<![CDATA[Supplier performance evaluation in the automotive manufacturing industry is a critical activity that determines production line continuity. However, this process remains predominantly manual, resulting in administrative inefficiency, data inconsistency, and slow decision-making. This study aims to design and implement a web-based manufacturing information system that integrates a hybrid rule-based and machine learning approach to optimize supplier performance evaluation at PT ABC. The dataset comprises 1,008 transaction records from 28 suppliers over three years (2022–2024) with seven evaluation criteria: Accident, Incident, Line Stop, Off Line, Kanban Delay, Delivery Problem Report (LMD), and Delay Delivery. The research methodology employs Research and Development (R&D) with the Waterfall SDLC model enriched by the CRISP-DM methodology for the analytical component. Feature engineering produced 22 input variables through lag-1, trend analysis, and rolling average techniques, while class imbalance was addressed using SMOTE. Three ensemble algorithms (Random Forest, XGBoost, and Gradient Boosting) were evaluated through 5-Fold Stratified Cross Validation. XGBoost was selected as the best model with 88.82% accuracy and 88.80% Macro F1-Score. The hybrid fusion layer successfully generated tiered action recommendations across five urgency categories, with prediction accuracy on actual operational data reaching 93.16%. The contribution of this research to the development of scientific knowledge is the integration of an AI-based decision support system concept with an operational manufacturing information system platform, while providing a replicable hybrid framework for other manufacturing industry contexts in Indonesia.]]>
