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

An-Naas Shahifatun Na’iema; Harminto Mulyo; Nur Aeni Widiastuti

doi:10.14710/jtsiskom.2021.14110

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

An-Naas Shahifatun Na’iema, Harminto Mulyo

, Nur Aeni Widiastuti

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.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to cite (IEEE): A. S. Na’iema, H. Mulyo, and N. A. Widiastuti, "Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor," Jurnal Teknologi dan Sistem Komputer, vol. 10, no. 1, pp. 32-37, Jan. 2022. https://doi.org/10.14710/jtsiskom.2021.14110

How to cite (APA): Na’iema, A. S., Mulyo, H., & Widiastuti, N. A. (2022). Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor. Jurnal Teknologi dan Sistem Komputer, 10(1), 32-37. https://doi.org/10.14710/jtsiskom.2021.14110

How to cite (BCREC): Na’iema, A. S., Mulyo, H., Widiastuti, N. A. (2022). Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor. Jurnal Teknologi dan Sistem Komputer, 10 (1), 32-37 (doi:10.14710/jtsiskom.2021.14110)

How to cite (Chicago): Na’iema, An-Naas S., Harminto Mulyo, and Nur A. Widiastuti. "Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor." Jurnal Teknologi dan Sistem Komputer 10, no. 1 (2022): 32-37. Accessed April 4, 2025. https://doi.org/10.14710/jtsiskom.2021.14110

How to cite (Vancouver): Na’iema AS, Mulyo H, Widiastuti NA. Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor. Jurnal Teknologi dan Sistem Komputer [Online]. 2022 Jan;10(1):32-37. https://doi.org/10.14710/jtsiskom.2021.14110.

How to cite (Harvard): Na’iema, A. S., Mulyo, H., and Widiastuti, N. A., 2022. Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor. Jurnal Teknologi dan Sistem Komputer, [Online] Volume 10(1), pp. 32-37. https://doi.org/10.14710/jtsiskom.2021.14110 [Accessed 4 Apr. 2025].

How to cite (MLA8): Na’iema, An-Naas Shahifatun, Harminto Mulyo, and Nur Aeni Widiastuti. "Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor." Jurnal Teknologi dan Sistem Komputer, vol. 10, no. 1, 31 Jan. 2022, pp. 32-37 , https://doi.org/10.14710/jtsiskom.2021.14110. Accessed 4 Apr. 2025.

BibTex Citation Data :

@article{JTSISKOM14110,
    author = {An-Naas Shahifatun Na’iema and Harminto Mulyo and Nur Aeni Widiastuti},
    title = {Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor},
    journal = {Jurnal Teknologi dan Sistem Komputer},
  volume = {10},
    number = {1},
    year = {2022},
    keywords = {K-Nearest Neighbor; Euclidean distance; k-fold cross validation; house rehabilitation program},
    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.},
   issn = {2338-0403},   pages = {32--37}  doi = {10.14710/jtsiskom.2021.14110},
    url = {https://jtsiskom.undip.ac.id/article/view/14110}
}

Refworks Citation Data :

@article{{JTSISKOM}{14110}, author = {Na’iema, A., Mulyo, H., Widiastuti, N.}, title = {Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {10}, number = {1}, year = {2022}, doi = {10.14710/jtsiskom.2021.14110}, url = {https://jtsiskom.undip.ac.id/article/view/14110} }

Citation Format:

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.

Fulltext View|Download Email colleagues

Keywords: K-Nearest Neighbor; Euclidean distance; k-fold cross validation; house rehabilitation program

Funding: Universitas Islam Nahdlatul Ulama

Article Metrics:

Article Info

Section: Original Research Articles

Language : ID

In Volume 10, Issue 1, Year 2022 (January 2022)

Most viewed articles

Perancangan Sistem Informasi Akademik Berbasis Mobile Web Studi Kasus di Program Studi Sistem Komputer Universitas Diponegoro Decision Support System for Thesis Graduation Recommendation Using AHP-TOPSIS Method Sistem Monitoring Digital Penggunaan dan Kualitas Kekeruhan Air PDAM Berbasis Mikrokontroler ATMega328 Menggunakan Sensor Aliran Air dan Sensor Fotodiode Pemanfaatan Augmented Reality untuk Pembelajaran Pengenalan Alat Musik Piano Implementasi Algoritma Kriptografi RSA untuk Enkripsi dan Dekripsi Email More articles

Most cited articles

Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi Perancangan Permainan “UangAndro” Sebagai Media Pembelajaran Pengenalan Mata Uang Rupiah Pada Anak Berbasis Android Mamdani fuzzy inference system for mapping water quality level of biofloc ponds in catfish cultivation Automatic Home Roof System on Smarthome by Using Arduino Sistem Pendeteksi Kualitas Daging Dengan Ekualisasi Histogram Dan Thresholding Berbasis Android More cited articles

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
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
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
J. Han, M. Kamber, and J. Pei, Data Mining : Concept and Techniques. San Francisco: Elsevier, 2012, pp. 1–38
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
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
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
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
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
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
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
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
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
R. Feldman and J. Sanger, The text mining handbook: advanced approaches in analyzing unstructured data. England: Cambrige Univ. Press, 2007
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
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

Last update:

No citation recorded.

Last update: 2025-04-02 17:10:28

No citation recorded.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Starting from 2021, the author(s) whose article is published in the JTSiskom journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JTSiskom, the author(s) agree with this policy. No special document approval is required.

The author(s) guarantee that:

their article is original, written by the mentioned author(s),
has never been published before,
does not contain statements that violate the law, and
does not violate the rights of others, is subject to copyright held exclusively by the author(s), is free from the rights of third parties, and the necessary written permission to quote from other sources has been obtained by the author(s).

The author(s) retain all rights to the published work, such as (but not limited to) the following rights:

Copyright and other proprietary rights related to the article, such as patents,
The right to use the substance of the article in its own future works, including lectures and books,
The right to reproduce the article for its own purposes,
The right to archive all versions of the article in any repository, and
The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (Jurnal Teknologi dan Sistem Komputer).

Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JTSiskom will not be held responsible for anything arising because of the writer's internal dispute. JTSiskom will only communicate with correspondence authors.

Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JTSiskom allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JTSiskom to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.

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

EDITORIAL OFFICE OF JURNAL TEKNOLOGI DAN SISTEM KOMPUTER