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All Journal Repeater: Publikasi Teknik Informatika dan Jaringan
Eninta Rahayu Barus
STMIK Kaputama
Computer Science & IT

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Pengelompokan Data Penerima Bantuan untuk Disabilitas di Kota Binjai Menggunakan Metode Clustering Algoritma K-Means Eninta Rahayu Barus; Novriyenni Novriyenni; Suci Ramadani
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 2 No. 4 (2024): Oktober: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v2i4.207

Abstract

In Indonesia, people with disabilities are often overlooked and underestimated because they do not have perfect physical abilities to do certain jobs or activities. The majority of them come from underprivileged families and are often underdeveloped. The unstructured process of distributing assistance can result in the assistance provided is not in accordance with the needs, so it is not optimal in improving the welfare of persons with disabilities. In addition, without a clear grouping, it is difficult for the government to design a more specific and targeted assistance program. Therefore, to overcome this problem, the agency needs to have an additional system to be able to assist in overcoming the problem of disability assistance recipients, namely by using the clustering method to group beneficiary data based on age, type of disability, and type of assistance. Thus, this clustering is expected to provide information and a clearer picture of the needs of each disability group, so that the assistance program provided can be distributed more optimally according to what people with disabilities need. After calculating using the existing cluster formula4, iteration 2 is the same as in iteration 1 and there is no data that moves groups anymore so the calculation can be stopped. So that a cluster graph can be made grouping data on beneficiaries of assistance for disabilities in Binjai City using the K-Means algorithm clustering method.
Co-Authors Novriyenni Novriyenni suci ramadani