<![CDATA[Delays in electricity bill payments pose a significant challenge for PLN in maintaining financial stability and delivering equitable service quality to the public. This study aims to develop a payment delay prediction system to assist PLN UP3 Makassar Utara in prioritizing invoice distribution to customers with a high likelihood of late payments. The Random Forest algorithm was chosen for its ability to handle complex data and produce reliable predictions. This research analyses historical electricity customer data from 2018 to 2023, encompassing 227,163 entries. The data is processed using validation techniques such as K-Fold Validation and Rolling Window Validation to ensure the accuracy and generalizability of the model. The study's findings demonstrate that an accurate payment delay prediction model can be developed using the Random Forest method, incorporating historical features such as lag features, moving averages, and seasonal variables. Additionally, the system prioritizes invoice delivery to high-risk customers based on risk scores derived from historical delay patterns. This system reduces payment arrears at PLN UP3 Makassar Utara through proactive measures such as early notifications, personalized reminders, or payment incentives to encourage timely payments. As a result, the study indicates that the system effectively enhances the efficiency of payment management and supports the company's financial stability. However, the research is limited by the use of data from a single region, the absence of external factors in the model, and the high computational requirements. For broader implementation, further research should include data from other regions, consider external factors, and optimize computational resource usage.]]>
Copyrights © 2025
