Clustering Kabupaten/Kota di Jawa Tengah Tahun 2022 berdasarkan Jumlah Kasus Kemunculan Penyakit dengan Algoritma K-Means

Nadiya Azhar  Mufid; Derra Risqi Nurulita Hanum; Aldy Hasbiya Sidiq

doi:10.47134/ppm.v1i1.107

Authors

Nadiya Azhar Mufid Universitas Negeri Yogyakarta
Derra Risqi Nurulita Hanum Universitas Negeri Yogyakarta
Aldy Hasbiya Sidiq Universitas Negeri Yogyakarta

DOI:

https://doi.org/10.47134/ppm.v1i1.107

Keywords:

clustering analysis, disease occurrence, k-means algorithm

Abstract

This research aims to conduct clustering or grouping of Regencies/Cities in Central Java Province based on the number of occurrences of specific diseases in 2022 using the K-Means algorithm. The research results obtained 3 clusters, namely high, medium, and low for 29 Regencies and 6 Cities. The percentage for cluster 1 is 34.29%, consisting of 10 regencies and 2 cities, cluster 2 is 40.00%, consisting of 11 regencies and 3 cities, and cluster 3 is 25.71%, consisting of 8 regencies and 1 city. These clustering results can be used as a basis for making effective strategic decisions in the development of prevention and control efforts for diseases in each region.

References

S. Wibowo and I. D. Mulyastuti, “Penerapan Algoritma K-Means Clustering Pada Jumlah Fasilitas Kesehatan Menurut Pemerintah Provinsi DKI Jakarta,” 2022. [Online]. Available: https://jakarta.bps.go.id DOI: https://doi.org/10.37817/tekinfo.v23i2.2603

Abualigah, L. M. (2018). Hybrid clustering analysis using improved krill herd algorithm. Applied Intelligence, 48(11), 4047–4071. https://doi.org/10.1007/s10489-018-1190-6 DOI: https://doi.org/10.1007/s10489-018-1190-6

Afzal, A. (2020). Response surface analysis, clustering, and random forest regression of pressure in suddenly expanded high-speed aerodynamic flows. Aerospace Science and Technology, 107. https://doi.org/10.1016/j.ast.2020.106318 DOI: https://doi.org/10.1016/j.ast.2020.106318

Alam, M. S. (2019). Automatic human brain tumor detection in mri image using template-based k means and improved fuzzy c means clustering algorithm. Big Data and Cognitive Computing, 3(2), 1–18. https://doi.org/10.3390/bdcc3020027 DOI: https://doi.org/10.3390/bdcc3020027

Borlea, I. D. (2021). A Unified Form of Fuzzy C-Means and K-Means algorithms and its Partitional Implementation. Knowledge-Based Systems, 214. https://doi.org/10.1016/j.knosys.2020.106731 DOI: https://doi.org/10.1016/j.knosys.2020.106731

Dinas Kesehatan Provinsi Jawa Tengah, “Buku Saku Kesehatan Provinsi Jawa Tengah Triwulan 1 Tahun 2023,” 2023. [Online]. Available: https://dinkesjatengprov.go.id/v2018/buku-saku-2/

Dubey, A. (2018). Comparative study of K-means and fuzzy C-means algorithms on the breast cancer data. International Journal on Advanced Science, Engineering and Information Technology, 8(1), 18–29. https://doi.org/10.18517/ijaseit.8.1.3490 DOI: https://doi.org/10.18517/ijaseit.8.1.3490

Franceschi, F. (2018). Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering. Atmospheric Pollution Research, 9(5), 912–922. https://doi.org/10.1016/j.apr.2018.02.006 DOI: https://doi.org/10.1016/j.apr.2018.02.006

Gianniou, P. (2018). Clustering-based analysis for residential district heating data. Energy Conversion and Management, 165, 840–850. https://doi.org/10.1016/j.enconman.2018.03.015 DOI: https://doi.org/10.1016/j.enconman.2018.03.015

Guo, Z. (2021). Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management. Geoscience Frontiers, 12(6). https://doi.org/10.1016/j.gsf.2021.101249 DOI: https://doi.org/10.1016/j.gsf.2021.101249

Hashmi, M. R. (2020). m-Polar Neutrosophic Topology with Applications to Multi-criteria Decision-Making in Medical Diagnosis and Clustering Analysis. International Journal of Fuzzy Systems, 22(1), 273–292. https://doi.org/10.1007/s40815-019-00763-2 DOI: https://doi.org/10.1007/s40815-019-00763-2

Islam, M. (2018). Combining K-MEANS and a genetic algorithm through a novel arrangement of genetic operators for high quality clustering. Expert Systems with Applications, 91, 402–417. https://doi.org/10.1016/j.eswa.2017.09.005 DOI: https://doi.org/10.1016/j.eswa.2017.09.005

Ismkhan, H. (2018). I-k-means−+: An iterative clustering algorithm based on an enhanced version of the k-means. Pattern Recognition, 79, 402–413. https://doi.org/10.1016/j.patcog.2018.02.015 DOI: https://doi.org/10.1016/j.patcog.2018.02.015

