Analisis Perbandingan Algoritma Naive Bayes dan Support Vector Machine dengan Pendekatan TF-IDF Sebagai Klasifikasi Perintah Suara - Syarifuddin, Faisal; Kusumaningsih, Dewi (Forum Kerjasama Pendidikan Tinggi, 2025)
This study evaluates the performance of two classification algorithms, namely Naive Bayes and Support Vector Machine (SVM), in identifying voice commands in financial applications for the blind. The data used has gone through a preprocessing process including tokenization, stemming, and stopword removal, and was extracted using the TF-IDF method. The models were trained using a data sharing scheme of 80% for training and 20% for testing, then evaluated based on accuracy, precision, recall, and F1-score. The test results show that both models achieve a very high level of accuracy, with Naive Bayes achieving an accuracy of 98.6% and SVM reaching 98.4%. Both show high precision, recall, and F1-score in each voice command category, with the highest value in the