Analisis Pola Penyakit Kronis pada Lansia Menggunakan K-Means Clustering di Puskesmas Kelurahan Semper Barat
Article Sidebar
Main Article Content
Abstract
Article Summary
Chronic diseases have become a major health problem as well as the leading cause of death worldwide. The main chronic diseases causing death globally are cardiovascular diseases, chronic respiratory diseases, and metabolic diseases such as diabetes (WHO, 2018). Semper Barat Subdistrict is an area with a significant number of elderly residents, with healthcare services centered at the Semper Barat Community Health Center (Puskesmas). However, so far, there has been no study that specifically analyzes the patterns of chronic diseases among the elderly in this area using a data mining approach. This study presents a novelty in the form of a case study on clustering chronic diseases within the community of the subdistrict using data mining with the K-Means algorithm. The results show that this model is capable of providing precise values in clustering chronic diseases. The clustering results can be utilized by the Semper Barat Community Health Center as a basis for decision-making in conducting targeted outreach and treatment, thereby facilitating better access to elderly individuals who already have a history of chronic diseases according to their disease group. The testing results from the previous Cluster Distance Performance showed an evaluation value of 0.579 for two clusters, which was the closest to zero compared to other numbers of clusters. In the context of the K-Means algorithm, values closer to zero indicate that the data within a cluster have greater similarity, and the distance between clusters is sufficiently distinct.
Keywords
Article Keywords
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
License: All articles are licensed under 
Ariyanto, D. (2022). Data Mining Menggunakan Algoritma K-Means untuk Klasifikasi Penyakit Infeksi Saluran Pernafasan Akut. Jurnal Sistim Informasi dan Teknologi, 13-18. https://doi.org/10.37034/jsisfotek.v4i1.117.
Aulia, S. (2021). Klasterisasi pola penjualan pestisida menggunakan metode K-Means clustering (Studi Kasus di Toko Juanda Tani Kecamatan Hutabayu Raja). Djtechno: Jurnal Teknologi Informasi, 1(1), 1–5. https://doi.org/10.46576/djtechno.v1i1.964.
Gustiane, I. T., Martanto, M., & Suprapti, T. (2024). Clustering Hasil Cek Darah Diabetes Lansia Menggunakan Metode K-Means di Posbindu Kp. Lebakjero Desa Ciherang. JATI (Jurnal Mahasiswa Teknik Informatika), 8(2), 2125-2129.
Gustrianda, R., & Mulyana, D. I. (2022). Penerapan Data Mining Dalam Pemilihan Produk Unggulan dengan Metode Algoritma K-Means Dan K-Medoids. J. Media Inform. Budidarma, 6(1), 27.
Kusuma, P. A., & Firmansyah, A. U. (2022). Deteksi Penyebaran Penyakit Tuberkulosis dengan Algoritma K-Means Clustering Menggunakan Rapid Miner. Teknol. Inform. dan Komput, 8(2), 41-54.
Nahjan, M. R., Heryana, N., & Voutama, A. (2023). Implementasi Rapidminer Dengan Metode Clustering K-Means Untuk Analisa Penjualan Pada Toko Oj Cell. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 101-104. https://doi.org/10.36040/jati.v7i1.6094.
Narulita, S., Prihati, P., Oktaga, A. T., & Widyantoro, A. E. (2023). Performansi algoritma clustering K-means untuk penentuan status malnutrisi pada balita. Jurnal Informasi, Sains Dan Teknologi, 6(1), 188-202. https://doi.org/10.55606/isaintek.v6i02.128.
Niko, N. S., Rahman, A., Atmaja, D. M. U., & Basri, A. (2023). Klasterisasi Stok Produk Retail Untuk Menetukan Pergerakan Kebutuhan Konsumen Dengan Algoritma K-Means. Bulletin of Information Technology (BIT), 4(3), 306-312. https://doi.org/10.47065/bit.v4i3.736.
Novita, I., Hr, M., Rosita, S., Martunis, M., Sakdah, N., & Yunita, Y. (2024). HUBUNGAN PENGETAHUAN, PERAN KADER DAN DUKUNGAN KELUARGA TERHADAP KEIKUTSERTAAN LANSIA DALAM PROGRAM PENGELOLAAN PENYAKIT KRONIS (PROLANIS) DI POSYANDU LANSIA GAMPONG MULIA. JOURNAL OF HEALTHCARE TECHNOLOGY AND MEDICINE, 10(2), 87-96. https://doi.org/10.33143/jhtm.v10i2.4291.
Novitasari, N., Nuris, N. D., & Herdiana, R. (2023). Penerapan algoritma K-Means untuk clustering data jumlah penduduk miskin berdasarkan kota/kabupaten di Jawa Barat menggunakan RapidMiner. Jurnal Informatika Terpadu, 9(1), 68–73. https://doi.org/10.54914/jit.v9i1.660.
Sudrajat, W., Cholid, I., & Petrus, J. (2022). Penerapan algoritma K-Means clustering untuk pengelompokan UMKM menggunakan RapidMiner. Jurnal JUPITER, 14(1), 27–36.
Sugianto, C. A., Rahayu, A. H., & Gusman, A. (2020). Algoritma K-Means untuk pengelompokan penyakit pasien pada Puskesmas Cigugur Tengah. Journal of Information Technology, 2(2), 39–44. https://doi.org/10.47292/joint.v2i2.30.
Tinendung, I. S., & Zufria, I. (2023). Pengelompokan status stunting pada anak menggunakan metode K-Means clustering. Jurnal Media Informatika Budidarma, 7(4), 2014. https://doi.org/10.30865/mib.v7i4.6908.