Implementasi Data Mining Menggunakan Algoritma Naïve Bayes Untuk Klasifikasi Penyaluran Dana Zakat - Muhammad Saiful; Amri Muliawan Nur; Aswian Editri Sutriandi; Eka Puspita; B. Nadila Nuzululnisa (Infotek: Jurnal Informatika dan Teknologi, 2025)
Zakat is one of the pillars in Islam that aims to reduce economic disparity and assist those in need. Effective distribution of zakat requires a system capable of accurately identifying and targeting mustahik (zakat recipients). This study aims to implement data mining techniques using the Naïve Bayes Algorithm for the classification of zakat fund distribution at the National Amil Zakat Agency (BAZNAS) in East Lombok Regency. The Naïve Bayes Algorithm was chosen for its ability to predict categories based on probability and historical data. The data used is private data obtained through the financial reports of BAZNAS East Lombok Regency for the years 2022-2023, with 461 mustahik including family members, income, and economic status. There are 8 attributes used in this study. Data processing is conducted using RapidMiner software with the Naïve Bayes algorithm. The results show that the Naïve Bayes Algorithm achieved the best accuracy rate from the 3-fold validation test, amounting to 99.57% with an AUC value of 0.997%. The tests conducted provided excellent classification results. With this comprehensive and data-driven approach, it is hoped that this study can provide effective solutions to the current zakat fund distribution issues