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All Journal 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 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/r4d62a66

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.
Co-Authors Aris Wahyu Murdiyanto Merriam Listiany Modeong Sumiyatun