Application of K-Means Clustering Algorithm for Disease Grouping at Blessing Dental Care Clinic

Cintiya Aulya Fransiska, Dafid Dafid

Sari


Blessing Dental Care Clinic is a clinic that provides dental practice services and general practitioner practices located in Palembang. This clinic offers general care and dental care managed by experienced doctors in their fields. The focus of the data used is medical record data, especially from general practice. Grouping large data into several groups based on similar pattern characteristics by utilizing the K-Means Clustering algorithm in CRISP-DM data mining was chosen to be more effective in handling various complaints of various diseases through the Clustering process. The results showed that the form of cluster 1 was 220 dominant data in the respiratory disease category, cluster 2 was 335 dominant data in the cardiovascular disease category, cluster 3 was 584 dominant data in the cardiovascular disease category, cluster 4 was 363 dominant data in respiratory disease, cluster 5 was 70 dominant data in respiratory disease, cluster 6 was 254 dominant data in cardiovascular disease and cluster 7 was 165 dominant data in ENT disease. In cluster 7 with an SSE value of 3189.16, the decrease is getting smaller and the spread pattern is starting to be optimal with a tendency for the pattern to be more spread out.

Teks Lengkap:

PDF

Referensi


H. Dilawati, H. Widianto, dan A. Kuswiadji, “Klasterisasi Data Rekam Medis Pasien Menggunakan Metode K-Means Clustering Di Rumah Sakit Widodo Ngawi,†BIOS J. Teknol. Inf. dan Rekayasa Komput., vol. 5, no. 2, hal. 5–8, 2024, [Daring]. Tersedia pada: https://bios.sinergis.org/bios/article/view/134

L. ’Izzah dan A. Jananto, “Penerapan Algoritma K-Means Clustering Untuk Perencanaan Kebutuhan Obat Di Klinik Citra Medika,†J. Ilm. Komput., vol. 18, no. 1, hal. 69, 2022, doi: 10.35889/progresif.v18i1.769.

Nadhila, Marsono, dan J. Halim, “Penerapan Data Mining Untuk Pengelompokan Penyakit Yang Sering Terjadi Pada Pasien RSUD (Rumah Sakit Umum Daerah) Kota Langsa Menggunakan Metode K-Means Clustering,†J. Cybertech, no. September, hal. 1–12, 2020, [Daring]. Tersedia pada: www.trigunadharma.ac.id

E. A. Herdiaman, A. Sudiarjo, dan M. Hikmatyar, “KLASTERISASI PASIEN PADA RSUD CIAMIS MENGGUNAKAN METODE K-MEANS,†JITET (Jurnal Inform. dan Tek. Elektro Ter., vol. 12, no. 3, 2024, [Daring]. Tersedia pada: https://journal.eng.unila.ac.id/index.php/jitet/article/view/5124/2092

A. Ali, “Klasterisasi Data Rekam Medis Pasien Menggunakan Metode K-Means Clustering di Rumah Sakit Anwar Medika Balong Bendo Sidoarjo,†MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 19, no. 1, hal. 186–195, 2019, doi: 10.30812/matrik.v19i1.529.

W. Purba, G. A. Sembiring, A. Saputra, M. T. Turnip, dan B. J. I. Manihuruk, “PENERAPAN DATA MINING UNTUK PENGELOLAAN DATA REKAM MEDIS MENGGUNAKAN METODE K-MEANS CLUSTERING PADA RUMAH SAKIT ROYAL PRIMA MEDAN,†J. TEKINKOM, vol. 6, no. 1, hal. 158–168, 2023, doi: 10.37600/tekinkom.v6i1.857.

O. J. Harmaja, H. Halawa, W. S. Hulu, dan S. Loi, “Implementasi Algoritma K-Means Clustering Untuk Pengelompokkan Penyakit Pasien Pada Puskesmas Pulo Brayan,†Sains dan Teknol., vol. 5, no. 1, hal. 150–157, 2023, doi: https://doi.org/10.55338/saintek.v5i1.1306.

C. A. Sugianto, A. H. Rahayu, dan A. Gusman, “Algoritma K-Means untuk Pengelompokkan Penyakit Pasien pada Puskesmas Cigugur Tengah,†Jt. (Journal Inf. Technol., vol. 2, no. 2, hal. 39–44, 2020, doi: 10.47292/joint.v2i2.30.

F. Kurnia, I. Fahmi, E. Wahyudi, dan G. E. S. Mige, “Penerapan Algoritma K-Means Untuk Pengelompokan Diagnosa Penyakit Mata Berdasarkan Rentang Usia,†J. SPEKTRO, vol. 2, no. 1, hal. 10–17, 2019, [Daring]. Tersedia pada: https://ejurnal.undana.ac.id/index.php/spektro/article/view/1373/1092

K. Rahayu, L. Novianti, dan M. Kusnandar, “Implementation Data Mining With K-Means Algorithm For Clustering Distribution Rabies Case Area In Palembang City,†J. Phys. Conf. Ser., vol. 1500, no. 1, 2020, doi: 10.1088/1742-6596/1500/1/012121.

A. E. Clarke, S. J. Elliott, Y. St. Pierre, L. Soller, S. La Vieille, dan M. Ben-Shoshan, “Demographic characteristics associated with food allergy in a Nationwide Canadian Study,†Allergy, Asthma Clin. Immunol., vol. 17, no. 1, hal. 1–7, 2021, doi: 10.1186/s13223-021-00572-z.

Widiastuti, “Klasifikasi jenis pekerjaan kantor yang di lakukan mahasiswa pada praktik kerja lapangan,†J. Pendidik. Manaj. PERKANTORAN, vol. 5, no. 1, hal. 109–117, 2020, doi: 10.17509/jpm.v4i2.18008.

I. L. Organization, “The International Standard Classification of Occupations.†[Daring]. Tersedia pada: https://isco-ilo.netlify.app/en/isco-08/

W. H. Organization, “International Statistical Classification of Diseases and Related Health Problems 10th Revision.†[Daring]. Tersedia pada: https://icd.who.int/browse10/2010/en

F. Cirett-Galán, R. T. Peralta, dan O. F. G. Mora, “K-Means Cluster Analysis to Support Diabetic Patient Care,†Res. Sq., 2023.

A. A. Wahid, “Analisis Metode Waterfall Untuk Pengembangan Sistem Informasi,†J. Ilmu-ilmu Inform. dan Manaj. STMIK, vol. 1, 2020, [Daring]. Tersedia pada: https://www.researchgate.net/profile/Aceng-Wahid/publication/346397070_Analisis_Metode_Waterfall_Untuk_Pengembangan_Sistem_Informasi/links/5fbfa91092851c933f5d76b6/Analisis-Metode-Waterfall-Untuk-Pengembangan-Sistem-Informasi.pdf

L. Setiyani, “Desain Sistem : Use Case Diagram Pendahuluan,†LPPM STIMK ROSMA/ Pros. Semin. Nas. Inov. Adopsi Teknol., hal. 246–260, 2021, [Daring]. Tersedia pada: https://journal.uii.ac.id/AUTOMATA/article/view/19517


Refbacks

  • Saat ini tidak ada refbacks.


Indexing :

Creative Commons License
Journal of Artificial Intelligence and Software Engineering (JAISE) licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.