MSMEs Recommendation System using Item-Based Collaborative Filtering and LightGBM Machine Learning - Mar’atuttahirah, Mar’atuttahirah; Tunnisa, Khaera; Ra, Danang Fatkhur Razak; Najwa, Hafizah; Fahrisal, Januar (Jurnal Teknik Informatika (Jutif), 2025)
Micro, Small, and Medium Enterprises (MSMEs) face challenges in recommendation systems for digital economy growth, particularly in participatory development and sustainable revenue optimization. This study aims to develop a recommendation system using Item-Based Collaborative Filtering and LightGBM for stock prediction and item recommendation at Kedai Pesisir MSME. Based on 1,229 transaction records from January to July 2025, we performed preprocessing, feature engineering, and LightGBM training to generate daily stock predictions and monthly priorities for August 2025 to January 2026. Evaluation yielded RMSE 0.069, MAE 0.034, and MAPE 1.14%, indicating high accuracy. This advances informatics by providing a scalable AI tool for MSME inventory management and revenue enhancement, supporting strategic decisions in dynamic markets.