FEASIBILITY TEST OF POOR RICE RECIPIENTS IN BENCOY SUKABUMI VILLAGE USING NAIVE BAYES

Taufik Hidayatulloh; Ardi Winardi; Lestari Yusuf; Satia Suhada; Saeful Bahri

doi:10.33480/pilar.v17i1.2227

Taufik Hidayatulloh ⁽¹⁾ Universita Bina Sarana Informatika
Ardi Winardi ⁽²⁾ Universita Bina Sarana Informatika
Lestari Yusuf ^(3*) Universitas Nusamandiri
Satia Suhada ⁽⁴⁾ Universitas Nusamandiri
Saeful Bahri ⁽⁵⁾ Universitas Nusamandiri

(*) Corresponding Author

DOI: https://doi.org/10.33480/pilar.v17i1.2227

Keywords: Naïve Bayes, Data Mining, Poor Rice

Abstract

A regional head must have a work plan every regional head must have a work plan which is sure to be of benefit to the community. Assisting is a definite work plan in every region. A lot of assistance is usually given from the government to the community and must be managed by the village government so that the aid gets to the right hands. And to improve food security, the people in each region have activities to distribute Poor Rice as a subsidy from the government. In the distribution method, sometimes there are constraints in data collection so that poor rice or what we usually call Raskin is not suitable for distribution. Because of this, a way is needed so that the distribution is appropriate or not in the community in accepting the Raskin so that government assistance can be delivered properly and on target. By using secondary data obtained from Bencoy Village, 205 data were obtained containing the attributes of the eligibility category of Raskin recipients, and 6 categories of attributes were found with the classification method of the Naïve Bayes algorithm. The accuracy value obtained is 96.59%, proving that the prediction using the Naive Bayes algorithm has a good performance. The next results obtained are in the form of AUC value which after being calculated produces a value of 0.999 and this results in an application which is an implementation with a flow that is adjusted to the calculation algorithm in the form of a web-based application.

Downloads

Download data is not yet available.

References

Ermawati, E., & Hidayatulloh, T. (2016). Penerapan Algoritma C4 . 5 Pada Sistem Penunjang Keputusan Penentuan Penerima Raskin (Beras Masyarakat Miskin). Seminar Nasional Ilmu Pengetahuan Dan Teknologi Komputer Nusa Mandiri, 123–134.

Fadlan, C., Ningsih, S., & Windarto, A. P. (2018). Penerapan Metode Naïve Bayes Dalam Klasifikasi Kelayakan Keluarga Penerima Beras Rastra. Jurnal Teknik Informatika Musirawas (JUTIM), 3(1), 1. https://doi.org/10.32767/jutim.v3i1.286

Firdyana, S., Cahyadi, D., & Astuti, I. F. (2017). Penerapan Metode Weighted Product Untuk Menentukan Penerima Bantuan Beras Masyarakat Miskin ( Raskin ). Prosiding SAKTI (Seminar Ilmu Komputer Dan Teknologi Informasi), 2(1), 336–342. http://e-journals.unmul.ac.id/index.php/SAKTI/article/view/282

Hidayat, R., Marlina, S., & Utami, L. D. (2017). Perancangan Sistem Informasi Penjualan Barang Handmade Berbasis Website Dengan Metode Waterfall. Simnasiptek 2017, A-178.

Hidayatulloh, T. et al. (2021). Feasibility Test Of Poor Rice Recipients In Bencoy Sukabumi Village Using Naive Bayes.

Kaesman, Y. R. (2016). Penentuan Penerima Beras Raskin di Kelurahan Oesapa Barat Menggunakan Metode K-Nearest Neighbor. Teknologi Terpadu, 2(2).

Maricar, M. A., & Dian Pramana. (2019). Perbandingan Akurasi Naïve Bayes dan K-Nearest Neighbor pada Klasifikasi untuk Meramalkan Status Pekerjaan Alumni ITB STIKOM Bali. Jurnal Sistem Dan Informatika (JSI), 14(1), 16–22. https://doi.org/10.30864/jsi.v14i1.233

Nasir, jamal A. (2019). Sistem Pendukung Keputusan PEmberian BEras Untuk KEluarga Miskin Dengan MEtode Simple Additive Weigthing. Jurnal Riset Informatika, 1(3), 134–138.

Nisak, A. F. (2014). Implementasi Kebijakan Beras Miskin ( Raskin ) di Kecamatan Kenjeran Kota Surabaya : Studi Deskriptif pada Kelurahan Tanah Kalikedinding. Jurnal Politik Muda, 3(2), 17–25.

Simbolon, L. D., Situmorang, M., & Napitupulu, N. (2014). Aplikasi Metode Transportasi dalam Optimasi Biaya Distribusi Beras Miskin (Raskin) pada Perum Bulog Sub Divre Medan. Saintia Matematika, 2(3), 299-311.

Sugiharti, E., Firmansyah, S., & Devi, F. R. (2017). Predictive evaluation of performance of computer science students of unnes using data mining based on naÏve bayes classifier (NBC) algorithm. Journal of Theoretical and Applied Information Technology, 95(4), 902–911.

Suryeni, E., Dan, Y. H. A., & Nurfitria, Y. (2015). Sistem Pendukung Keputusan Kelayakan Penerimaan Bantuan Beras Miskin Dengan Metode Weighted Product Di Kelurahan Karikil Kecamatan Mangkubumi Kota Tasikmalaya. Konferensi Nasional Sistem & Informatika 2015, 345–350.

Tone, K. (2016). Untuk perancangan proses digambarkan menggunakan DFD (. Jurnal Instek, 1(1), 50–60.

Waliyansyah, R. R., & Fitriyah, C. (2019). Perbandingan Akurasi Klasifikasi Citra Kayu Jati Menggunakan Metode Naive Bayes dan k-Nearest Neighbor (k-NN). Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(2), 157. https://doi.org/10.26418/jp.v5i2.32473

Winardi, A. dkk. (2021). The Feasibility Test For Beras Miskin In The Village Of Bencoy Sukabumi Using Naïve Bayes.

Wulandari, D. A. N., Annisa, R., & Yusuf, L. (2020). an Educational Data Mining for Student Academic Prediction Using K-Means Clustering and Naïve Bayes Classifier. Jurnal Pilar Nusa Mandiri, 155–160.