Main Article Content

Allwin M.Simarmata
Maria Albina Pratiwi Sianipar
Sukbinder Singh
Intan Is Mutiara Gulo

Abstract

Diabetes Mellitus has two types of disease, namely Diabetes Type I and Diabetes Type II. Research on the grouping of diabetes based on age at RSU Royal Prima Medan has not been carried out. For this reason, the researchers made a research analysis in grouping diabetes based on age. The aim of this research is to find the age group that is susceptible to being diagnosed with Type I or Type II diabetes. Researchers took sample data from patients diagnosed with diabetes of 2019 as many as 200 data. The method used in grouping the data is Kmeans. The Kmeans method has been widely used in various studies for grouping in the medical field. The results of the calculation analysis show that a sample of 200 data is Type I Diabetes at the age of 43 - 47, 56, 62, 67, 68 while Type II diabetes is at the age of 49, 55, 63. It is possible from the results of the study that patients of that age are not based on There are not many types though. The conclusion in this study is that the grouping of Type I and Type II diabetes based on age is expected to assist the hospital in identifying patients quickly so that patients can receive health services quickly as well.

Downloads

Download data is not yet available.

Article Details

How to Cite
M.Simarmata, A. . ., Sianipar, M. A. P. ., Singh , S. . and Gulo , I. I. M. . (2021) “Grouping Diabetes Diagnosis Based on Age Range with K-means Algorithm”, Jurnal Mantik, 5(2), pp. 1408-1412. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/1586 (Accessed: 21August2026).
References
[1] Agusta, Y. (2007). K-Means–Penerapan, Permasalahan dan Metode Terkait. Jurnal Sistem dan Informatika, 3(1), 47-60.
[2] Agustina, S., Yhudo, D., Santoso, H., Marnasusanto, N., Tirtana, A., & Khusnu, F. (2012). Clustering Kualitas Beras Berdasarkan Ciri Fisik Menggunakan Metode K-Means. Universitas Brawijaya Malang, Malang.
[3] Asroni, A., & Adrian, R. (2015). Penerapan metode K-means untuk clustering mahasiswa berdasarkan nilai akademik dengan Weka Interface studi kasus pada jurusan Teknik Informatika UMM Magelang. Semesta Teknika, 18(1), 76-82.
[4] Ediyanto, M. N. M., & Satyahadewi, N. (2013). Pengklasifikasian Karakteristik Dengan Metode K-Means Cluster Analysis. Bimaster, 2(02).
[5] Handoko, K. (2016). Penerapan Data Mining dalam Meningkatkan Mutu Pembelajaran Menggunakan Metode K-MEANS Clustering. Jurnal Nasional Teknologi dan Sistem Informasi, 2(3), 31-40.
[6] METISEN, Benri Melpa; SARI, Herlina Latipa. Analisis clustering menggunakan metode K-Means dalam pengelompokkan penjualan produk pada Swalayan Fadhila. Jurnal media infotama, 2015, 11.2
[7] Sani, A. (2018). Penerapan metode k-means clustering pada perusahaan. Jurnal Ilmiah Teknologi Informasi, 353, 1-7.
[8] Sibuea, M. L., & Safta, A. (2017). Pemetaan Siswa Berprestasi Menggunakan Metode K-Means Clustring. JURTEKSI (Jurnal Teknologi dan Sistem Informasi), 4(1), 85-92.
[9] Suprawoto, T. (2016). Klasifikasi data mahasiswa menggunakan metode k-means untuk menunjang pemilihan strategi pemasaran. JIKO (Jurnal Informatika dan Komputer), 1(1).
[10] Waworuntu, M. N. V., & Amin, M. F. (2018). Penerapan Metode K-Means Untuk Pemetaan Calon Penerima Jamkesda. KLIK-Kumpulan Jurnal Ilmu Komputer, 5(2), 190-200.
[11] BASTIAN, A. (2018). Penerapan Algoritma K-Means Clustering Analysis Pada Penyakit Menular Manusia (Studi Kasus Kabupaten Majalengka). Jurnal Sistem Informasi, 14(1), 28-34. Https://doi.org/10.21609/jsi.v14i1.566.
[12] Meisida, Novita; soesanto, Oni; candra, Heru Kartika. K-Means untuk Klasifikasi Penyakit Karies Gigi. klik-kumpulan Jurnal Ilmu Komputer, 2017, 1.1: 12-22.
[13] Sugianto, Castaka Agus; rahayu, Ayu Hendrati; gusman, Aditia. Algoritma K-Means Untuk Pengelompokkan Penyakit Pasien Pada Puskesmas Cigugur Tengah. Journal of Information Technology, 2020, 2.2: 39-44.