Meningkatkan Efisiensi Prediksi Risiko Diabetes Melitus dengan Metode Fuzzy Decision Tree - Deddy Kurniawan; Wahyu Yanuarta; Tina Tri Wualansari; Niken Ayu Dwi Febrianti (STMIK Banjarbaru, 2025)
The main problem in Fuzzy Logic (FL)-based prediction models is that the number of rules increases as the data dimension increases, reducing the efficiency of the system in interpretation and prediction. This study aims to unravel the complexity and improve the accuracy value of DM predictions using the Fuzzy Decision Tree (FDT) method based on Iterative Dichotomiser 3 (ID3). The research data was obtained from the National Institute of Diabetes and Digestive and Kidney Diseases with the parameters of glucose, BMI, HDL, and systolic blood pressure. The process includes data fuzzification, the formation of a decision tree with ID3, and the application of two thresholds, namely FCT and LDT. The results showed that the FDT model succeeded in reducing the number of rules by 25%, from 81 rules to 60 rules. The application of ID3-based FDT succeeded in increasing the accuracy value of DM predictions by 80%. The conclusion of the study states that the FDT model is able to unravel the complexity of the prediction model by using a simpler number of rules and can maintain and increase the accuracy value of the DM prediction model.Keywords: Diabetes Melitus; Fuzzy Decision Tree; Fuzzy Logic; Fuzzy Sugeno; Risk Prediction AbstrakPermasalahan utama dalam model prediksi berbasis Fuzzy Logic (FL) adalah meningkatnya jumlah aturan seiring bertambahnya dimensi data, mengurangi efisiensi sistem dalam interpretasi dan prediksi. Penelitian ini bertujuan mengurai kompleksitas dan meningkatkan nilai akurasi prediksi DM menggunakan metode Fuzzy Decision Tree (FDT) berbasis Iterative Dichotomiser 3 (ID3). Data penelitian diperoleh dari National Institute of Diabetes and Digestive and Kidney Diseases dengan parameter glukosa, BMI, HDL, dan tekanan darah sistolik. Proses meliputi fuzzifikasi data, pembentukan pohon keputusan dengan ID3, serta penerapan dua threshold, yaitu FCT dan LDT. Hasil penelitian menunjukkan bahwa model FDT berhasil mengurangi jumlah aturan sebesar 25%, dari 81 aturan menjadi 60 aturan. Penerapan FDT berbasis ID3 berhasil meningkatkan nilai akurasi prediksi DM sebesar 80%. Simpulan penelitian menyatakan bahwa model FDT mampu untuk mengurai kompleksitas model prediksi dengan menggunakan menghasilkan jumlah aturan yang lebih sederhana dan dapat menjaga serta meningkatkan nilai akurasi model prediksi DM.Kata kunci: Diabetes Melitus; Pohon Keputusan Fuzzy; Logika Fuzzy; Fuzzy Sugeno; Prediksi Risiko