Clustering Data Pasien Berdasarkan Usia di Puskesmas Menerapkan Metode K-Means
- Alifia Herlin Lutfiannisa Universitas Muhammadiyah Magelang, Magelang, Indonesia
-
Maimunah Maimunah
*
Universitas Muhammadiyah Magelang, Magelang,
Indonesia
- Pristi Sukmasetya Universitas Muhammadiyah Magelang, Magelang, Indonesia (*) Corresponding Author
Abstract
This research aims to perform clustering of disease data based on patient age using the K-Means method at the Tlogomulyo Community Health Center in Temanggung Regency, which faces challenges in managing irregular health data. As a primary healthcare centre, the health facility requires data analysis to identify patterns or groups of diseases based on age. The K-Means clustering method is employed in processing patient data to understand the distribution of diseases and aid decision-making regarding prevention, treatment, and healthcare planning. The clustering results reveal two clusters where the first cluster is dominated by scabies cases in the 10-20 age group, while the second cluster exhibits a high prevalence of Acute Respiratory Infection (ARI) in patients around the age of 51. Evaluation using the Silhouette Coefficient indicates that forming 2 clusters is the most optimal, with a value of 0.44. These findings provide crucial insights for the development of more effective disease management strategies based on the characteristics and health profiles of each cluster at the Tlogomulyo Community Health Center.
Downloads
References
R. A. Farissa, R. Mayasari, and Y. Umaidah, “Perbandingan Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Data Obat dengan Silhouette Coefficient di Puskesmas Karangsambung,” J. Appl. Informatics Comput., vol. 5, no. 2, pp. 109–116, 2021, doi: 10.30871/jaic.v5i1.3237.
D. Ariyanto, “Data Mining Menggunakan Algoritma K-Means untuk Klasifikasi Penyakit Infeksi Saluran Pernafasan Akut,” J. Sistim Inf. dan Teknol., vol. 4, pp. 13–18, 2022, doi: 10.37034/jsisfotek.v4i1.117.
A. Y. Simanjuntak, I. S. E. S. Simatupang, and A. Anita, “Implementasi Data Mining Menggunakan Metode NaãVe Bayes Classifier Untuk Data Kenaikan Pangkat Dinas Ketenagakerjaan Kota Medan,” J. Sci. Soc. Res., vol. 5, no. 1, p. 85, 2022, doi: 10.54314/jssr.v5i1.804.
N. Klakotskaya, P. Laurson, A. V. Libek, and A. Kikas, “Assessment of the Aim Characteristics of Strawberry (Fragaria × Ananassa) Cultivars in Estonia by Using the K-Means Clustering Method,” Horticulturae, vol. 9, no. 1, 2023, doi: 10.3390/horticulturae9010104.
D. Andreswari, R. Efendi, and K. Prastio, “Clustering Data Rekam Medis untuk Penentuan Penyakit Endemi di Daerah Kabupaten Bengkulu Selatan dengan Mengimplementasikan Metode FUZZY C-MEANS,” J. Rekursif, vol. 11, no. 1, pp. 42–52, 2023, doi: 10.33369/rekursif.v11i1.23274.
D. Gustian and M. S. Al-Farits, “Data Mining Untuk Melihat Minat Belajar Siswa Menerapkan Metode K-Means,” J. Inf. Syst. Res., vol. 4, no. 3, pp. 775–784, 2023, doi: 10.47065/josh.v4i3.3218.
D. A. I. C. Dewi and D. A. K. Pramita, “Analisis Perbandingan Metode Elbow dan Silhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali,” Matrix J. Manaj. Teknol. dan Inform., vol. 9, no. 3, pp. 102–109, 2019, doi: 10.31940/matrix.v9i3.1662.
T. D. Harjanto, A. Vatresia, and R. Faurina, “Analisis Penetapan Skala Prioritas Penanganan Balita Stunting Menggunakan Metode DBSCAN Clustering (Studi Kasus Data Dinas Kesehatan Kabupaten Lebong),” Rekursif J. Inform., vol. 9, no. 1, pp. 30–42, 2021, doi: 10.33369/rekursif.v9i1.14982.
U. I. N. Ar-raniry, F. Tarbiyah, D. A. N. Keguruan, P. Studi, and P. Teknologi, “Perancangan Media Pembelajaran Bahasa Pemrograman Python Menggunakan Aplikasi Scratch Untuk Siswa Sekolah Menengah,” p. 72, 2022.
A. Praja, C. Lubis, and D. E. Herwindiati, “Deteksi Penyakit Diabetes Dengan Metode Fuzzy C-Means Clustering Dan K-Means Clustering,” Comput. J. Comput. Sci. Inf. Syst., vol. 1, no. 1, p. 15, 2017, doi: 10.24912/computatio.v1i1.233.
