Diabetes mellitus (DM) is one of the chronic and deadly diseases that are widely observed in various countries today. This disease continues and is increasing to a very alarming stage. This study aims to identify and see the relationship between factors that influence DM disease. The method used in this research is C4.5 algorithm which is one of the algorithms used to make predictive classifications. Classification is one of the processes in data mining that aims to find patterns in relatively large data that use the representations in the form of decision trees. This method is applied to data from medical records of patients with DM in 2014-2018 taken from the Hasanuddin University Teaching Hospital. The results obtained indicate that there are four factors that influence the prediction of a patient's DM status namely; Fasting Blood Glucose (GDP), LDL Cholesterol, Triglycerides, and Body Weight.

Diabetes mellitus (DM) is one of the chronic and deadly diseases that are widely observed in various countries today. This disease continues and is increasing to a very alarming stage. Indonesia ranks fourth in the world with the highest DM after the United States, India and China. The method used in this study is data collection, variable selection, classification methods, validation and evaluation and decision making. The algorithm used in this study is C4.5 Algorithm and Naive Bayesian Method using a dataset obtained from the results of Hasanuddin University hospital medical records. The results of calculations that have been done obtained accuracy on the C4.5 algorithm of 100% and on the Bayesian naive method obtained at 90%. From these results it can be concluded that to diagnose DM disease it is recommended to use the C4.5 Algorithm .

The regression approach can be carried out using three approaches namely parametric, nonparametric and semiparametric approaches. Nonparametric regression is a statistical method used to see the relationship between the response variable and the predictor variable when the shape of the data curve is unknown. Diabetes mellitus (DM) or commonly called diabetes is a disease that is found and observed in various parts of the world today. DM is often marked by a significant increase in blood sugar levels. In this study using blood sugar levels as response variables, body mass index and triglycerides as predictor variables. Data were analyzed using truncated linear spline with one, two and three point knots experiments. The best model is obtained based on the minimum generalized cross validation (GCV) value. The results obtained that the best model is linear spline using three point knots.