Analyzing the Distribution of Health Workers in Semarang City Using K-Means Clustering Method
DOI:
https://doi.org/10.51519/journalisi.v6i1.663Keywords:
K-Means Clustering, Health, Community, Distribution, Health WorkersAbstract
This research employed the K-Means Clustering method to examine the distribution of health workers in Semarang City, emphasizing their pivotal role in the public health infrastructure. Leveraging current data encompassing health worker locations and quantities, the clustering analysis discerned areas exhibiting similar distribution characteristics through the application of the K-Means technique. Quantitative analysis revealed distinct clusters, shedding light on the spatial patterns of health workforce dispersion within Semarang City. The study's quantitative findings furnish valuable insights crucial for formulating more efficacious health policies. By delineating the utility of the K-Means Clustering method in public health planning and providing quantitative evidence of health worker distribution, this research substantially augments geographical comprehension in the examined region.
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References
World Health Organization, "Health for the World's Adolescents: A Second Chance in the Second Decade," Geneva: World Health Organization Department of Noncommunicable Disease Surveillance, 2017.
D. Ratna Sari et al., "Application of the Naive Bayes Method in Predicting Student Satisfaction with Method Lecturer Teaching," Proceedings of the National Information Science Research Seminar (SENARIS), September 2019, p. 287.
B. Serasi Ginting and M. Simanjuntak, "Grouping Diseases in Patients Based on Age Using the K-Means Clustering Method (Case Study: Bahorok Health Center)," ALGORITHM: Journal of Computer Science and Informatics, vol. 6341, November 2021.
N. Nugroho and F.D. Adhinata, "Use of K-Means and K-Means++ Methods for Clustering Covid-19 Data on the Island of Java," Teknika, vol. 11, no. 3, pp. 170–179, 2022, doi: 10.34148/teknika.v11i3.502.
I. Nisa et al., "K-Means Cluster Analysis of Health Workers in Banten Province," J. Science and Math. Unpam, vol. 5, no. 2, pp. 63–71, 2022.
D.K. Sitinjak, B.A. Pangestu, and B.N. Sari, "Clustering Health Workers Based on Districts in Karawang Regency Using the K-Means Algorithm," J. Appl. Informatics Comput., vol. 6, no. 1, pp. 47–54, 2022, doi: 10.30871/jaic.v6i1.3855.
B.L. Pailan et al., "Analysis of Health Personnel Needs Using," Sakti - Science, Appl. Computing and Technology. Inf., vol. 3, no. 1, pp. 1–9, 2021.
M. Zulfadhilah, Mambang, and S. Eka Prastya, "Implementation of the K-Means Clustering Method to Improve Student Networking," Thematic, vol. 9, no. 2, pp. 152–160, 2022, doi: 10.38204/tematic.v9i2.1053.
R. Bayu Prasetyo, Y.A. Pranoto, and R.P. Prasetya, "Implementation of Data Mining Using the K-Means Clustering Algorithm for Outpatient Diseases at Dr. Clinic. Atirah, Sioyong Village, Central Sulawesi," ITN, 2023.
R. Muliono and Z. Sembiring, "Data Mining Clustering Using the K-Means Algorithm for Lecturer Teaching Tridharma Level Clustering," CESS (Journal of Computer Engineering, Systems and Science), vol. 4, no. 2, 2502–2714, 2019.
G.P. Abdillah, "Application of Customer Water Use Data Mining to Determine the Potential Classification of New Customer Water Use at PDAM Tirta Raharja Using the K-Means Algorithm," Sentika, vol. I, p. 498, 2019.
S. Hendrian, "Data Mining Classification Algorithm to Predict Students in Receiving Educational Financial Assistance," Exacta Factors, vol. 11, no. 3, pp. 266–274, 2018.
C.H. Cheng and Y.S. Chen, "Classifying the segmentation of customer value via RFM model and RS theory," Expert Systems with Applications, vol. 36, no. 3, pp. 4176–4184, 2019.
K. Khalili-Damghani, F. Abdi, and S. Abolmakarem, "Solving customer insurance coverage recommendation problem using a two-stage clustering-classification model," International Journal of Management Science and Engineering Management, vol. 14, no. 1, 2019, doi: 10.1080/17509653.2018.1467801.
Sugiyono, Combination Research Methods (Mix Methods), Bandung: Alphabeta, 2018.
R.S. Pressman, Software Engineering: A Practitioner's Approach, Yogyakarta: Andi
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