Algoritma K-Nearest Neighbor Untuk Menentukan Kelayakan Keluarga Penerima Bantuan Pangan Non Tunai (Studi Kasus : Kelurahan Karya Jaya)

Sastri Yani; Fithri Selva Jumeilah; Muhamad Kadafi

doi:10.51519/journalita.volume1.isssue2.year2020.page75-87

Authors

Sastri Yani UIN Raden Fatah Palembang
Fithri Selva Jumeilah UIN Raden Fatah Palembang
Muhamad Kadafi UIN Raden Fatah Palembang

DOI:

10.51519/journalita.volume1.isssue2.year2020.page75-87

Keywords:

Feasibility, BPNT, Data Mining, K-Nearest Neighbor

Abstract

Non-cash Food Assistance (BPNT) is food social assistance in the form of non-cash. In its implementation, this program still encounters a number of obstacles, one of which is in the sub-optimal distribution of aid in several regions, including Karya Jaya Village. This is because the Ministry of Social Affairs is not optimal in determining BPNT recipients. One way to solve this problem is by utilizing one of the data mining concepts, namely the classification technique with the K-Nearest Neighbor algorithm. Where KPM data previously only accumulated can be used as useful information, one of which is to predict the eligibility of BPNT recipients in the next period. The results of this research are in the form of information on the results of predictions of appropriate KPM as BPNT recipients in 2021 and Local Environmental Units (SLS) which are the most receiving regions. This information can be used as evaluation material for the Ministry of Social Affairs in determining the more targeted BPNT recipients. The prediction results of BPNT recipients in Karya Jaya Village in 2021 are 511 recipients with an accuracy rate of 75.79%, 76.17% Precision, 89.24% Recall, and 82.19% F-measure. And it can be seen that the most BPNT recipient categories are in SLS RW 005, namely 74 recipients. Where there are variables that most influence, namely sta_kis

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