Classification of Determination the Recipients of the Program Keluarga Harapan (PKH) Using K-Nearest Neighbor Algorithm

Fajriana Fajriana

doi:10.31289/jite.v6i1.7543

Classification of Determination the Recipients of the Program Keluarga Harapan (PKH) Using K-Nearest Neighbor Algorithm

Authors

Fajriana Fajriana

DOI:

10.31289/jite.v6i1.7543

Published:

2022-07-23

Issue:

Vol. 6 No. 1 (2022): Issues July 2022

Keywords:

Classification, Data Mining, K-NN, System, Web

Articles

Downloads

PDF

Downloads

Download data is not yet available.

Abstract

Classification is one method of data mining techniques for classifying data by system according to predetermined rules. In this study, the algorithm used was K-Nearest Neighbor (KNN) algorithm and the data used in this study was the community data obtained from Kantor Desa Gampong Uteun Geulinggang, Dewantara, in Aceh Utara. The main focus of this study was to analyzing and applying K-NN algorithm in a web-based system to classify data on beneficiaries of the Program Keluarga Harapan (PKH) in Gampong Uteun Geulinggang, Dewantara, Aceh Utara. This study used 16 (sixteen) criterias, namely house status, floor area, floor type, gas cylinder, refrigerator, air conditioner, water heater, television, jewelry/gold (10 grams), computer/laptop, bicycle, motorcycle, car, cow, buffalo and goat. The data was classified into 3 classes, namely worthy, not unworthy and very unworthy. The results of this study with a value of k = 3, obtained a precision value of 97%, recall value of 95% and the accuracy value of 97%.

References

Atma, Y. D., & Setyanto, A. (2018). Perbandingan Algoritma C4. 5 Dan K-Nn Berbasis Fitur Seleksi Forward Selection Dalam Identifikasi Mahasiswa Berpotensi Drop Out. Metik Jurnal, 2(2), 31-37.

Bhosle, U., & Deshmukh, J. (2019). Mammogram classification using AdaBoost with RBFSVM and Hybrid KNNâ€“RBFSVM as base estimator by adaptively adjusting Î³ and C value. International Journal of Information Technology, 11(4), 719-726.

Cherif, W. (2018). Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis. Procedia Computer Science, 127, 293-299.

Dinata, R. K., Akbar, H., & Hasdyna, N. (2020). Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus. ILKOM Jurnal Ilmiah, 12(2), 104-111.

Dinata, R. K., Fajriana, F., Zulfa, Z., & Hasdyna, N. (2020). Klasifikasi Sekolah Menengah Pertama/Sederajat Wilayah Bireuen Menggunakan Algoritma K-Nearest Neighbors Berbasis Web. CESS (Journal of Computer Engineering, System and Science), 5(1), 33-37.

Hasdyna, N., Sianipar, B., & Zamzami, E. M. (2020). Improving The Performance of K-Nearest Neighbor Algorithm by Reducing The Attributes of Dataset Using Gain Ratio. In Journal of Physics: Conference Series (Vol. 1566, No. 1, p. 012090). IOP Publishing.

Maleki, N., Zeinali, Y., & Niaki, S. T. A. (2021). A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection. Expert Systems with Applications, 164, 113981.

Mezquita, Y., Alonso, R. S., Casado-Vara, R., Prieto, J., & Corchado, J. M. (2020, June). A review of k-nn algorithm based on classical and quantum machine learning. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 189-198). Springer, Cham.

Nasution, D. A., Khotimah, H. H., & Chamidah, N. (2019). Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN. CESS (Journal of Computer Engineering, System and Science), 4(1), 78-82.

Pertiwi, I. P., Fedinandus, F. X., & Limantara, A. D. (2019). Sistem Pendukung Keputusan Penerima Program Keluarga Harapan (PKH) Menggunakan Metode Simple Additive Weighting. CAHAYAtech, 8(2), 182-195.

Sandag, G. A., Leopold, J., & Ong, V. F. (2018). Klasifikasi Malicious Websites Menggunakan Algoritma K-NN Berdasarkan Application Layers dan Network Characteristics. CogITo Smart Journal, 4(1), 37-45.

Shah, K., Patel, H., Sanghvi, D., & Shah, M. (2020). A comparative analysis of logistic regression, random forest and KNN models for the text classification. Augmented Human Research, 5(1), 1-16.

Su, Z. G., Hu, Q., & Denoeux, T. (2020). A distributed rough evidential K-NN classifier: integrating feature reduction and classification. IEEE Transactions on Fuzzy Systems, 29(8), 2322-2335.

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.

Waliyansyah, R. R., & Fitriyah, C. (2019). Perbandingan Akurasi Klasifikasi Citra Kayu Jati Menggunakan Metode Naive Bayes dan k-Nearest Neighbor (k-NN). JEPIN (Jurnal Edukasi dan Penelitian Informatika), 5(2), 157-163.

License

This work is licensed under aCreative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).

Classification of Determination the Recipients of the Program Keluarga Harapan (PKH) Using K-Nearest Neighbor Algorithm

Authors

DOI:

Published:

Issue:

Keywords:

Articles

Downloads

Downloads

Abstract

References

License

Most read articles by the same author(s)

MENU

Official Website of
UMA Open Access Journals

Contact Us

Information

Share & Follow