Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal KOMPUTIKA - Jurnal Sistem Komputer Journal of Embedded Systems, Security and Intelligent Systems
Wahid, Yokogeri Abdullah
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimizing Career Choices in the World of Programming: A Web-Based Decision Support System with the Simple Additive Weighting (SAW) Method Akbar, Mohammad Arsan; Rusli, Risvan; Wahid, Yokogeri Abdullah; Surianto, Dewi Fatmarani; Adiba, Fathiah
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12404

Abstract

This study proposes the development of a web-based Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to help students choose a career in programming. By integrating data from online questionnaire surveys and observations, this research highlights the complexity of career choice in the world of programming. Criteria such as salary, work location, and educational requirements were identified as key factors in decision-making. The SAW method was chosen because of its ease of understanding, flexibility, and ability to handle complex problems. The system implementation process involves data collection, observation, web-based system design, and website development. The final results show that alternative A3 (Software development) received the highest preference weight, confirming it as the best choice based on the specified criteria. The use of DSS is expected to provide effective guidance for students in making more informed career decisions.
Performance Comparison of Svm and Naïve Bayes For Indonesian-Language Sentiment Analysis On Free Fire Reviews Using Tf-Idf And Smote Wahid, Yokogeri Abdullah; Sanatang; Andayani, Dyah Darma
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10818

Abstract

The popularity of online games continues to increase, including Free Fire, which has gained more than one billion downloads and millions of user reviews on the Google Play Store. However, the variation and inconsistency of user comments make manual sentiment evaluation difficult. This study aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes in classifying user review sentiment on the Free Fire game. A total of 535 Indonesian-language reviews were collected using web scraping and processed through text cleaning, case folding, normalization, stopword removal, and stemming. Sentiment labels were assigned manually based on review content. The dataset was divided into training and testing using a 70:30 ratio, and feature extraction used Term Frequency–Inverse Document Frequency (TF-IDF). Two scenarios were implemented: a baseline without class balancing and a scenario using Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Results show that SVM outperforms Naïve Bayes in both scenarios. In the baseline, SVM achieved 89.81% accuracy, while Naïve Bayes obtained 82.80%. After SMOTE, SVM improved to 91.08% accuracy and Naïve Bayes to 89.17%. These findings indicate that SVM, especially with SMOTE, provides a more effective and balanced performance for sentiment classification on Free Fire reviews. The study contributes to providing a more accurate understanding of user perception and strengthening model development for sentiment analysis on digital game applications.
Co-Authors Adiba, Fathiah Akbar, Mohammad Arsan Dewi Fatmarani Surianto Rusli, Risvan Sanatang