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All Journal Jurnal Ilmu Siber dan Teknologi Digital Jurnal Studi Multidisiplin Ilmu
Sulyono Sulyono
Institut Informatika dan Bisnis Darmajaya, Bandar Lampung, Indonesia
Computer Science & IT Economics, Econometrics & Finance Education Engineering Social Sciences Other

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Decision Tree C4.5 Algorithm for Classifying Bullying and Sexual Harassment Types in Senior High Schools Rafli Pahlevi; Sulyono Sulyono
Jurnal Ilmu Siber dan Teknologi Digital Vol 4 No 2 (2026): Mei
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jisted.v4i2.6939

Abstract

Purpose: This study aims to implement the Decision Tree C4.5 algorithm to classify bullying and sexual harassment cases in senior high schools and develop a web-based decision support system for consistent, evidence-based identification and intervention.Methodology: A quantitative experimental approach was applied. Data were collected through anonymous student surveys and interviews with Guidance and Counselling (BK) teachers, resulting in 120 cases (93 bullying and 27 sexual harassment). The C4.5 algorithm was implemented using RapidMiner, while the web system was developed using Waterfall System Development Life Cycle (SDLC) with Personal Home Page (PHP) Laravel, MySQL, HTML/CSS, and tested using black box testing.Results: The model produced a total entropy of 2.44989, with “Incident Type” as the root node (Information Gain = 1.811). “Incident Frequency” became the second-level node. The system successfully classified cases and provided recommendations with 100% success in all nine black box tests covering authentication, classification, reporting, and data management modules. Conclusions: The C4.5 algorithm effectively classifies bullying and sexual harassment cases, while the web-based system enhances consistency and reduces subjectivity in school decisionmaking.Limitations: The dataset is limited to 120 cases at the senior high school level, without precision, recall, or F1-score analysis and no longitudinal data.Contributions: This study provides an operational decision support system using C4.5 for structured classification of schoolbased bullying and sexual harassment cases.
K-Nearest Neighbors Based Matic Motorcycle Damage Prediction System Web Application Preventive Maintenance Bengkel Sahabat Motor Anggi Wijaya; Sulyono Sulyono
Jurnal Studi Multidisiplin Ilmu Vol 3 No 1 (2025): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jasmi.v3i1.6937

Abstract

Purpose: This study develops a web-based matic motorcycle damage prediction system using the K-Nearest Neighbors (KNN) algorithm at Bengkel Sahabat Motor to support early damage detection, preventive maintenance, and cost reduction. Methodology: A quantitative approach with waterfall System Development Life Cycle (SDLC) was used. Data were collected through observation, interviews, and workshop records. The system was built using Personal Home Page (PHP), html, Cascading Style Sheets (CSS), JavaScript, and MySQL. KNN with Euclidean distance and K=3 was applied, using a three-level symptom scale. System design used Unified Modeling Language (UML) and validation was conducted through black box testing. Results: The system accurately classifies motorcycle damage, with test outputs correctly identifying "Engine Overheating" based on nearest neighbor distances. Black box testing achieved 100% acceptance across 143 test items, categorized as “Very Good.” Diagnosis time decreased from 30 to 10 minutes per case. Conclusions: The KNN-based system effectively automates motorcycle damage classification and improves diagnostic efficiency. Limitations: The study is limited to a single workshop, small dataset, no IoT integration, and lacks formal accuracy metrics. Contributions: This study provides a practical machine learningbased predictive maintenance system for motorcycle workshops, offering a replicable framework for digital diagnostics in the automotive service sector.
Co-Authors Anggi Wijaya Rafli Pahlevi