ANALYSIS OF CLUSTERING METHODS ON THE CAUSAL FACTORS OF DIABETES MELLITUS WITH FUZZY C MEANS METHOD - Adiwidyatma, Afdhal Reshanda; Mas Diyasa, I Gede Susrama; Trimono, Trimono (LPPM Universitas Bina Bangsa, 2024)
This study focuses on the effectiveness of clustering algorithms, namely Fuzzy C-Means by using k-Means algorithm as a supporting method, in the factors that cause Diabetes Mellitus. Diabetes mellitus is a chronic disease characterized by high levels of sugar (glucose) in the blood. Indonesia ranks 5th with the highest diabetes Mellitus patients in the world. This study aims to understand the pattern of factors causing Diabetes Mellitus and test the effectiveness of the clustering algorithm used. The data analysis methods include data collection, data pre-processing, distribution of cluster numbers, algorithm implementation, model adjustment, model training, model evaluation, and analysis of results. The results showed that the Fuzzy C-Means algorithm gets a coefficient of Fuzzynes score of 0.23 with a validation score of 0.40, while for supporting methods used K-Means algorithm gets a validation score of 0.32. This result shows that Fuzzy C-Means algorithm is superior in clastering the factors that cause Diabetes mellitus. The results of what variables have the most effect on cluster values 0 and 1. Where cluster 0 is a cluster that shows which variables are more at risk of diabetes, while cluster 1 is a cluster whose value shows what variables are far from the risk of causing diabetes mellitus. Then based on the results of the cluster that has been done, random blood sugar variables become the most influential variable on the risk of developing diabetes mellitus, followed by blood sugar variables 2 hours PP, and fasting blood sugar