PENGELOMPOKAN TINGKAT RISIKO PENYAKIT DIABETES MELITUS MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING
Abstract
Penelitian ini fokus pada diabetes melitus (DM), kondisi metabolik kronis dengan tingkat gula darah tinggi karena kurangnya insulin. Faktor penyebab DM bervariasi, termasuk kurangnya produksi insulin oleh sel beta Langerhans di pankreas dan ketidakresponsifan tubuh terhadap insulin. Penyakit ini prevalen di negara berkembang dan diperkirakan terus meningkat. Studi ini menggunakan algoritma K-Means Clustering untuk mengelompokkan risiko DM. Evaluasi pada k = 2 menunjukkan data dalam klaster cenderung bercampur, dengan nilai Silhouette Coefficient 0.5716 dan Davies Bouldin Index 0.672. Visualisasi scatter menunjukkan penyebaran data yang seragam dalam klaster, memberikan pemahaman mendalam tentang pola data. Hasilnya dapat mendukung pemahaman dan penanganan lebih lanjut terhadap DM.
References
Ahmed, M., Seraj, R., Islam, S.M.S., 2020. The k-means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics 9, 1295. https://doi.org/10.3390/electronics9081295
Beginner’s Guide To K-Means Clustering [WWW Document], n.d. URL https://analyticsindiamag.com/beginners-guide-to-k-means-clustering/ (accessed 8.19.23).
Berkhin, P., 2006. A Survey of Clustering Data Mining Techniques, in: Kogan, J., Nicholas, C., Teboulle, M. (Eds.), Grouping Multidimensional Data: Recent Advances in Clustering. Springer, Berlin, Heidelberg, pp. 25–71. https://doi.org/10.1007/3-540-28349-8_2
Bholowalia, P., n.d. EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN. International Journal of Computer Applications 105.
Bosnic, Z., Yildirim, P., Babič, F., Šahinović, I., Wittlinger, T., Martinović, I., Majnaric, L.T., 2021. Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians’ Clinical Reasoning by Reducing Patients’ Complexity. Healthcare (Basel) 9, 1687. https://doi.org/10.3390/healthcare9121687
Chen, J., Qi, X., Chen, L., Chen, F., Cheng, G., 2020. Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection. Knowledge-Based Systems 203, 106167. https://doi.org/10.1016/j.knosys.2020.106167
Debatty, T., Michiardi, P., Mees, W., Thonnard, O., 2014. Determining the k in k-means with MapReduce, in: EDBT/ICDT 2014 Joint Conference, Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference. Athènes, Greece.
Ediyanto, M.N.M., 2013. PENGKLASIFIKASIAN KARAKTERISTIK DENGAN METODE K-MEANS CLUSTER ANALYSIS. Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya 2. https://doi.org/10.26418/bbimst.v2i02.3033
Harahap, F., 2021. Perbandingan Algoritma K Means dan K Medoids Untuk Clustering Kelas Siswa Tunagrahita. TIN: Terapan Informatika Nusantara 2, 191–197.
Jain, A.K., 2010. Data clustering: 50 years beyond K-means. Pattern Recognition Letters, Award winning papers from the 19th International Conference on Pattern Recognition (ICPR) 31, 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
Jain, A.K., Murty, M.N., Flynn, P.J., 1999. Data clustering: a review. ACM Comput. Surv. 31, 264–323. https://doi.org/10.1145/331499.331504
Larose, D.T., Larose, C.D., 2014. Discovering Knowledge in Data: An Introduction to Data Mining. John Wiley & Sons.
Merliana, N.P.E., Ernawati, Santoso, A.J., 2015. ANALISA PENENTUAN JUMLAH CLUSTER TERBAIK PADA METODE K-MEANS CLUSTERING. Proceeding SENDI_U.
Na, S., Xumin, L., Yong, G., 2010. Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm, in: 2010 Third International Symposium on Intelligent Information Technology and Security Informatics. Presented at the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, pp. 63–67. https://doi.org/10.1109/IITSI.2010.74
Syakur, M.A., Khotimah, B.K., Rochman, E.M.S., Satoto, B.D., 2018. Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster. IOP Conf. Ser.: Mater. Sci. Eng. 336, 012017. https://doi.org/10.1088/1757-899X/336/1/012017
Tan, P.-N., Steinbach, M., Kumar, V., 2006. Introduction to Data Mining. Pearson Addison Wesley.
