Said Nanda Saputra, Elin Haerani, J. Jasril, Lola Oktavia, Fadhilah Syafria
{"title":"Application of K-Means Algorithm on Clustering Recipients of Non-Cash Food Assistance (NCFA)","authors":"Said Nanda Saputra, Elin Haerani, J. Jasril, Lola Oktavia, Fadhilah Syafria","doi":"10.24114/cess.v8i2.48026","DOIUrl":null,"url":null,"abstract":"Persoalan Kemiskinan pada berbagai daerah Indonesia menjadi fokus perhatian. Program BPNT (Bantuan Pangan Non Tunai) bermaksud memangkas biaya pangan dan membagikan gizi yang sepadan terhadap KPM (Keluarga Penerima Manfaat). Penelitian ini menerapkan algoritma K-Means untuk menganalisis pola karakteristik penerima BPNT di Pekanbaru. Data yang digunakan berasal dari penelitian sebelumnya oleh Firza Syahputra dan dari Dinas Sosial Kota Pekanbaru tahun 2020-2021 dengan 732 data dan 41 parameter. Penerapan K-Means dilakukan melalui Google Colab. Melalui data mining dan metode clustering, ditemukan dua klaster dengan 666 data dalam klaster 1 dan 16 data dalam klaster 2. Evaluasi menggunakan Silhouette Score menunjukkan hasil yang baik, dengan nilai 0.9169796594018274. Penelitian ini berpotensi membantu pemerintah dalam mengambil keputusan yang efektif selama penyebaran bantuan pangan non tunai kepada rakyat yang membutuhkan. Dengan demikian, algoritma K-Means Clustering dapat mengidentifikasi pola karakteristik penerima BPNT dan membedakan kelompok yang layak dan tidak layak menerima bantuan.Poverty issues in various parts of Indonesia are the focus of attention. The NCFA (Non-Cash Food Assistance) program's purpose are to lower food consumption and give Beneficiary Families (BF) a healthy diet. The k-means technique use in this study to assess the distinctive patterns of NCFA grantees in Pekanbaru. The data used comes from previous research by Firza Syahputra and from Social Affairs Office Pekanbaru in 2020-2021 with 732 data and 41 parameters. The application of k-means is done through Google Colab. Through data mining and clustering methods, two clusters were found with 666 data in cluster 1 and 16 data in cluster 2. Evaluation using Silhouette Score showed good results, with a value of 0.9169796594018274. This research has the potential to assist the government in making effective decisions in distributing non-cash food help people in need. For the result, the k-means Clustering technique is able to recognize the traits of NCFA recipients and identify groups that are and are not eligible for aid.","PeriodicalId":53361,"journal":{"name":"CESS Journal of Computer Engineering System and Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CESS Journal of Computer Engineering System and Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24114/cess.v8i2.48026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Persoalan Kemiskinan pada berbagai daerah Indonesia menjadi fokus perhatian. Program BPNT (Bantuan Pangan Non Tunai) bermaksud memangkas biaya pangan dan membagikan gizi yang sepadan terhadap KPM (Keluarga Penerima Manfaat). Penelitian ini menerapkan algoritma K-Means untuk menganalisis pola karakteristik penerima BPNT di Pekanbaru. Data yang digunakan berasal dari penelitian sebelumnya oleh Firza Syahputra dan dari Dinas Sosial Kota Pekanbaru tahun 2020-2021 dengan 732 data dan 41 parameter. Penerapan K-Means dilakukan melalui Google Colab. Melalui data mining dan metode clustering, ditemukan dua klaster dengan 666 data dalam klaster 1 dan 16 data dalam klaster 2. Evaluasi menggunakan Silhouette Score menunjukkan hasil yang baik, dengan nilai 0.9169796594018274. Penelitian ini berpotensi membantu pemerintah dalam mengambil keputusan yang efektif selama penyebaran bantuan pangan non tunai kepada rakyat yang membutuhkan. Dengan demikian, algoritma K-Means Clustering dapat mengidentifikasi pola karakteristik penerima BPNT dan membedakan kelompok yang layak dan tidak layak menerima bantuan.Poverty issues in various parts of Indonesia are the focus of attention. The NCFA (Non-Cash Food Assistance) program's purpose are to lower food consumption and give Beneficiary Families (BF) a healthy diet. The k-means technique use in this study to assess the distinctive patterns of NCFA grantees in Pekanbaru. The data used comes from previous research by Firza Syahputra and from Social Affairs Office Pekanbaru in 2020-2021 with 732 data and 41 parameters. The application of k-means is done through Google Colab. Through data mining and clustering methods, two clusters were found with 666 data in cluster 1 and 16 data in cluster 2. Evaluation using Silhouette Score showed good results, with a value of 0.9169796594018274. This research has the potential to assist the government in making effective decisions in distributing non-cash food help people in need. For the result, the k-means Clustering technique is able to recognize the traits of NCFA recipients and identify groups that are and are not eligible for aid.