K-Nearest Neighbors Based Matic Motorcycle Damage Prediction System Web Application Preventive Maintenance Bengkel Sahabat Motor - Anggi Wijaya; Sulyono Sulyono (Penerbit Goodwood, 2025)
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