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All Journal Jurnal Informatika Progres CSRID
Wal Ikram, Dzul Jalali
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Computer Science & IT

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Deteksi Spam Bot Pada Komentar Youtube: Tinjauan Literatur Sistematis Pane, Syafrial; Wal Ikram, Dzul Jalali
CSRID (Computer Science Research and Its Development Journal) Vol. 15 No. 2: June 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.15.2.2023.103-123

Abstract

YouTube is a very popular social media platform and is used by millions of people around the world. However, the presence of spam in comments can disrupt the user experience and affect the overall quality of the platform. Therefore, in this article, we conducted a Systematic Literature Review (SLR) to evaluate methods for detecting spam in comments on YouTube. In this SLR, we search for related research published between 2018 and 2023 in trusted databases such as Science Direct, IEEE Xplore, and Springer using Publish or Perish software. After making the selection, 17 of the 80 selected articles met our research criteria. The SLR results show that the Email dataset is the most widely used in spam detection research, and the most frequently used approach is supervised learning. In addition, most of the research focuses more on selecting features to improve accuracy in spam detection. The findings from this SLR can provide important insights for researchers who wish to conduct further research on spam detection on comments on YouTube.
IMPLEMENTASI ALGORITMA APRIORI UNTUK ANALISIS PERSEDIAAN MATERIAL DI WAREHOUSE PT. TELKOM AKSES MAKASSAR Wal Ikram, Dzul Jalali; Sadrin, Ahmad Rifai; Moeis, Dikwan; Rosnani
PROGRESS Vol 17 No 1 (2025): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i1.453

Abstract

This research aims to implement Apriori algorithm for data mining in material inventory management at PT. Telkom Akses Makassar. Apriori algorithm identifies frequent itemsets and generates association rules from transaction data to optimize warehouse stock management. The methodology includes data collection through observation, interviews, and historical transaction datasets. Data processing uses Apriori to calculate support, confidence, and lift metrics. The results indicate that frequent item combinations can improve planning accuracy and reduce stockouts. A web-based application, Material Analyzer, was developed for analysis and visualization, featuring dashboard, analysis, history, and visualization modules. This study contributes practically by supporting logistics decision-making and theoretically by expanding data mining applications in inventory systems.
Co-Authors Moeis, Dikwan Pane, Syafrial Rosnani Sadrin, Ahmad Rifai