COMPARISON OF NAIVE BAYES ALGORITHM AND C.45 ALGORITHM IN CLASSIFICATION OF POOR COMMUNITIES RECEIVING NON CASH FOOD ASSISTANCE IN WANASARI VILLAGE KARAWANG REGENCY
Perbandingan Algoritma Naive Bayes Dan Algoritma C.45 Pada Klasifikasi Masyarakat Miskin Penerima Bantuan Pangan Non Tunai Di Desa Wanasari Karawang
Non-Cash Food Assistance or Bantuan Pangan Non-Tunai (BPNT) is food assistance from the government given to the Beneficiary Family (KPM) every month through an electronic account mechanism that is used only to buy food at the Electronic Shop Mutual Assistance Joint Business Group Hope Family Program (e-Warong KUBE PKH ) or food traders working with Bank Himbara. In its distribution, BPNT still has problems that occur that are experienced by the village apparatus especially the apparatus of Desa Wanasari on making decisions, which ones are worthy of receiving (poor) and not worthy of receiving (not poor). So one way that helps in making decisions can be done through the concept of data mining. In this study, a comparison of 2 algorithms will be carried out namely Naive Bayes Classifier and Decision Tree C.45. The total sample used is as much as 200 head of household data which will then be divided into 2 parts into validation techniques is 90% training data and 10% test data of the total sample used then the proposed model is made in the RapidMiner application and then evaluated using the Confusion Matrix table to find out the highest level of accuracy from 2 of these methods. The results in this classification indicate that the level of accuracy in the Naive Bayes Classifier method is 98.89% and the accuracy level in the Decision Tree C.45 method is 95.00%. Then the conclusion that in this study the algorithm with the highest level of accuracy is the Naive Bayes Classifier algorithm method with a difference in the accuracy rate of 3.89%.
Alkhalifi, Y., Zumarniansyah, A., Ardianto, R., Hardi, N., & Annisa Elfina Augustia. (2020). Laporan Akhir Penelitian Mandiri. Jakarta.
Apandi, T. H., Maulana, R. B., Piarna, R., & Vernanda, D. (2019). Menganalisis Kemungkinan Keterlambatan Pembayaran Spp Dengan Algoritma C4.5 (Studi Kasus Politeknik Tedc Bandung). Jurnal Techno Nusa Mandiri, 16(2), 93–98. https://doi.org/10.33480/techno.v16i2.659
Bustami. (2014). Penerapan Algoritma Naive Bayes Untuk Mengklasifikasi Data Nasabah Asuransi. Jurnal Informatika (Yogyakarta), 8(1), 884–898. https://doi.org/10.26555/jifo.v8i1.a2086
Ermawati, E. (2019). Ermawati, Algoritma Klasifikasi C4.5 Berbasis Particle Swarm Optimization Untuk Prediksi Penerima Bantuan Pangan Non Tunai 513. Sistemasi, 8(September), 513–528.
Husin, A. I., & Mulyaningsih, F. (2015). Penerapan Metode Data Mining Analisis Terhadap Data Penjualan Pakaian Dengan Algoritma Apriori. Sniptek, 45–56.
Katadata Indonesia. (2018). 2018, Jumlah Penduduk Indonesia Mencapai 265 Juta Jiwa.
Katadata Indonesia. (2019). Jumlah Penduduk Indonesia 2019 Mencapai 267 Juta Jiwa.
Kementerian Sosial Republik Indonesia. (2017). Bantuan Pangan Non Tunai (BPNT). Jakarta Pusat.
Saleh, A. (2015). Implementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga. Citec Journal, 2(3), 207–217. https://doi.org/doi.org/10.24076/citec.2015v2i3.49
Septiyana Firdyana, Dedy Cahyadi, I. F. A. (2017). Penerapan Metode Weighted Product Untuk Menentukan Penerima Bantuan Beras Masyarakat Miskin ( Raskin ). Prosiding Seminar Ilmu Komputer Dan Teknologi Informasi, 2(1), 1–7.
Sugianto, C. A., Maulana, F. R., & Mining, D. (2019). Algoritma Naïve Bayes Untuk Klasifikasi Penerima Bantuan Pangan Non Tunai ( Studi Kasus Kelurahan Utama ). TechnoCom, 18(4), 321–331.
Yunita, F. (2018). Penerapan Data Mining Menggunkan Algoritma K-Means Clustring Pada Penerimaan Mahasiswa Baru (Studi Kasus : Universitas Islam Indragiri). Jurnal Sistemasi, 7(September), 238–249.
Zuhair, A., Suyono, H., Muslim, M. A., Elektro, J. T., Teknik, F., & Brawijaya, U. (2019). Optimasi Injeksi Photovoltaic Distributed Generation Menggunakan Metode Ant Colony Optimization Continuous Domain dan Improved Particle Swarm Optimization, 13(3), 145–149.
Abstract viewed = 432 times
PDF downloaded = 367 times
The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license.
- The copyright in each article belongs to the author.
- Authors retain all their rights to published works, not limited to the rights set out on this page.
- The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC).
- Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri);
- The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author.
- If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.