Grouping of General Hospitals Based on Specialist Doctors in the Special Region of Yogyakarta with the K-Means Clustering and Visualization on the Dashboard Tableau
Keywords:hospitals, K-Means, specialist doctor, Tableau
One of the most needed health workers in hospitals is a specialist doctor. The number of general hospitals in Special Region of Yogyakarta (DIY) is 55, with different characteristics of specialist doctors. Purpose: Information on the grouping of general hospitals based on the characteristics of specialist doctors will help provide information about the distribution of health workers. Methods: This study uses K-Means Clustering as a non-hierarchical clustering method that can group large amounts of data with fast and efficient computation time and is easy to adapt. Results: This study used the number of clusters (k) 2, 3, 4, and 5 and then compared them. Based on the largest silhouette index coefficient, the best number of clusters is k = 2. A total of 35 hospitals in cluster 2 represent hospitals with an adequate number of specialists. A total of 2 hospitals in cluster 2 represent hospitals with a sufficient number of specialists. Visualization using the Tableau dashboard has also provided benefits, namely providing information on the number of specialist doctors in each general hospital, cluster results, and cluster profiling. Implications: The K-Means method can provide a reference for the distribution of specialist doctors in the Special Region of Yogyakarta.
N. Dwitri, J. A. Tampubolon, S. Prayoga, F. I. R. H Zer, and D. Hartama, “Penerapan algoritma K-means dalam menentukan tingkat penyebaran pandemi Covid-19 di Indonesia,” J. Teknol. Inf., vol. 4, no. 1, 2020, doi: 10.36294/jurti.v4i1.1266.
K. P. Sinaga and M. S. Yang, “Unsupervised K-means clustering algorithm,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.2988796.
Y. D. Darmi and A. Setiawan, “Penerapan metode clustering K-means dalam pengelompokan penjualan produk,” J. Medi Infotama, vol. 12, no. 2, 2017, doi: 10.37676/jmi.v12i2.418.
K. Kurniawan and D. Antoni, “Visualisasi data penduduk dalam membangun e-government berbasis Sistem Informasi Geografis (GIS),” J. Sisfokom (Sistem Inf. dan Komputer), vol. 9, no. 3, 2020, doi: 10.32736/sisfokom.v9i3.828.
M. Silvana, R. Akbar, and R. Tifani, “Penerapan dashboard system di Perpustakaan Universitas Andalas menggunakan Tableau Public,” Semin. Nas. Sains dan Teknol. 2017, November, 2017.
I. Hussain, “Outlier detection using graphical and nongraphical functional methods in hydrology,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 12, 2019, doi: 10.14569/ijacsa.2019.0101259.
I. L. Kirilyuk and O. V. Senko, “Assessing the validity of clustering of panel data by Monte Carlo methods (using as example the data of the Russian regional economy),” Comput. Res. Model., vol. 12, no. 6, 2020, doi: 10.20537/2076-7633-2020-12-6-1501-1513.
J. Kokina, D. Pachamanova, and A. Corbett, “The role of data visualization and analytics in performance management: Guiding entrepreneurial growth decisions,” J. Account. Educ., vol. 38, 2017, doi: 10.1016/j.jaccedu.2016.12.005.
N. Damanik and M Sigiro, “Penerapan data mining menggunakan algoritma K-Means clustering pada penerimaan mahasiswa baru sebagai metode promosi,” Jutisal J. Tek. Inform. Univers., 2021.
Y. Setyawan, R. D. Bekti, and F. Isarlin, “Application of SKATER and Ward’s methods in grouping Indonesian provinces based on monthly expenditure per capita of food commodity groups,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 807, no. 1. doi: 10.1088/1757-899X/807/1/012017.
R. D. Bekti, G. E. Dirgantara, and E. Sutanta, “Distance and AMOEBA weights matrices in local getis Ord-G statistics to identify spatial cluster of gini ratio,” 2021. doi: 10.1109/ICERA53111.2021.9538666.
H. Sulastri and A. I. Gufroni, “Penerapan data mining dalam pengelompokan penderita thalassaemia,” J. Nas. Teknol. dan Sist. Inf., vol. 3, no. 2, 2017, doi: 10.25077/teknosi.v3i2.2017.299-305.
R. Sammouda and A. El-Zaart, “An optimized approach for prostate image segmentation using K-Means clustering algorithm with Elbow method,” Comput. Intell. Neurosci., vol. 2021, doi: 10.1155/2021/4553832.