Sistem Klasifikasi Kelayakan Penerima Bantuan Langsung Tunai Menggunakan Metode K-Nearest Neighbor (KNN) Berbasis Website
DOI:
https://doi.org/10.47065/jimat.v5i3.702Keywords:
Classification; BLT (Direct Cash Assistance); K-Nearest Neighbor (K-NN)Abstract
Poverty is the condition of a person's inability to fulfill the basic needs of life, which is often measured by income that is lower than the average in a region. In Indonesia, the poverty rate, including in Kabupaten Jombang, continued to increase from 2012 to 2017, with various government efforts to overcome this through social programs such as BLT (Direct Cash Assistance), Community Health Insurance, and the Family Hope Program (PKH). However, despite these programs, the data collection process for beneficiaries in some areas, such as Tanah Periuk Village, is still done manually, causing inaccurate targeting in the provision of assistance. For this reason, a more efficient solution is needed in determining the eligibility of beneficiaries. One of the technologies that can be used is data mining, especially the classification method, to analyze beneficiary data based on certain criteria. This research uses the K-Nearest Neighbor (K-NN) algorithm to build a classification system for the eligibility of direct cash transfer recipients in Tanah Periuk Village, with the aim of improving accuracy and efficiency in the beneficiary selection process. This system is web-based, which allows ease of processing and updating data centrally. The results of this study provide eligibility scores for BLT (Direct Cash Assistance) recipients based on the criteria provided. The criteria that are assessed are: House Condition, Income, Occupation and Number of Dependents. One of the families who “DESERVE” to receive assistance is Martina with semi-permanent house conditions, an income of IDR 1,000,000/month, a housewife's job, and 3 dependents.
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