KLASIFIKASI KELAYAKAN PENERIMA BANTUAN LANGSUNG TUNAI DANA DESA (BLT DD) MENGGUNAKAN ALGORITMA NAÏVE BAYES DI DESA TARAJU: Bahasa Indonesia - Neng Sri Lathifah Zulfa; Iffah Athifah (SKANIKA: Sistem Komputer dan Teknik Informatika, 2025)
The Direct Cash Assistance from Village Funds (BLT-DD) program is designed to provide support to rural communities with limited economic means. To ensure that the assistance is properly targeted, the selection process for beneficiaries must be carried out carefully. This study applies the Naïve Bayes algorithm to classify the eligibility of BLT-DD recipients in Taraju Village. Three variants of the Naïve Bayes algorithm were tested, namely Bernoulli Naïve Bayes, Gaussian Naïve Bayes, and Complement Naïve Bayes, using 10-fold cross-validation for evaluation. The results showed that Bernoulli Naïve Bayes achieved the highest accuracy at 91%, followed by Gaussian Naïve Bayes with 90%, and Complement Naïve Bayes with 64%. These findings indicate that Bernoulli Naïve Bayes is more effective in classifying the eligibility of BLT-DD recipients compared to the other two variants.