CLASSIFICATION OF FAMILY HOPE PROGRAM RECIPIENTS USING NAIVE BAYES AND C4.5 METHODS

Farras Ahmad Fauzi; Tatang  Rohana; Ayu Ratna Juwita; Deden Wahiddin

doi:10.52436/1.jutif.2024.5.5.3697

Farras Ahmad Fauzi Informatics Engineering, Computer Science Faculty, Buana Perjuangan Karawang University, Indonesia
Tatang Rohana Informatics Engineering, Computer Science Faculty, Buana Perjuangan Karawang University, Indonesia
Ayu Ratna Juwita Informatics Engineering, Computer Science Faculty, Buana Perjuangan Karawang University, Indonesia
Deden Wahiddin Informatics Engineering, Computer Science Faculty, Buana Perjuangan Karawang University, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.5.3697

Keywords: C4.5, Confusion Matrix, Classification, Naive Bayes, ROC Curve

Abstract

Receiving PKH assistance in Rawamerta District does not always go well, so there are people who are not entitled to receive assistance. This is because there is still no system that can facilitate the process of classifying PKH assistance recipients. The application of data mining can facilitate classification with high speed and accuracy. The purpose of this study is to classify PKH assistance recipients using the Naïve Bayes and C4.5 methods to determine the eligibility of PKH for people facing social welfare problems. The data used is PKH data in Rawamerta District, Karawang Regency in 2023, totaling 1834 data. The results of naive bayes accuracy of 98.89%, precision 98.25%, recall 98.51%, F1-score 98.89%, and AUC 1.00 are included in the excellent classification because they are in the range of 0.90-1.00, while the C4.5 algorithm produces Accuracy values of 99.26%, Precision 99.25%, Recall 99.25%, F1-score 99.25% and AUC 0.99 are included in the excellent classification because they are in the range of 0.90-1.00. The C4.5 algorithm is superior to Naive Bayes, because the accuracy produced is higher.

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