Klasifikasi Tulisan Tangan Huruf Hijaiyah Anak Usia 6-8 Tahun Menggunakan Metode Support Vector Machine - Roofiad, Ahmad Maulidi; Alam, Cecep Nurul; Atmadja, Aldy Rialdy (SENTRI: Jurnal Riset Ilmiah, 2025)
This study aims to develop a handwritten Hijaiyah letter classification system for children aged 6–8 years using the Support Vector Machine (SVM) algorithm. The main problem in elementary education is the difficulty children face in recognizing and writing Hijaiyah letters due to the similarity of their shapes and variations in handwriting. The research process uses the CRISP-DM stages, consisting of problem understanding, data collection and preparation, modeling with SVM (GridSearch for hyperparameter tuning), and evaluation using a confusion matrix and f1-score. The dataset used consists of 2,100 images of handwritten letters from elementary school students. The results show that the SVM model with RBF kernel, C=10, and gamma="scale" achieved the highest accuracy of 83.57%. This study demonstrates that an SVM-based machine learning approach can assist in recognizing Hijaiyah letters, making it a practical solution for teachers in teaching Hijaiyah writing.