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All Journal Sistemasi: Jurnal Sistem Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Building of Informatics, Technology and Science Jurnal Tunas J-SAKTI (Jurnal Sains Komputer dan Informatika) J-Com (Journal of Computer) Jurnal IPTEK Bagi Masyarakat Journal of Computer Science and Informatics Engineering Journal of Information Systems and Technology Research Jurnal Informatika dan Teknologi Informasi Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Journal Of Indonesian Social Society Nuansa Informatika Bigint Computing Journal
Irawati, Novica
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Journal : Sistemasi: Jurnal Sistem Informasi

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Classification of Eligibility for BPNT Recipients Using the K-Nearest Neighbor Algorithm Rahayu, Eka; Irawati, Novica; Ananda, Ricki
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3250

Abstract

BPNT (Non-Cash Food Assistance) is food social assistance in non-cash form from the government which is given to Beneficiary Families (KPM) every month. In its implementation, BPNT is still encountering a number of obstacles, one of which is in terms of distribution of aid which has not been optimal in several areas, including in Mekar Sari Village, Kec.Pulau Rakyat, Kab. Sharpen. In carrying out the BPNT program, many residents complained that they did not receive this assistance, but they felt they had the right to receive assistance like the others. The aim of the research is to apply the K-Nearest Neighbor algorithm so that it can help the process of classifying data on citizens who are eligible or not eligible to receive non-cash food assistance (BPNT). The method used uses the application of data mining classification techniques with the K-Nearest Neighbor algorithm. Based on the results of implementing the K-Nearest Neighbor data mining algorithm, the results of the system created can predict and help the village government to make decisions and describe residents who are eligible and not eligible for BPNT assistance using data on poor residents recorded in Mekar Sari Village, Kec. People's Island, Kab. Asahan by using the K-Nearest Neighbor algorithm data mining system.
Analysis of the k-Means Method in Clustering Acceptance of PKH Aid in Pulau Rakyat Tua Village Utami, Dwi Kurnia; Irawati, Novica; Sumantri, Sumantri
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3236

Abstract

The Family Hope Program (PKH) is a program that provides cash assistance to Very Poor Households (RSTM) which are required to fulfill requirements related to efforts to improve the quality of human resources. In selecting residents to be recipients of the Family Hope Program (PKH) in Pulau Rakyat Tua Village, the problem that often arises is that the provision of Family Hope Program assistance is often considered not to be on target. In addition, errors often occur because the selection is still done manually and requires a long time in selecting participants, which can be influenced by the objective assessment of PKH companions. The research objective is to apply the k-means clustering algorithm in selecting prospective beneficiaries of the Family Hope Program (PKH). The method used uses the application of data mining with the k-means clustering algorithm. Based on the results of applying the k-means clustering algorithm, the results of the system being built can make it easier to select potential recipients of Family Program assistance. The results of the k-means clustering algorithm test produced Cluster 1 in the Eligible category totaling 29 PKH beneficiary data and Cluster 2 in the Ineligible category totaling 1 PKH beneficiary data.
Co-Authors andriyani, suci Arhani, Riza Arridha Zikra Syah Astuti, Bela Asyahri Hadi Nasyuha Damanik, Rissa Azzahra Edi Kurniawan EKA RAHAYU Handayani, Masitah Helmiah, Fauriatun Jeperson Hutahaean Johny, Johny Larasati, Mustika Fitri Latifah Hanum Latiffani, Chitra Maulana, Alfarizi Maulana, Cecep Muhazir, Ahmad Muthia Dewi Nabila, Andini Nata, Andri Nofitri, Rika Pratiwi, Radila Rahman, Arief Fathur Ramadhani, Winda Ayu Ricki Ananda Rika Nofitri Saragih, Sisca Sri Dewi Sari, Ade Mutiara Sarmila, Sarmila Siagian, Yessica Simargolang, Muhammad Yasin Sinaga, Hommy Dorthy Ellyany Sumantri Sumantri Syafnur, Afdhal Syahillah, Bella Umbari Putri, Lia Utami, Dwi Kurnia Utami, Lola Citra Wulandari, Naiyla Yustria Handika Siregar Zannah, Nur Zulfi Azhar