Vora, P., 2013. A Survey on K-mean Clustering and Particle Swarm Optimization 1.
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D., 2008. Top 10 algorithms in data mining. Knowl Inf Syst 14, 1–37. https://doi.org/10.1007/s10115-007-0114-2
Beginner’s Guide To K-Means Clustering [WWW Document], n.d. URL https://analyticsindiamag.com/beginners-guide-to-k-means-clustering/ (accessed 8.19.23).
Berkhin, P., 2006. A Survey of Clustering Data Mining Techniques, in: Kogan, J., Nicholas, C., Teboulle, M. (Eds.), Grouping Multidimensional Data: Recent Advances in Clustering. Springer, Berlin, Heidelberg, pp. 25–71. https://doi.org/10.1007/3-540-28349-8_2
Bholowalia, P., n.d. EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN. International Journal of Computer Applications 105.
Bosnic, Z., Yildirim, P., Babič, F., Šahinović, I., Wittlinger, T., Martinović, I., Majnaric, L.T., 2021. Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians’ Clinical Reasoning by Reducing Patients’ Complexity. Healthcare (Basel) 9, 1687. https://doi.org/10.3390/healthcare9121687
Chen, J., Qi, X., Chen, L., Chen, F., Cheng, G., 2020. Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection. Knowledge-Based Systems 203, 106167. https://doi.org/10.1016/j.knosys.2020.106167
Debatty, T., Michiardi, P., Mees, W., Thonnard, O., 2014. Determining the k in k-means with MapReduce, in: EDBT/ICDT 2014 Joint Conference, Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference. Athènes, Greece.
Ediyanto, M.N.M., 2013. PENGKLASIFIKASIAN KARAKTERISTIK DENGAN METODE K-MEANS CLUSTER ANALYSIS. Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya 2. https://doi.org/10.26418/bbimst.v2i02.3033
Harahap, F., 2021. Perbandingan Algoritma K Means dan K Medoids Untuk Clustering Kelas Siswa Tunagrahita. TIN: Terapan Informatika Nusantara 2, 191–197.
Jain, A.K., 2010. Data clustering: 50 years beyond K-means. Pattern Recognition Letters, Award winning papers from the 19th International Conference on Pattern Recognition (ICPR) 31, 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
Jain, A.K., Murty, M.N., Flynn, P.J., 1999. Data clustering: a review. ACM Comput. Surv. 31, 264–323. https://doi.org/10.1145/331499.331504
Larose, D.T., Larose, C.D., 2014. Discovering Knowledge in Data: An Introduction to Data Mining. John Wiley & Sons.
Merliana, N.P.E., Ernawati, Santoso, A.J., 2015. ANALISA PENENTUAN JUMLAH CLUSTER TERBAIK PADA METODE K-MEANS CLUSTERING. Proceeding SENDI_U.
Na, S., Xumin, L., Yong, G., 2010. Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm, in: 2010 Third International Symposium on Intelligent Information Technology and Security Informatics. Presented at the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, pp. 63–67. https://doi.org/10.1109/IITSI.2010.74
Syakur, M.A., Khotimah, B.K., Rochman, E.M.S., Satoto, B.D., 2018. Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster. IOP Conf. Ser.: Mater. Sci. Eng. 336, 012017. https://doi.org/10.1088/1757-899X/336/1/012017
Tan, P.-N., Steinbach, M., Kumar, V., 2006. Introduction to Data Mining. Pearson Addison Wesley.
Vora, P., 2013. A Survey on K-mean Clustering and Particle Swarm Optimization 1.
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D., 2008. Top 10 algorithms in data mining. Knowl Inf Syst 14, 1–37. https://doi.org/10.1007/s10115-007-0114-2
Published
2024-05-21
How to Cite
GESTAVITO, Rio; HADIANA, Asep Id; UMBARA, Fajri Rakhmat.
PENGELOMPOKAN TINGKAT RISIKO PENYAKIT DIABETES MELITUS MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING.
JUMANJI (Jurnal Masyarakat Informatika Unjani), [S.l.], v. 8, n. 1, p. 16-35, may 2024.
ISSN 2598-8069.
Available at: <http://jumanji.unjani.ac.id/index.php/jumanji/article/view/338>. Date accessed: 26 dec. 2024.
doi: https://doi.org/10.26874/jumanji.v8i1.338.
Section
Articles
