The Application of the K-Nearest Neighbor (KNN) Method to Determine House Locations in the Batuphat and Tambon Tunong Areas, Aceh

Abil Khairi; Irgi Fahrezi; Irfan Sahputra; Said Fadlan Anshari

doi:10.29103/jacka.v1i1.14531

The Application of the K-Nearest Neighbor (KNN) Method to Determine House Locations in the Batuphat and Tambon Tunong Areas, Aceh

Abil Khairi, Irgi Fahrezi, Irfan Sahputra, Said Fadlan Anshari

Abstract

This study aims to apply the K-Nearest Neighbors (KNN) method to find the location of a house situated precisely on the border between Batuphat and Tambon Tunong. The issue faced by the college friends is the difficulty in determining whether the house falls within the Batuphat or Tambon Tunong area. The KNN method is used due to its ability to classify based on the nearest neighbors' distance.The data used in this research includes information on the house's location and the Batuphat and Tambon Tunong areas. The training process is conducted to form the KNN model based on the known location data, while the testing process is employed to classify the unknown house location into either the Batuphat or Tambon Tunong area.The results of the study demonstrate that the KNN method can be utilized to determine the location of a house situated on the border between Batuphat and Tambon Tunong. By considering the nearest neighbors' distance, the house can be classified into one of the areas with a high level of accuracy.This research contributes to providing a solution for college friends who face difficulties in determining the house location on the Batuphat and Tambon Tunong border. The KNN method can serve as an effective tool in addressing this problem. Moreover, this study can serve as a basis for further development in the field of location classification based on the KNN method.

Keywords

K-Nearest Neighbors (KNN) method; house location; Batuphat; Tambon Tunong; classification

Full Text:

PDF (Indonesian)

References

Anshori, L., Regasari, R., & Putri, M. (2018). Implementasi Metode K-Nearest Neighbor untuk Rekomendasi Keminatan Studi ( Studi Kasus : Jurusan Teknik Informatika Univ .... Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(7), 2745–2753.

Dinata, R.K., Adek, R.T., Hasdyna, N., Retno, S. (2023). K-nearest neighbor classifier optimization using purity. AIP Conference Proceedings. 2431(1).

Hasdyna, N., Retno, S. (2022). Machine Learning Approach to Determine the Drug-Prone Areas in Lhokseumawe City, Indonesia. International Journal of Multidiciplinary Research and Analysis. 5(9): 2354-2464.

Hutami, R., & Astuti, E. Z. (2016). Implementasi Metode K-Nearest Neighbor Untuk Prediksi Penjualan Furniture Pada CV. Octo Agung Jepara. Universitas Dian Nuswantoro Semarang.

Mustakim, G. O. F. (2016). Algoritma K-Nearest Neighbor Classification Sebagai Sistem Prediksi Predikat Prestasi Mahasiswa, 13(2), 195–202.

Rahardja, C.A., Juardi, T., Agung, H. (2019). “Implementasi Algoritma K-Nearest Neighbor Pada Website Rekomendasi Laptop,” J. Buana Inform., vol. 10, no. 1, p. 75, 2019, doi: 10.24002/jbi.v10i1.184.

Retno, S., Dinata, R.K., Hasdyna, N. (2023). Evaluasi model data chatbot dalam natural language processing menggunakan k-nearest neighbor. Jurnal CoSciTech (Computer Science and Information Technology. 4(1): 146-153.

Retno, S., Rosnita, L., Anshari, S.F. (2023). Sistem Informasi Pelayanan Cuti Berbasis Web Pada PT Pupuk Iskandar Muda Menggunakan PHP dan MySQL. TECHSI-Jurnal Teknik Informatika, 14(1), 33-41.

Sesilia, N.R, Harsani, P. (2018) .“Penerapan K-Nearest Neighbor ( KNN ) untuk Klasifikasi Anggrek Berdasarkan Karakter Morfologi Daun dan Bunga,” vol. 15, no. 1, pp. 118–125.

Sumarlin, S. (2016). Implementasi Algoritma K-Nearest Neighbor Sebagai Pendukung Keputusan Klasifikasi Penerima Beasiswa PPA dan BBM. Jurnal Sistem Informasi Bisnis, 5(1), 52–62. https://doi.org/10.21456/vol5iss1pp52-62

Suwirmayanti, N. L. G. P. (2017). Penerapan Metode K-Nearest Neighbor Untuk Sistem Rekomendasi Pemilihan Mobil. Techno. Com, 16(2), 120–131.

Tang, Y., Jing, L., Li, H., & Atkinson, P. M. (2016). A multiple-point spatially weighted k-NN method for object-based classification. International Journal of Applied Earth Observation and Geoinformation, 52, 263–274. https://doi.org/10.1016/j.jag.2016.06.017

Tharwat, A., Mahdi, H., Elhoseny, M., & Hassanien, A. E. (2018). Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm. Expert Systems with Applications, 107, 32–44. https://doi.org/10.1016/j.eswa.2018.04.017

Yahya, Y and Hidayanti, W.P. (2020). “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Efektivitas Penjualan Vape (Rokok Elektrik) pada ‘Lombok Vape On,’” Infotek J. Inform. dan Teknol., vol. 3, no. 2, pp. 104–114, 2020, doi: 10.29408/jit.v3i2.2279.

DOI: https://doi.org/10.29103/jacka.v1i1.14531