Jia, C. (2018). O-GlcNAcPRED-II: An integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K -means PCA oversampling technique. Bioinformatics, 34(12), 2029–2036. https://doi.org/10.1093/bioinformatics/bty039 DOI: https://doi.org/10.1093/bioinformatics/bty039

D. Sitinjak, A. B. Pangestu, and N. B. Sari, “Clustering Jumlah Tenaga Kesehatan Berdasarkan Kecamatan di Kabupaten Karawang Menggunakan Algoritma K-Means,” Journal of Applied Informatics and Computing (JAIC), vol. 6, no. 1, pp. 46–54, 2022. DOI: https://doi.org/10.30871/jaic.v6i1.3855

Larijani, M. R. (2019). Evaluation of image processing technique in identifying rice blast disease in field conditions based on KNN algorithm improvement by K-means. Food Science and Nutrition, 7(12), 3922–3930. https://doi.org/10.1002/fsn3.1251 DOI: https://doi.org/10.1002/fsn3.1251

A. Sembiring, T. A. Agus, M. Fitri, and L. Sibuea, “Penerapan Metode Algoritma K-Means Clustering Untuk Pemetaan Penyebaran Penyakit Demam Berdarah Dengue (DBD),” 2021. [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR DOI: https://doi.org/10.54314/jssr.v4i3.712

G. Sadewo, A. P. Windarto, S. R. Andani, and Handrizal, “Pemanfaatan Algoritma Clushtering Dalam Mengelompokkan Jumlah Desa / Kelurahan Yang Memiliki Sarana Kesehatan Menurut Provinsi Dengan K-Means,” KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 1, no. 1, pp. 124–131, 2017, [Online]. Available: https://ejurnal.stmik-budidarma.ac.id/index.php/komik/article/view/483/424 DOI: https://doi.org/10.30865/komik.v2i1.943

Nguyen, H. (2019). A new soft computing model for estimating and controlling blast-produced ground vibration based on Hierarchical K-means clustering and Cubist algorithms. Applied Soft Computing, 77, 376–386. https://doi.org/10.1016/j.asoc.2019.01.042 DOI: https://doi.org/10.1016/j.asoc.2019.01.042

Efendi, F. Farady Coastera, and F. Rizky Tanjung, “Pengelompokan dan Pemetaan Derajat Kesehatan Kota Bengkulu dengan Metode K-Means Clustering,” 2019. [Online]. Available: http://ejournal.unib.ac.id/index

Rezaee, M. J. (2021). GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game. Knowledge-Based Systems, 213. https://doi.org/10.1016/j.knosys.2020.106672 DOI: https://doi.org/10.1016/j.knosys.2020.106672

Sinaga, K. P. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716–80727. https://doi.org/10.1109/ACCESS.2020.2988796 DOI: https://doi.org/10.1109/ACCESS.2020.2988796

Sun, M. (2019). Pedestrian crash analysis with latent class clustering method. Accident Analysis and Prevention, 124, 50–57. https://doi.org/10.1016/j.aap.2018.12.016 DOI: https://doi.org/10.1016/j.aap.2018.12.016

Udler, M. S. (2018). Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis. PLoS Medicine, 15(9). https://doi.org/10.1371/journal.pmed.1002654 DOI: https://doi.org/10.1371/journal.pmed.1002654

Xie, H. (2019). Improving K-means clustering with enhanced Firefly Algorithms. Applied Soft Computing Journal, 84. https://doi.org/10.1016/j.asoc.2019.105763 DOI: https://doi.org/10.1016/j.asoc.2019.105763

Yang, M. S. (2019). A feature-reduction multi-view k-means clustering algorithm. IEEE Access, 7, 114472–114486. https://doi.org/10.1109/ACCESS.2019.2934179 DOI: https://doi.org/10.1109/ACCESS.2019.2934179

Yu, S. S. (2018). Two improved k-means algorithms. Applied Soft Computing Journal, 68, 747–755. https://doi.org/10.1016/j.asoc.2017.08.032 DOI: https://doi.org/10.1016/j.asoc.2017.08.032

Zhang, G. (2018). Improved K-means algorithm based on density Canopy. Knowledge-Based Systems, 145, 289–297. https://doi.org/10.1016/j.knosys.2018.01.031 DOI: https://doi.org/10.1016/j.knosys.2018.01.031

Zhao, S. (2018). A user-adaptive algorithm for activity recognition based on K-means clustering, local outlier factor, and multivariate gaussian distribution. Sensors (Switzerland), 18(6). https://doi.org/10.3390/s18061850 DOI: https://doi.org/10.3390/s18061850

Zhu, E. (2019). Fast and stable clustering analysis based on Grid-mapping K-means algorithm and new clustering validity index. Neurocomputing, 363, 149–170. https://doi.org/10.1016/j.neucom.2019.07.048 DOI: https://doi.org/10.1016/j.neucom.2019.07.048