APPLICATION OF DATA MINING USING THE C4.5 ALGORITHM AND THE K-NEAREST NEIGHBOR (KNN)

Abstract

Direct cash assistance is a governmental or social institution intervention that provides financial aid directly to individuals or families in need. To streamline this process, a system is necessary to convert data into predictive information regarding eligibility for direct cash assistance. This research utilizes the C4.5 algorithm and the K-Nearest Neighbor algorithm for predicting eligibility based on factors such as housing status, employment, income, and eligibility status. Using the C4.5 algorithm, Microsoft Excel calculations identified 238 individuals as eligible and predicted 62 as ineligible who were eligible, out of a total of 300 recipients. The accuracy rate from RapidMiner calculations was 93.00%. Regarding the K-Nearest Neighbor method, Microsoft Excel calculations identified 226 eligible and 74 ineligible recipients out of 300. RapidMiner analysis showed an accuracy rate of 76.55% for the 226 eligible recipients and 98.23% for the 74 ineligible recipients.

Keywords

• algoritma c4.5, k-nearest neighbor, data mining, blt