Geographic Information System (GIS) For Road Repair Planning Prioritization Using Naive Bayes - Alamsyah, Bintang; Hasanah, Herliyani; Oktaviani, Intan (Journal of Artificial Intelligence and Software Engineering, 2025)
Prioritizing road infrastructure repair in rural areas often faces challenges, particularly due to budget limitations and subjective evaluation processes. This study aims to design and develop a web-based Geographic Information System (GIS) integrated with the Naive Bayes classification algorithm to support objective road repair prioritization. The system was developed using the Rapid Application Development (RAD) approach, involving active user participation in iterative development cycles. The application was built using Laravel as the backend framework, Leaflet.js for interactive map visualization, and PostgreSQL with the PostGIS extension for spatial data management. The system is capable of managing regional and road data, receiving road damage reports, and classifying repair priorities into high, medium, or low categories based on parameters such as damage level, traffic volume, and road length. The classification results are visualized on an interactive map to assist village officials in monitoring infrastructure and making informed decisions. System evaluation using black box testing confirmed that all functionalities operate validly in accordance with user requirements. This system offers an accurate and transparent data-driven solution for managing road infrastructure in Jelobo Village and has the potential to be replicated in other regions with similar conditions.