Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Rumah Layak Atau Tidak Layak Huni (Studi Kasus: Desa Bulu Kecamatan Kraksaan Kabupaten Probolinggo)

Deniyanto Muchlizin Wahidillah; Abu Tholib; Muafi Muafi

doi:10.35316/jimi.v7i2.75-84

Deniyanto Muchlizin Wahidillah Universitas Nurul Jadid
Abu Tholib Universitas Nurul Jadid
Muafi Muafi Universitas Nurul Jadid

10.35316/jimi.v7i2.75-84

Abstract

The house is a need that must be met, in addition to food and clothing needs, as well as an indicator of community welfare, to create a safe, comfortable and healthy living environment. The government provides social rehabilitation assistance to the community for uninhabitable houses by helping to buy building materials so they can rehabilitate homes that are not proper. In Bulu Village there are still many places to live that are categorized as uninhabitable houses. This is because of the community's income factor and limited knowledge about the function of the house, as well as facilities and infrastructure that make it increasingly difficult to realize livable dwellings. This research uses the K-Nearest Neighbor algorithm for the classification of decent or uninhabitable homes which aims to provide convenience in determining prospective recipients of social rehabilitation assistance for uninhabitable houses with accurate results and minimizing mistargeting. The results of this study are an average accuracy of 96.25% with a standard deviation of 5.73% in the 7th model test using 10-fold cross-validation with odd k and validation and evaluation of results with the confusion matrix getting an accuracy value of 100%, the precision value is 100%, the recall value is 100%, and the f-measure value is 100% with k = 13.

Downloads

Download data is not yet available.

References

A. Muhidin and I. Baragigiratri, “Pemetaan Penduduk Calon Penerima Bantuan Renovasi Rumah Desa Pesangkalan Menggunakan Algoritma Clustering K-Means,” J. SIGMA, vol. 9, no. 3, pp. 75–82, Mar. 2019.

M. M. Baharuddin, H. Azis, and T. Hasanuddin, “Analisis Performa Metode K-Nearest Neighbor Untuk Identifikasi Jenis Kaca,” Ilk. J. Ilm., vol. 11, no. 3, pp. 269-274, Dec. 2019.

R. Hasanah, M. Hasan, W. Pangesti, F. Wati, and W. Gata, “Klasifikasi Penerima Dana Bantuan Desa Menggunakan Metode KNN (K-Nearest Neighbor),” Techno, vol. 16, no. 1, pp. 1-6, Mar. 2019.

S. Diansyah, “Klasifikasi Tingkat Kepuasan Pengguna dengan Menggunakan Metode K-Nearest Neighbour (KNN),” J. Sistim Inf. Dan Teknol., pp. 7–12, Feb. 2022.

R. Wahyudi, M. Orisa, and N. Vendyansyah, “Penerapan Algoritma K-Nearest Neighbors Pada Klasifikasi Penentuan Gizi Balita (Studi Kasus Di Posyandu Desa Bluto),” JATI J. Mhs. Tek. Inform., vol. 5, no. 2, pp. 750-757, Oct. 2021.

R. K. Dinata, H. Akbar, and N. Hasdyna, “Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus,” Ilk. J. Ilm., vol. 12, no. 2, pp. 104-111, Aug. 2020.

P. Soleh, A. Tholib and M. N. F Hidayat, “Penerapan Data Mining Untuk Analisa Pola Pembelian Produk Menggunakan Algoritma Frequent Pattern–Growth,” Rekayasa, vol. 14, no. 3, pp. 456-460, 2021.

F. N. Fajri, A. Tholib and W Yuliana, “Application of Machine Learning Algorithm for Determining Elective Courses in Informatics Study Program,” J. Tek. Inform. Dan Sist. Inf., vol. 8, no. 3, pp. 485-496, Dec. 2022.

K. Malik, Y. Pratama, and K. Nisa’, “Diagnosa Pharyngitis Menggunakan Metode K-Nearest Neighbor (K-NN) di Puskesmas Leces Probolinggo,” TRILOGI J. Ilmu Teknol. Kesehat. Dan Hum., vol. 2, no. 3, pp. 199-206, Dec. 2021.

S. Sudriyanto, R. Rizaldi, and M. A. R. Hariri, “Implementasi Algoritme Decision Tree (C4. 5) dengan Optimize Weights (PSO) untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu,” J. Informatika, vol. 6, no. 2, pp. 252-257, Jun. 2021.