Implementation of the Naive Bayes and Simple Additive Weighting Methods in the Feasibility Selection of Village Cash Assistance Recipients

Bain Khusnul Khotimah; Wahyu Indra Kustina; Yeni Kustiyahningsih; Devie Rosa Anamisa; Fifin Ayu Mufarroha

doi:10.55927/ijis.v2i5.4244

Authors

Bain Khusnul Khotimah Department of Informatics Engineering, University of Trunojoyo Madura, East Java
Wahyu Indra Kustina Department of Informatics Engineering, University of Trunojoyo Madura, East Java
Yeni Kustiyahningsih Department of Informatics Engineering, University of Trunojoyo Madura, East Java
Devie Rosa Anamisa Department of Informatics Engineering, University of Trunojoyo Madura, East Java
Fifin Ayu Mufarroha Department of Informatics Engineering, University of Trunojoyo Madura, East Java

DOI:

https://doi.org/10.55927/ijis.v2i5.4244

Keywords:

VF-DCA, Decision Support Systems, Methods, NB, SAW

Abstract

Village Fund Direct Cash Assistance (VF-DCA) is a direct cash assistance policy in which funds come from the village. The VF-DCA policy is expected to ease the burden on the community, especially those with a low economy. Still, it is undeniable that there are opportunities for misuse of village funds by some VF-DCA organizers, intentionally or unintentionally. Problems that arise in implementing VF-DCA are mistakes in determining the beneficiary communities that are not on target so that the impact of VF can be manipulated for the interests of certain groups in several cases of social assistance programs. The solution to these problems is that this research creates a decision support system in selecting the eligibility of assistance recipients. This research used the Naïve Bayes (NB) method in selecting VF-DCA recipients and the Simple Additive Weighting (SAW) way to determine the order of eligibility scores for each candidate for VF-DCA recipients. In this study, the results of the classification process using the NB method obtained an accuracy rate of 99.36%. Hence, this is simplified in determining potential recipients of VF-DCA using the NB and SAW methods

Downloads

Download data is not yet available.

References

Arfanda, I., Ramdhan, W. and Yusda, R.A. (2021a) ‘Digital Transformation Technology ( Digitech ) | e-ISSN : 9999-9999 Naive Bayes Dalam Menentukan Penerima Bantuan Langsung Tunai Digital Transformation Technology ( Digitech ) | e-ISSN : 9999-9999’, 1(1), pp. 9–16.

Arfanda, I., Ramdhan, W. and Yusda, R.A. (2021b) ‘Naive Bayes Dalam Menentukan Penerima Bantuan Langsung Tunai’, Digital Transformation Technology, 1(1), pp. 9–16. Available at: https://doi.org/10.47709/digitech.v1i1.1091.

Herdiana, D. et al. (2021) ‘Implementasi Kebijakan Bantuan Langsung Tunai ( BLT ) Dana Desa Bagi Masyarakat Terdampak COVID-19 di Kabupaten Sumedang : Isu dan Tantangan Implementation of Village Fund Direct Cash Assistance Policy for Villagers Affected by COVID-19 in Sumedang Regenc’, Jurnal Inspirasi, 5(June), pp. 1–16. Available at: https://doi.org/10.35880/inspirasi.v11i1.175.

Nasution, S.R., Andreswari, D. and Wahyu, T. (2019) ‘Implementasi Naïve Bayes Classifier Dan Simple Additive Weighting (Saw) Untuk Pemilihan Menu Diet Penyakit Diabetes Mellitus’, Jurnal Rekursif, 7(1), pp. 1–9.

Ningtyas, R.P.A.C. and Suyatno, D.F. (2021) ‘Sistem Pendukung Keputusan Bantuan Langsung Tunai Pada Masa Pandemi Covid 19 Menggunakan Perbandingan Metode Simple Additive Weighting dan Fuzzy Berbasis Website (Studi Kasus: Desa Krisik, Kecamatan Gandusari, Kabupaten Blitar)’, Journal of Emerging Information System and Business Intelligence (JEISBI), 2(2), pp. 56–65.

Pertiwi, I.P., Fedinandus, F. and Limantara, A.D. (2019) ‘Sistem Pendukung Keputusan Penerima Program Keluarga Harapan (PKH) Menggunakan Metode Simple Additive Weighting’, CAHAYAtech, 8(2), p. 182. Available at: https://doi.org/10.47047/ct.v8i2.46.

Saputra, P.S. (2021) ‘Perbandingan Algoritma Fuzzy C-Means Dan Algoritma Naive Bayes Dalam Menentukan Keluarga Penerima Manfaat (Kpm) Berdasarkan Status Sosial Ekonomi (Sse) Terendah’, JST (Jurnal Sains dan Teknologi), 10(1), pp. 1–8. Available at: https://doi.org/10.23887/jstundiksha.v10i1.23340.

Socrates, I.G.A. et al. (2016) ‘Optimasi Naive Bayes Dengan Pemilihan Fitur Dan Pembobotan Gain Ratio’, Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 7(1), p. 22. Available at: https://doi.org/10.24843/lkjiti.2016.v07.i01.p03.

Syarifudin, A., Hidayat, N. and Fanani, L. (2018) ‘Sistem Pakar Diagnosis Penyakit Pada Tanaman Jagung Menggunakan Metode Naive Bayes Berbasis Android’, 2(7).

Syarli, S. and Muin, A. (2016) ‘Metode Naive Bayes Untuk Prediksi Kelulusan (Studi Kasus: Data Mahasiswa Baru Perguruan Tinggi)’, Jurnal Ilmiah Ilmu Komputer, 2(1), pp. 22–26.