Machine Learning-Based Predictive System for Cultural Heritage Site Condition Assessment - Arif Ridho Lubis; Ali Basrah Pulungan (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
Cultural heritage preservation in North Sumatra faces challenges from manual, decentralized, and reactive site condition reporting. This study designs and develops a web-based Cultural Heritage Information System (CHIS) integrated with a Machine Learning (ML) prediction module for the Disbudparekraf of North Sumatra Province. The system is built using the CodeIgniter 4 framework with Role-Based Access Control (RBAC), MariaDB database, GIS integration via Leaflet.js, and a prediction module employing Random Forest Regressor with features including health score, structural integrity, physical integrity, authenticity, age factor, and maintenance score. Development follows the Waterfall model encompassing requirements analysis, design, implementation, black-box testing (47 test cases), and deployment. Results demonstrate successful integration of 12 historical assessment records from 10 priority heritage sites, generating 8 predictions with an average confidence score of 45.9% and automatic identification of high-risk sites such as Masjid Raya Al Mashun (confidence 51%). The system produces structured maintenance recommendations across three priority categories with specific timelines. The moderate confidence score reflects initial dataset limitations and is expected to improve with accumulating assessment data. This research contributes a replicable GIS-ML integration model for heritage conservation at the Indonesian local government level.