A. Triono, A. S. Budi, and R. Abdillah, “Implementasi peretasan sandi vigenere chipher menggunakan bahasa pemrograman python,” vol. 1, no. 1, pp. 1–9, 2023.
A. Chusyairi and P. Ramadar Noor Saputra, “Pengelompokan Data Puskesmas Banyuwangi Dalam Pemberian Imunisasi Menggunakan Metode K-Means Clustering,” Telematika, vol. 12, no. 2, pp. 139–148, 2019, doi: 10.35671/telematika.v12i2.848.
A. Maulana, A. Nazir, R. M. Candra, S. Sanjaya, and F. Syafria, “Clustering Vaksinasi Penyakit Mulut dan Kuku Menggunakan Algoritma K-Means,” J. Inf. Syst. Res., vol. 4, no. 3, pp. 894–902, 2023, doi: 10.47065/josh.v4i3.3363.
B. Serasi Ginting and M. Simanjuntak, “Pengelompokan Penyakit Pada Pasien Berdasarkan Usia Dengan Metode K-Means Clustering (Studi Kasus : Puskesmas Bahorok),” Algoritm. J. Ilmu Komput. dan Inform., vol. 6341, no. November, p. 2, 2021.
D. U. Iswavigra, S. Defit, and G. W. Nurcahyo, “Data Mining dalam Pengelompokan Penyakit Pasien dengan Metode K-Medoids,” J. Inf. dan Teknol., vol. 3, pp. 181–189, 2021, doi: 10.37034/jidt.v3i4.150.
M. L. Kolling et al., “Data mining in healthcare: Applying strategic intelligence techniques to depict 25 years of research development,” Int. J. Environ. Res. Public Health, vol. 18, no. 6, pp. 1–21, 2021, doi: 10.3390/ijerph18063099.
B. H. Prakoso, E. Rachmawati, D. R. P. Mudiono, V. Vestine, and G. E. J. Suyoso, “Klasterisasi Puskesmas dengan K-Means Berdasarkan Data Kualitas Kesehatan Keluarga dan Gizi Masyarakat,” J. Buana Inform., vol. 14, no. 01, pp. 60–68, 2023, doi: 10.24002/jbi.v14i01.7105.
A. D. Andini and T. Arifin, “Implementasi Algoritma K-Medoids Untuk Klasterisasi Data Penyakit Pasien Di Rsud Kota Bandung,” J. Responsif Ris. Sains dan Inform., vol. 2, no. 2, pp. 128–138, 2020, doi: 10.51977/jti.v2i2.247.
B. Wira, A. E. Budianto, and A. S. Wiguna, “Implementasi Metode K-Medoids Clustering untuk Mengetahui Pola Pemilihan Program Studi,” J. Terap. Sains Teknol., vol. 1, no. 3, pp. 54–69, 2019.
N. Purba, P. Poningsih, and H. S. Tambunan, “Penerapan Algoritma K-Means Clustering Pada Penyebaran Penyakit Infeksi Saluran Pernapasan Akut (ISPA) di Provinsi Riau,” J. Inf. Syst. Res., vol. 2, no. 3, pp. 220–226, 2021, [Online]. Available: http://ejurnal.seminar-id.com/index.php/josh/article/view/736.
V. Herlinda and D. Darwis, “Analisis Clustering Untuk Recredesialing Fasilitas Kesehatan Menggunakan Metode Fuzzy C-Means,” Darwis, Dartono, vol. 2, no. 2, pp. 94–99, 2021, doi: 10.33365/jtsi.v2i2.890.
P. Zong, J. Jiang, and J. Qin, “Study of high-dimensional data analysis based on clustering algorithm,” 15th Int. Conf. Comput. Sci. Educ. ICCSE 2020, no. Iccse, pp. 638–641, 2020, doi: 10.1109/ICCSE49874.2020.9201656.
M. A. V. Ideal, “Classification of Patient Complaints against Patient Medical Record Data Using the K Means Method,” J. Sistim Inf. dan Teknol., vol. 5, pp. 1–6, 2022, doi: 10.37034/jsisfotek.v5i1.151.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Clustering Data Pasien Berdasarkan Usia di Puskesmas Menerapkan Metode K-Means
Pages: 639-647
Copyright (c) 2024 Alifia Herlin Lutfiannisa, Maimunah Maimunah, Pristi Sukmasetya
This work is licensed under a Creative Commons Attribution 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 Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment 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 acknowledgment 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 (Refer to The Effect of Open Access).
