Pillar Algorithm in K-Means Method for Identification Health Human Resources Availability Profile in Central Java
Abstract
Based on data from the Ministry of Health, the distribution ratio between health workers and patients in Indonesia is still not equal distributed. It influenced by the distribution of health human resources that are not in accordance with the ideal needs of health services. This results need to identify the profile of the availability of health human resources in Indonesia. In this study, an approach will be implemented to identify the profile of health human resources availability using K-Means Clustering with a combination of pillar algorithms in optimizing the selection of the initial cluster centroid. Chi-square analysis is used to determine the disparity in the needs of health human resources with the conditions of the availability of health human resources in the Central Java region. The data collection method used in this research is the observation method, while the scientific method used in this research is the K-Means Clustering method. The results showed that the application has been generated can dynamically determine the health human resource cluster based on the disparity category of health human resource availability in the Central Java region. In addition, the labeling of the Pillar K-Means cluster based on the Chi-square test has a high degree of accuracy, namely 80%.
Keywords
References
[1] Dinas Kesehatan Provinsi Jawa Tengah, “Buku Data Dasar Puskesmas & Rumah Sakit Tahun 2019,” Available: https://dinkesjatengprov.go.id/v2018/wp-content/uploads/2020/09/Buku-Data-Dasar-Puskesmas-dan-RS-2019.pdf, 2020. [Online]. [Accessed: 11-Nov-2020.
[2] A. Kurniati and F. Efendi, Kajian Sumber Daya Manusia Kesehatan di Indonesia, Jakarta: Salemba Medika, 2012.
[3] WHO, “Global Health Workforce statistics database,” https://www.who.int/gho/health_workforce/physicians_ density/en, 2020. [Online]. [Accessed: 23-Nov-2020].
[4] Anindhita Maharrani, “Distribusi tenaga kesehatan tak kunjung merata,” https://lokadata.id/artikel/distribusi-tenaga-kesehatan-tak-kunjung-merata, 2020. [Online]. [Accessed: 18-Nov-2020].
[5] C. D. Manning, An Introduction to Information Retrieval. Cambridge: Cambridge University Press, 2009.
[6] X. Wu et al., “Top 10 algorithms in data mining,” Knowl. Inf. Syst., vol. 14, no. 1, pp. 1–37, 2008.
[7] A. R. Barakbah and Y. Kiyoki, “A pillar algorithm for K-means optimization by distance maximization for initial centroid designation,” IEEE Symposium on Computational Intelligence and Data Mining, pp. 61–68, 2009.
[8] B. B. Bhusare and S. M. Bansode, “Centroids Initialization for K-Means Clustering using Improved Pillar Algorithm,”International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 3, no. 4, pp. 1317–1322, 2014.
[9] O. J. Oyelade, O. O. Oladipupo, and I. C. Obagbuwa, “Application of k Means Clustering algorithm for prediction of Students Academic Performance,” International Journal of Computer Science and Information Security (IJCSIS), vol. 7, pp. 292–295, 2010.
[10] A. R. Barakbah and A. Helen, “Optimized K-means : an algorithm of initial centroids optimization for K-means,” Semin. Soft Comput. Intell. Syst. Inf. Technol., 2005.
[11] Gde Agung Brahmana Suryanegara, Adiwijaya, and Mahendra Dwifebri Purbolaksono, “Peningkatan Hasil Klasifikasi pada Algoritma Random Forest untuk Deteksi Pasien Penderita Diabetes Menggunakan Metode Normalisasi,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 114–122, 2021.
[12] T. Alfina, B. Santosa, and A. R. Barakbah, “Tahta Alfina, Budi Santosa, dan Ali Ridho Barakbah Jurusan Teknik Industri, Fakultas Teknologi Industri, Institut Teknologi Sepuluh Nopember (ITS) Jl. Arief Rahman Hakim, Surabaya 60111,” J. Tek. POMITS, vol. 1, no. 1, pp. 1–5, 2012.
[13] A. Hadi, “Segmentasi Pelanggan Internet Service Provider (ISP) Berbasis Pillar K-Means,” J. Ilm. Teknol. Inf. Asia, vol. 13, no. 2, pp. 151, 2019.
[14] S. K. Dini and A. Fauzan, “Clustering Provinces in Indonesia based on Community Welfare Indicators,” EKSAKTA J. Sci. Data Anal., vol. 1, no. 1, pp. 56–63, 2020.
[15] A. R. Barakbah and Y. Kiyoki, “A pillar algorithm for k-means optimization by distance maximization for initial centroid designation,” 2009 IEEE Symp. Comput. Intell. Data Mining, CIDM 2009 - Proc., pp. 61–68, 2009.
[16] H. Frigui, “Clustering: Algorithms and applications,” 2008 1st Int. Work. Image Process. Theory, Tools Appl. IPTA. 2008.
[17] I. H. Rifa, H. Pratiwi, and R. Respatiwulan, “Clustering of Earthquake Risk in Indonesia Using K-Medoids and K-Means Algorithms,” Media Stat., vol. 13, no. 2, pp. 194–205, 2020.
[18] I. Wahyudin, T. Djatna, and W. A. Kusuma, “Cluster Analysis for SME Risk Analysis Documents Based on Pillar K-Means,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 14, no. 2, pp. 674–683, 2016.
DOI: 10.30595/juita.v9i2.9860
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