DOI: https://doi.org/10.14710/jtsiskom.2021.14110

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.
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Keywords: K-Nearest Neighbor; Euclidean distance; k-fold cross validation; house rehabilitation program
Funding: Universitas Islam Nahdlatul Ulama

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Language : ID
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  1. M. Ismowati and A. Subhan, “Implementasi program pemberdayaan sosial kegiatan rehabilitasi sosial rumah tidak layak huni (RTLH) bagi masyarakat miskin di kabupaten Pandeglang,” Transparansi Jurnal Ilmiah Ilmu Administrasi, vol. 1, no. 2, pp. 194–205, 2018. doi: 10.31334/trans.v1i2.384
  2. M. A. Nurhakim and E. Pandamdari, “Pemenuhan atas sarana dan utilitas pada perumahan subsidi Mutiara Puri Harmoni Rajeg Tangerang menurut undang-undang nomor 1 tahun 2011 tentang perumahan dan permukiman,” Jurnal Hukum Adigama, vol. 1, no. 1, pp. 437-461, 2018. doi: 10.24912/adigama.v1i1.2152
  3. T. Sugihartono, “Implementasi sistem pendukung keputusan penerima bantuan rumah tidak layak huni berbasis web,” Jurnal Sistem Informasi dan Komputer, vol. 7, no. 1, pp. 52-56, Mar. 2018. doi: 10.32736/sisfokom.v7i1.299
  4. J. Han, M. Kamber, and J. Pei, Data Mining : Concept and Techniques. San Francisco: Elsevier, 2012, pp. 1–38
  5. D. A. Adeniyi, Z. Wei, and Y. Yongquan, “Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method,” Applied Computing and Informatics, vol. 12, no. 1, pp. 90–108, 2016. doi: 10.1016/j.aci.2014.10.001
  6. F. J. Simatupang and T. Wuryandari, “Klasifikasi rumah layak huni di kabupaten Brebes dengan menggunakan metode learning vector quantization dan naive bayes,” Jurnal Gaussian, vol. 5, no. 1, pp. 99–111, 2016
  7. R. R. P. Putri, M. T. Furqon, and B. Rahayudi, “Implementasi metode JST-Backpropagation untuk klasifikasi rumah layak huni (Studi kasus desa Kidul kecamatan Tumpang kabupaten Malang),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 10, pp. 3360–3365, 2018
  8. B. Agustina, W., Furqon, M., & Rahayudi, “Implementasi metode support vector machine (SVM) untuk klasifikasi rumah layak huni (Studi kasus desa Kidul kecamatan Tumpang abupaten Malang),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 10, pp. 3366–3372, 2018
  9. D. I. Permatasari, “Klasifikasi status ekonomi keluarga dengan menggunakan algoritma k-nearest neighbor di desa Pacewetan kecamatan Pace kabupaten Nganjuk,” Simki-Techsain, vol. 1, no. 1, pp. 1-10, 2017
  10. S. Sugriyono and M. U. Siregar, “Preprocessing kNN algorithm classification using K-means and distance matrix with students’ academic performance dataset,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 4, pp. 311–316, 2020. doi: 10.14710/jtsiskom.2020.13874
  11. D. A. Nasution, H. H. Khotimah, and N. Chamidah, “Perbandingan normalisasi data untuk klasifikasi wine menggunakan algoritma K-NN,” Computer Engineering, Science and System Journal, vol. 4, no. 1, pp. 78-82, 2019. doi: 10.24114/cess.v4i1.11458
  12. S. Kapil and M. Chawla, “Performance evaluation of K-means clustering algorithm with various distance metrics,” in International Conference on Power Electronics, Intelligent Control and Energy Systems, Delhi, India, Jul. 2016, pp. 1–4. doi: 10.1109/ICPEICES.2016.7853264
  13. S. A. Salihu, I. P. Onyekwere, M. A. Mabayoje, and H. A. Mojeed, “Performance evaluation of manhattan and euclidean distance measures for clustering based automatic text summarization,” FUOYE Journal of Engineering and Technology, vol. 4, no. 1, pp. 135-139, 2019. doi: 10.46792/fuoyejet.v4i1.316
  14. R. Feldman and J. Sanger, The text mining handbook: advanced approaches in analyzing unstructured data. England: Cambrige Univ. Press, 2007
  15. E. Nasri and A. Selamet, “Aplikasi seleksi penentuan nasabah untuk penjualan barang secara kredit dengan algoritma k-nearest neighbour,” Jurnal Ilmiah Sains Dan Teknologi, vol. 4, no. 1, pp. 1–11, 2020
  16. M. Lestandy, L. Syafa’ah, and A. Faruq, “Classification of potential blood donors using machine learning algorithms approach,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 3, pp. 217–221, 2020. doi: 10.14710/jtsiskom.2020.13619

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