Silk Fabric Quality Prediction Using Random Forest and Simulated Physical Material Feature Data - Moh. Zainuddin; Rachmat Rachmat; Muh. Rafli R. (Universitas Islam Raden Rahmat, Malang, 2026)
This study develops an interpretable Random Forest workflow to predict silk fabric quality from simulated physical material feature data. The model uses 150 controlled observations with eight predictors: fabric thickness, fabric weight, thread density, tensile strength, elasticity, luster, softness, and surface defects. The simulated values were generated with class-specific truncated normal distributions based on measurable textile properties, standard testing variables, and domain assumptions. Data were evaluated using a stratified 80:20 holdout, stratified 5-fold cross-validation, weighted accuracy-related metrics, and confusion matrix analysis. Random Forest achieved 0.87 holdout accuracy, 0.88 precision, 0.87 recall, and 0.87 F1-score. It showed competitive performance against KNN, Decision Tree, SVM, ANN, and XGBoost while providing direct feature-importance interpretation. Surface defects, tensile strength, and thread density were the most influential predictors. The main contribution is a reproducible simulation and interpretation framework for early-stage silk fabric quality prediction. Because the dataset is simulated, the results demonstrate method feasibility and require validation with real textile production or laboratory data.