Prediksi Risiko Diabetes Mellitus Menggunakan Algoritma Neural Network - Sukma Ayu Andini; Hasbi Firmansyah; Wahyu Asriyani; Eko Budiraharjo (Jejak digital: Jurnal Ilmiah Multidisiplin, 2025)
Diabetes mellitus is classified as a chronic disease with increasing prevalence and the potential to cause various health complications. Therefore, a method capable of predicting diabetes risk early is needed. This study focuses on the implementation of the Neural Network method to predict the potential for diabetes using the Pima Indians Diabetes dataset sourced from the Kaggle platform. The dataset includes eight medical variables, including: pregnancy history, blood glucose concentration, blood pressure level, skinfold thickness, insulin level, Body Mass Index (BMI), diabetes family history, and age. The research procedure includes the initial dataset processing phase, separation of the dataset into training and testing segments, construction of a Neural Network model based on Multilayer Perceptron architecture, and assessment of model performance through the Root Mean Squared Error (RMSE) metric. Model implementation was carried out using RapidMiner software. The test results showed that the Neural Network model produced an RMSE value of 0.440, which indicates a relatively low level of prediction error. Thus, the Neural Network algorithm is considered effective in predicting diabetes risk and has the potential to be used as a decision-making tool for early detection.