Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Fakultas Teknik UNISA Kuningan
Neng Sri Lathifah Zulfa
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : SKANIKA: Sistem Komputer dan Teknik Informatika

 1 
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 Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3560

Abstract

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.
Co-Authors Fanji Fakhru Zaman Iffah Athifah Reni Nuraeni, Reni