IMPLEMENTASI NAIVE BAYES UNTUK MENGANALISIS SENTIMEN CAPRES 2024 PRABOWO SUBIANTO BERBASIS MACHINE LEARNING

Abstract

Targeted loan disbursement is an important factor in maintaining the stability and sustainability of credit unions. CU. Makmur Sejahtera, as a savings and loan cooperative, requires an objective and systematic approach to determining the eligibility of members for loan approval. This study aims to optimize the loan disbursement process by implementing the Multi-Attributive Border Approximation Area Comparison (MABAC) method. Five criteria are used in the decision-making process: savings amount, membership duration, monthly income, loan history, and asset collateral. The MABAC method is employed to calculate the score of each alternative based on the deviation of its values from the Border Approximation Area (BAA), thereby generating a fair and data-driven final ranking. The results indicate that this method can identify the most eligible members for loan approval while promoting transparency in the selection process. The implementation of the MABAC method can serve as an effective solution for supporting multicriteria decision-making in cooperatives.