Klasterisasi Data Penanganan dan Pelayanan Kesehatan Masyarakat dengan Algoritma K-Means
DOI:
https://doi.org/10.30865/jurikom.v9i5.4882Keywords:
Data Mining, K-Means, Health ServicesAbstract
Quality public health services are one of the characteristics of the country's successful development in the health sector. The Health Office has formulated a number of methods to determine the level of progress of health development at the center to the sub-districts. Every year the Lubuk Pakam Health Office collects public health data for processing so that it produces a ranking of regions with the predicate of healthy districts/cities. Data mining is a process used to extract and identify useful information and obtain some important information from data in analyzing public health data. Furthermore, the algorithm that will be used for data mining management in the case of analyzing public health data and used for cluster formation is the K-Means algorithm. The results obtained in the data grouping there are categories of patient assessment levels Very Satisfied, Satisfied, and Dissatisfied. From the results of the K-Means method, it can be concluded to improve services and health care as for the results of grouping the level of satisfaction
References
A. N. Yusril, I. Larasati, and Q. Aini, “Implementasi Text Mining Untuk Advertising Dengan Menggunakan Metode K-Means Clustering Pada Data Tweets Gojek Indonesia,†Sistemasi, vol. 9, no. 3, p. 586, 2020, doi: 10.32520/stmsi.v9i3.924.
M. Sholeh, M. A. Fauzi, and S. Adinugroho, “Implementasi Metode Text Mining dan K-Means Clustering untuk Pengelompokan Dokumen Skripsi ( Studi Kasus : Universitas Brawijaya ),†vol. 2, no. 11, pp. 5518–5524, 2018.
Y. Brasilka, E. Ernawati, and ..., “Klasifikasi citra batik besurek Berdasarkan ekstraksi fitur tekstur Menggunakan jaringan syaraf tiruan Self organizing map (som),†Rekursif J. …, vol. 3, no. 2, pp. 132–145, 2015.
Zulham and Asyahri Hadi Nasyuha, “Penerapan Data Mining Untuk Pengelompokan Wahana,†vol. 17, no. 1, pp. 92–104, 2018.
M. Z. Hossain, M. N. Akhtar, R. B. Ahmad, and M. Rahman, “A dynamic K-means clustering for data mining,†Indones. J. Electr. Eng. Comput. Sci., vol. 13, no. 2, pp. 521–526, 2019, doi: 10.11591/ijeecs.v13.i2.pp521-526.
P. Subarkah, E. P. Pambudi, S. Oktaviani, and N. Hidayah, “Perbandingan Metode Klasifikasi Data Mining untuk Nasabah Bank Telemarketing,†vol. 20, no. 1, 2020, doi: 10.30812/matrik.v20i1.826.
D. Hartama, A. Perdana Windarto, and A. Wanto, “The Application of Data Mining in Determining Patterns of Interest of High School Graduates,†J. Phys. Conf. Ser., vol. 1339, no. 1, p. 012042, Dec. 2019, doi: 10.1088/1742-6596/1339/1/012042.
M. J. Hamid Mughal, “Data mining: Web data mining techniques, tools and algorithms: An overview,†Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 6, pp. 208–215, 2018, doi: 10.14569/IJACSA.2018.090630.
A. R. Riszky and M. Sadikin, “Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan Data Mining using Apriori Algorithm for Product Recommendation for Customers,†vol. 7, no. December 2018, pp. 103–108, 2019, doi: 10.14710/jtsiskom.7.3.2019.103-108.
K. Dharmarajan and M. A. Dorairangaswamy, “Analysis of FP-growth and Apriori algorithms on pattern discovery from weblog data,†2016 IEEE Int. Conf. Adv. Comput. Appl. ICACA 2016, pp. 170–174, 2016, doi: 10.1109/ICACA.2016.7887945.
D. A. Muthia, “Komparasi Algoritma Klasifikasi Text Mining Untuk Analisis Sentimen Pada Review Restoran,†J. PILAR Nusa Mandiri, vol. 14, no. 1, pp. 69–74, 2018, doi: 10.31294/jtk.v4i2.3461.
Z. Pan, Y. Wang, and W. Ku, “A new k-harmonic nearest neighbor classifier based on the multi-local means,†Expert Syst. Appl., vol. 67, pp. 115–125, Jan. 2017, doi: 10.1016/j.eswa.2016.09.031.
A. H. Nasyuha, Z. Zulham, and I. Rusydi, “Implementation of K-means algorithm in data analysis,†TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 20, no. 2, p. 307, Apr. 2022, doi: 10.12928/telkomnika.v20i2.21986.
M. A. Syakur, B. K. Khotimah, E. M. S. Rochman, and B. D. Satoto, “Integration K-Means Clustering Method and Elbow Method for Identification of the Best Customer Profile Cluster,†IOP Conf. Ser. Mater. Sci. Eng., vol. 336, no. 1, 2018, doi: 10.1088/1757-899X/336/1/012017.
C. Yuan and H. Yang, “Research on K-Value Selection Method of K-Means Clustering Algorithm,†J, vol. 2, no. 2, pp. 226–235, 2019, doi: 10.3390/j2020016.
Y. P. Raykov, A. Boukouvalas, F. Baig, and M. A. Little, “What to do when K-means clustering fails: A simple yet principled alternative algorithm,†PLoS One, vol. 11, no. 9, p. e0162259, Sep. 2016, doi: 10.1371/journal.pone.0162259.
U. Stemmer, “Locally Private k -Means Clustering,†in Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, vol. 148, Philadelphia, PA: Society for Industrial and Applied Mathematics, 2020, pp. 548–559.
