Classification of Population Data of Nagari Based on Economic Level Using The K-Nearest Neighbor Method

Ainil Mardiah (1), Defni Defni (2), Aster Happy Lestari (3), Junaldi Junaldi (4), Titin Ritmi (5)
(1) Politeknik Negeri Padang, Kampus Limau Manis, Padang, 25164, Indonesia
(2) (Scopus ID: 57222636603); Politeknik Negeri Padang, Sumatera Barat, Indonesia
(3) Politeknik Negeri Padang, Kampus Limau Manis, Padang, 25164, Indonesia
(4) Politeknik Negeri Padang, Kampus Limau Manis, Padang, 25164, Indonesia
(5) Politeknik Negeri Padang, Kampus Limau Manis, Padang, 25164, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Mardiah, A., Defni, D., Lestari, A. H., Junaldi, J., & Ritmi, T. (2024). Classification of Population Data of Nagari Based on Economic Level Using The K-Nearest Neighbor Method. International Journal of Advanced Science Computing and Engineering, 6(1), 33–37. https://doi.org/10.62527/ijasce.6.1.191

The process of collecting data and classifying the level of economic status of the residents of Nagari is currently manual, so the efficiency in data collection is less than ideal. As a result, the population of Nagari is not well controlled by government authorities due to the lack of detailed government supervision. Therefore, a classification application system is needed that can overcome these problems. In making a classification application system, it will be analyzed using the K-Nearest Neighbor method for grouping the economic level of the community, where data from each occupant of Nagari is used as criteria. It is hoped that this classification application can facilitate the grouping of the economic class of the population, so that the regent of village  can make decisions on assistance from the system.

. A. Khairi, A. F. Ghozali and A. D. N. Hidayah, " implementation of K-Nearest Neighbor (KNN) for the classification of underprivileged people in Sapikerapp Village, Sukarapu Sub-District," Journal TRILOGII, vol. 2, no. 3, pp. 319-323, 2021.

. P. S. Saputra, " comparison of Fuzzy C-Means algorithm and Naive Bayes algorithm in determining beneficiary families (KPM) based on the lowest socioeconomic Status (SSE)," Journal of Science and Technology (Ann), vol. 10, no. 1, pp. 1-8, 2021.

. Y. Filki, " K-Means Clustering algorithm in predicting recipients of Village Fund Direct Cash Assistance (BLT)," Journal of Business Economics Informatics, vol. 4, no. 4, pp. 166-171, 2022.

. R. M. A. Thousands Of People, E. Bu'ulolo and S. A. Hutabarat, "K-Nearest Neighbor Clustering algorithm in Medan Area Sub-District Community grouping based on Family Economic level," KOMIK (National Conference of Information and Computer Technology), vol. 6, no. 1, pp. 773-782, 2022.

. A.- N. S. Na'iema, H. Cozy and N. A. Widiastuti, "classification of homeless rehabilitation program beneficiaries using k-Nearest Neighbor algorithm," Journal of Technology and Computer Systems, vol. 10, no. 1, pp. 32-37, 2022.

. P. Son, A. M. H. Pardede and S. Syahputra, " analysis of the K-Nearest neighbor (KNN) method in the classification of Iris Data," JTIK (Journal of Information Engineering Kaputama), vol. 6, no. 1, pp. 297-305, 2022.

. F. D. Son, J. Riyanto and A. F. Zulfikar, "Asset Management Information System Design at Pamulang University WEB-Based," Journal of Engineering, Technology, and Applied Science, vol. 2, no. 1, pp. 32-50, 2020.

. H. and R., "Effect of User Profiling on system recommendations using K Means and KNN," Journal of Information System Management (JOISM), vol. 2, no. 1, pp. 13-18, 2020.

. L. Farokhah, "K-Nearest Neighbor implementation for Flower classification by RGB color Feature Extraction," Journal of Information Technology and Computer Science, vol. 7, no. 6, pp. 1129-1136, 2020.

. R. Y. Endra, Y. April and Y. Yanu, "comparative analysis of PHP Programming Language Laravel with PHP Native in Website development," EXPERT: Journal of Information Systems Management and Technology, vol. 11, no. 1, p. 48, 2021.

. S. Manish and G. Parul, “A Review on Analysis of K-Nearest Neighbor Classification Machine Learning Algorithms based on Supervised Learning”, International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 43-48, 2022.

. M. Bansal, A. Goyal and A. Choudhary, “A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning”, Decision Analytics Journal, vol. 3, pp. 1-21, 2022.

. S. Maohua and Y. Ruidi, “An efficient secure k nearest neighbor classification protocol with high-dimensional features”, International Journal of Intelligent System, vol. 35, no. 11, pp. 1791-1813, 2020.

. S. R. Cholil, T. Handayani, R. Prathivi and T. Ardianita, “Implementation of K-Nearest Neighbor (KNN) Classification Algorithm for Scholarship Recipient Selection Classification”, Indonesian Journal on Computer and Information Technology (IJCIT), vol. 6, no. 2, pp. 118-127, 2021.

. V. A. Prilia Putri, A. B. Prasetijo and D. Eridani, “Comparison of the Performance of the Naive Bayes and K-Nearest Neighbor (KNN) Algoritm for House Price Prediction”, Scientific Journal of Electrical Engineering, vol. 24, no. 4, pp. 162-171, 2022.