Implementasi Business Intelligence Untuk Analisis Data Tingkat Kerawanan Kebakaran Berbasis Wilayah - Javier Alvino Alfian; Denny Ganjar Purnama (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
This study aims to implement a Business Intelligence (BI) approach to analyze fire risk levels based on regional characteristics using open government data from Satu Data Jakarta. The dataset consists of 5,471 records for the period 2024–2025, including hazard, vulnerability, and capacity indicators at the neighborhood (RW) level. The methodology involves Extract, Transform, Load (ETL) using Python, data warehouse design with a star schema in PostgreSQL, OLAP-based analysis using SQL queries, and visualization through a web-based dashboard. The results indicate a significant increase in the proportion of high-risk areas from 16.62% in 2024 to 33.42% in 2025. However, this increase does not fully reflect actual changes in field conditions and may also be influenced by data distribution and the underlying risk classification system. Furthermore, the analysis reveals that fire risk is not evenly distributed but concentrated in specific regions, particularly in several districts of East Jakarta and South Jakarta, highlighting the importance of spatial-based approaches in determining mitigation priorities. This study utilizes pre-defined risk categories provided by the data source without performing predictive modeling or reclassification. The BI implementation not only integrates disparate data but also uncovers distribution patterns, risk trends, and regional priorities more systematically compared to conventional descriptive analysis. The findings contribute to supporting data-driven decision-making, especially in identifying priority areas for fire risk mitigation.