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Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor

Classification of beneficiaries for the rehabilitation of uninhabitable houses using the K-Nearest Neighbor algorithm

Department of Informatics, Faculty of Science and Technology, Universitas Islam Nahdlatul Ulama. Jl. Taman Siswa (Pekeng) Tahunan, Jepara 59427, Indonesia

Received: 19 Feb 2021; Revised: 29 Jul 2021; Accepted: 20 Jan 2022; Published: 31 Jan 2022.
Open Access Copyright (c) 2022 The authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
The registrars for rehabilitation programs for uninhabitable settlements are increasing every year. The large data processing of registrants may result in inaccuracies and need a long time to determine livable houses (RTLH) and unfit for habitation (non RTLH). This study aims to apply the K-Nearest Neighbor algorithm in classifying the eligibility of recipients of uninhabitable house rehabilitation assistance. The data used in this study were 1289 data with 13 attributes from the Jepara Regency Public Housing and Settlement Service. Data processing begins with attribute selection, categorization, outlier data cleaning, and data normalization and method application. The proposed system has the best classification at k of 5 with an accuracy of 97.93%, 96.88% precision, 99.53% recall, and an AUC value of 0.964.
Keywords: K-Nearest Neighbor; Euclidean distance; k-fold cross validation; house rehabilitation program
Funding: Universitas Islam Nahdlatul Ulama

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