Abstract
Direct Cash Assistance (BLT) is a government program aimed at helping people who are classified as financially disadvantaged. However, in implementing it, there are challenges in accurately identifying BLT receipts that meet the set criteria. Therefore this study aims to develop a prediction model using data mining techniques to more effectively predict BLT receipts in Tumbang Langkai Village. In this study, we collected data on the population of Tumbang Langkai Village, including demographic information, education level, age, marital status, income, occupation, and history of receiving BLT previously. What we can do is process data such as correcting incomplete or valid data by conducting data analysis, then classification algorithms such as Naive Bayes, Decision Tree, or Support Vector Machines (SVM) are applied to train predictive models based on relevant attributes. It is hoped that the results of this study can contribute to the development of an efficient system for determining BLT beneficiaries. With an accurate prediction model, the government can optimize the allocation of funds and ensure that BLT assistance is on target This research can also provide insight into the factors that contribute to receiving social assistance in Tumbang Langkai Villagewhich can be the basis for more effective policy-making in supporting people's welfare
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