Analysis and Visualization of Operational Data Using Business Intelligence to Support Strategic Decision-Making at the Waste Bank in Lawangan Daya
Keywords:
Business Intelligence, Data Analytics, Data Visualization, Decision Support System (DSS), Bank SampahAbstract
Waste banks generate operational data—such as customer details, waste types and volumes, and transaction records—that often remain underutilized for evaluation and decision-making due to a lack of clear processing and presentation. This study aims to utilize and visualize the operational data of the Lawangan Daya Waste Bank using Business Intelligence (BI) to support strategic decision-making. A quantitative descriptive method was employed, with data collected through observation, interviews, and documentation. The data underwent cleaning, ETL (Extract, Transform, Load) processing, data warehouse storage, analysis, and dashboard visualization, utilizing simulated data covering the January–June period. The results indicate that the number of active customers rose from 28 to 43, with a total of 563 transactions, 2,204 kg of waste collected, and revenue amounting to IDR 6,612,000. Waste volume increased by approximately 60% between January and June, driven primarily by the growing customer base, while the average deposit per customer remained relatively stable at around 10–11 kg per month. Plastic constituted the largest share of waste (742 kg or approximately 34%), followed by paper and cardboard. The BI dashboard presents key indicators, monthly trends, and comparisons of waste types in a concise format. It is concluded that Business Intelligence assists management in monitoring operational performance and formulating strategies, such as those regarding consumer protection and the management of priority waste types. Future research is recommended to utilize real-world data over a longer period and incorporate additional indicators, such as customer retention rates and pricing per waste type.
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