Penerapan Metode OPTICS dan ST-DBSCAN untuk Klasterisasi Data Kesehatan
DOI:
https://doi.org/10.29408/edumatic.v8i1.25765Kata Kunci:
clustering, health workers, optics, st-dbscanAbstrak
One way to extract valuable insights from large datasets is through cluster analysis. This statistical technique involves grouping data objects based on their similarities, aiming to create distinct groups where objects within each group share high similarities but differ significantly from objects in other groups. Cluster analysis, such as the OPTICS and ST-DBSCAN methods, can be utilized in various domains, including healthcare workforce and demographic data. In a case study focusing on health workers in East Lombok, these clustering methods were employed. The study aimed to present the outcomes of clustering health workers using OPTICS and ST-DBSCAN and determine the superior method through internal validation. The results from OPTICS revealed the formation of 5 clusters: cluster-1 with two sub-district members, cluster-2 with three members, cluster-3 with two members, cluster-4 with three members, and cluster-5 with seven members. Conversely, ST-DBSCAN produced only 2 clusters: cluster-1 with six members and cluster-2 with four members. Based on the internal validation findings, OPTICS emerged as the more effective method for categorizing health workers in East Lombok.
Referensi
Abid, A., Masmoudi, A., Kachouri, A., & Mahfoudhi, A. (2017). Outlier Detection in Wireless Sensor Networks Based on OPTICS Method for Events and Errors Identification. Wireless Personal Communications, 97(1), 1503–1515. https://doi.org/10.1007/s11277-017-4583-7
Ahmed, M. A., Baharin, H., & Nohuddin, P. N. E. (2020). Analysis of K-means, DBSCAN and OPTICS Cluster Algorithms on Al-Quran Verses. International Journal of Advanced Computer Science and Applications, 11(8), 248–254. https://doi.org/10.14569/IJACSA.2020.0110832
Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. ACM Sigmod Record, 28(2), 49–60. https://doi.org/10.1145/304181.304187
Ansari, M. Y., Ahmad, A., Khan, S. S., Bhushan, G., & Mainuddin. (2020). Spatiotemporal clustering: A review. Artificial Intelligence Review, 53, 2381–2423. https://doi.org/10.1007/s10462-019-09736-1
Anshori, M. L. (2022). Implementasi Metode Ordering Points To Identify The Clustering Structure (Optics) Dengan Algoritma Db-Scan Dalam Pengelompokkan Pemesanan Indihome (Studi Kasus: Klaster Data Pemesanan Pemasangan Indihome Berdasarkan Jenis Fiber 3p Dan Fiber 2p Di Provinsi Lampung Pada Tahun 2020). [Unpublished Undergraduate Thesis] Universitas Islam Indonesia.
Elmayati, E. (2017). Data Mining dengan Metode Clustering untuk Pengolahan Informasi Persediaan Obat pada Klinik Srikandi Medika Berbasis Web. Jurnal Pelita Informatika, 6(2), 159–164.
Eriansya, M. I. P., & Syafrullah, M. (2018). Implementasi Algoritma ST-DBSCAN dan K-MEANS Untuk Pengelompokan Indeks Pembangunan Manusia Kabupaten/Kota Pulau Jawa Tahun 2014-2016 Berbasis Web Di Badan Pusat Statistik. SKANIKA: Sistem Komputer Dan Teknik Informatika, 1(3), 1026–1032.
Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd, 96(34), 226–231.
Fahzirah, R. (2021). Analisis Ketersediaan Tenaga Kesehatan Berdasarkan Standar Ketenagaan Minimal PERMENKES Nomor 43 Tahun 2019 di Puskesmas Tanjung Rejo Kecamatan Percut Sei Tuan Tahun 2021. Journal Transformation of Mandalika, 2(3), 106–111.
Gaonkar, M. N., & Sawant, K. (2013). AutoEpsDBSCAN: DBSCAN with Eps automatic for large dataset. International Journal on Advanced Computer Theory and Engineering, 2(2), 11–16.
Han, J., Kamber, M., & Mining, D. (2006). Concepts and techniques. Morgan Kaufmann, 340, 94104–3205.
Hastuti, S. H., Nurmayanti, W. P., & Saputri, A. A. (2023). Penerapan Metode Clustering Self Organizing Maps (SOM) dan K-Affinity Propagation (K-AP) dalam Mengelompokkan Nilai Tukar Petani di Indonesia 2022. VARIANCE: Journal of Statistics and Its Applications, 5(1), 79–88. https://doi.org/10.30598/variancevol5iss1page79-88
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1(14), 281–297.
Mustikasari, M., & Salman, N. (2023). Density-Based Clustering Analysis with DBSCAN and OPTICS. Jurnal INSYPRO (Information System and Processing), 8(1), 1–8. https://doi.org/10.24252/insypro.v8i1.36347
Nisrina, S., Nurmayanti, W. P., & Gazali, M. (2022). Penerapan Metode Clustering SOM dan DBSCAN dalam Mengelompokkan Unmet Need Keluarga Berencana di Nusa Tenggara Barat. J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, 15(2), 237-244. https://doi.org/10.36456/jstat.vol15.no2.a5549
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS), 42(3), 1–21. https://doi.org/10.1145/3068335
Sholikhah, N. A. (2022). Studi Perbandingan Clustering Kecamatan di Kabupaten Bojonegoro Berdasarkan Keaktifan Penduduk Dalam Kepemilikan Dokumen Kependudukan. Jurnal Statistika Dan Komputasi, 1(1), 42–53. https://doi.org/10.32665/statkom.v1i1.443
Silvi, R. (2018). Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia. Jurnal Matematika “MANTIK, 4(1), 22–31. https://doi.org/10.15642/mantik.2018.4.1.22-31
Yu, H., Lu, N., Fu, B., Zhang, L., Wang, M., & Tian, H. (2022). Hotspots, co-occurrence, and shifts of compound and cascading extreme climate events in Eurasian drylands. Environment International, 169, 107509. https://doi.org/10.1016/j.envint.2022.107509
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2024 Siti Hariati Hastuti, Ayu Septiani, Hendrayani Hendrayani, Wiwit Pura Nurmayanti
Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.
Semua tulisan pada jurnal ini adalah tanggung jawab penuh penulis. Edumatic: Jurnal Pendidikan Informatika bisa diakses secara free (gratis) tanpa ada pungutan biaya, sesuai dengan lisensi creative commons yang digunakan.
This work is licensed under a Lisensi a Creative Commons Attribution-ShareAlike 4.0 International License.
