algorithms were evaluated, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Gradient Boosting. Model performance was assessed using accuracy, precision, recall, F1-score, confusion matrix analysis, and 5-fold stratified cross-validation. The experimental results showed that the overall classification performance was modest. The Decision Tree model achieved the best testing performance with an accuracy and macro F1-score of 0.3400, while Gradient Boosting achieved the highest cross-validation performance with a mean accuracy of 0.3480 and a mean macro F1-score of 0.3467. Feature importance analysis using Random Forest indicated that productivity score, sleep hours, study or work hours, social media hours, and daily screen time were the most influential variables. These findings suggest that digital behavior and lifestyle indicators may provide useful exploratory insights for stress-related analysis, although their predictive power remains limited. Therefore, the proposed approach is more suitable as an exploratory digital well-being assessment framework rather than a clinical diagnostic tool.
ANALISIS DAN OPTIMASI PERFORMA MODEL YOLOV8 DALAM MENDETEKSI DAN MENGHITUNG POPULASI IKAN DI DALAM AIR M. Ikbal Siami; Lukman Saleh
Simtek : jurnal sistem informasi dan teknik komputer Vol. 11 No. 2 (2026): Oktober 2026
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v11i2.1821

Abstract

Manual monitoring of fish populations in floating net cages is time-consuming and prone to producing inconsistent data. This study evaluated the performance of YOLOv8n, a lightweight variant of YOLOv8, for automated fish detection and counting in floating net cages in Ambon, Indonesia. A total of 780 underwater images, obtained from publicly available datasets on Kaggle and field data collection, were annotated and preprocessed through resizing, contrast enhancement using contrast-limited adaptive histogram equalization (CLAHE), and grayscale conversion. Two models were trained and compared: Model 1 without data augmentation and Model 2 with horizontal flipping and rotation. Model 1 achieved a more balanced performance, with an mAP@0.5:0.95 of 0.603, an mAP@0.5 of 0.762, a precision of 0.737, a recall of 0.711, and an F1-score of 0.724. Model 2 achieved a higher precision of 0.861 but a lower recall. Field testing integrating YOLOv8n with DeepSORT demonstrated reliable fish detection and counting, although accuracy declined in turbid water and densely packed fish schools. These findings suggest that a lightweight YOLOv8 model without architectural modifications can serve as a viable starting point for developing automated fish monitoring systems for precision aquaculture in coastal regions such as Ambon.
Co-Authors Aris Wahyu Murdiyanto Lukman Saleh Merriam Listiany Modeong Sumiyatun