<![CDATA[This study aims to enhance the credit evaluation process within Credit Union (CU) Karya Bersama Lestari (KABARI). The study leveraged four distinct algorithms, namely Decision Tree C4.5, Naive Bayes, K-Nearest Neighbors (K-NN), and Random Forest, to predict the suitability of extending loans to potential borrowers. Rapid Miner was employed as a tool to maximize accuracy by analyzing the Confusion matrix. Testing was conducted on a dataset consisting of 459 member loan submissions. The results of the analysis revealed that the K-Nearest Neighbors (K-NN) algorithm achieved the highest accuracy among the evaluated algorithms. Specifically, the Decision Tree algorithm demonstrated an accuracy rate of 95.65%, along with a precision and recall of 94.12%. The Naive Bayes algorithm achieved an accuracy rate of 95.65%, supported by precision and recall values of 100% and 88.24%, respectively. The K-Nearest Neighbors algorithm displayed the highest accuracy rate of 97.83%, accompanied by 100% precision and 94.12% recall. Meanwhile, the Random Forest algorithm exhibited an accuracy rate of 93.48%, complemented by precision and recall values of 100% and 82.35%, respectively. The study's conclusions bear relevance for refining loan approval processes and fostering improved lending practices within financial institutions like CU KABARI. ]]>
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