Performance Analysis of Traditional Machine Learning Classifiers on LSTM-Extracted Features for Indonesian Sign Language System Recognition - Ho, Patricia; Santoso, Handri (Informatika, Universitas Jenderal Soedirman, 2026)
Recognizing affix gestures in the Indonesian Sign Language System (SIBI) remains challenging due to subtle visual differences in hand shape and movement, often resulting in lower classification accuracy compared to other categories. This study aims to evaluate whether lightweight traditional and hybrid classifiers can provide competitive performance to deep learning models for SIBI recognition. Using a dataset of 21,351 gesture videos covering four categories (Affix, Alphabet, Number, and Word), features were extracted from MediaPipe keypoints and processed as frozen LSTM embeddings. Six classifiers (Random Forest, K-Nearest Neighbors, Naïve Bayes, Multilayer Perceptron, Support Vector Machine, and Hidden Markov Model) were evaluated with 5-fold stratified cross-validation using accuracy, precision, recall, and F1-score, with statistical significance tested through Friedman and Nemenyi analyses. Results show that MLP and RF achieved high performance in Alphabet, Number, and Word categories (above 96 percent accuracy), while Affix remained the most difficult, with MLP reaching 81.17 percent, outperforming the 68.17 percent from a prior BiLSTM model. This study provides a benchmark for hybrid model implementation in sign language recognition, showing that while traditional classifiers on deep features are effective and computationally lighter for general gestures, deep architectures remain superior for capturing the fine-grained temporal nuances critical for complex categories like affixes.