A Comparative Study of the Performance of KNN, NBC, C4.5, and Random Forest Algorithms in Classifying Beneficiaries of the Kartu Indonesia Sehat Program - Putri Nabillah; Inggih Permana; M. Afdal; Fitriani Muttakin; Arif Marsal (Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang, 2024)
This study evaluates the performance of various algorithms in determining eligible recipients for the Kartu Indonesia Sehat program. The Random Forest algorithm demonstrated the highest accuracy, precision, and recall, with values of 72.08%, 72.41%, and 99.64%, respectively. The emphasis on recall helps minimize errors in identifying eligible recipients. Additionally, the C4.5 algorithm reduced the total number of variables from 33 to 8, highlighting its computational efficiency. The findings provide valuable insights for the Social Affairs Office of Dumai City in making informed decisions regarding KIS eligibility. The results underscore the effectiveness of using algorithmic approaches to enhance the accuracy and efficiency of aid distribution processes.