Analisis Perbandingan Algoritma Naive Bayes dan Support Vector Machine dengan Pendekatan TF-IDF Sebagai Klasifikasi Perintah Suara
-
Faisal Syarifuddin
*
Universitas Budi Luhur, Jakarta,
Indonesia
- Dewi Kusumaningsih Universitas Budi Luhur, Jakarta, Indonesia (*) Corresponding Author
Abstract
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 "QRIS Payment" category which achieved a precision and recall of 1.00. Confusion matrix analysis shows that classification errors occur in minimal amounts. This study also shows that TF-IDF as a feature extraction technique is effective in improving speech recognition accuracy by giving more weight to relevant and rarely appearing words in the dataset, which helps the model to focus more on the most important information. With these results, both algorithms are proven to be effective in recognizing voice commands. However, Naive Bayes is slightly superior in accuracy, so it is more recommended for voice-based applications in digital financial systems. These findings support the development of more inclusive and accessible technology for the visually impaired.
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References
R. C. Tarumingkeng, Natural Language Processing (NLP). 2024.
P. Kollamudi, B. Koduru, B. Poranki, and M. P. Golla, ‘Smart virtual assistant’, Mater Today Proc, 2021, doi: https://doi.org/10.1016/j.matpr.2021.07.303.
F. E. B. Setyawan, Pendekatan pelayanan kesehatan dokter keluarga (pendekatan holistik komprehensif). Zifatama Jawara, 2019.
I. Gavat, A. Griparis, and S. Segarceanu, ‘Natural language processing in assistive technologies’, Romanian Journal of Technical Sciences - Applied Mechanics, vol. 68, no. 2, pp. 129–140, 2023.
A. A. Ariffin, A. F. Ibrahim, S. Hasan, and R. Latip, ‘An Efficient Virtual Machine Scheduling Algorithm To Minimize Makespan And Maximize Profit Using Hyper Heuristic Approach’, International Journal of Advanced Trends in Computer Science and Engineering, vol. 8, no. 1, pp. 206–216, 2019.
R. L. Simanjuntak, T. R. Siagian, V. Anggriani, and A. Arnita, ‘Analisis Sentimen Ulasan Pada Aplikasi E-Commerce Shopee Dengan Menggunakan Algoritma Naïve Bayes’, Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, vol. 3, no. 3, pp. 23–39, 2023.
R. Ade and P. R. Deshmukh, ‘Efficient Knowledge Transformation System Using Pair of Classifiers for Prediction of Students Career Choice’, Procedia Comput Sci, vol. 46, pp. 176–183, 2015.
P. A. Pravesy, ‘Studi Perbandingan Metode Support Vector Machine, Random Forest, Dan Convolutional Neural Network Untuk Klasifikasi Penyakit Kulit’, Jurnal Kecerdasan Buatan dan Teknologi Informasi, vol. 4, no. 1, pp. 70–76, 2025.
S. Deshmukh, P. S. Rede, and S. Iyer, ‘Voice-Enabled Vision For The Visually Disabled’, in International Conference on Advances in Computing, Communication, and Control (ICAC3), 2021.
P. Hafidzah, S. Maryani, B. Y. Ihsani, N. Nurmiwati, E. Erwin, and A. K. Niswariyana, ‘Penerapan Deep Learning dalam Menganalisis Sentimen di Media Sosial’, in Seminar Nasional Paedagoria, 2024, pp. 328–339.
M. R. Saputra and P. Parjito, ‘Analisis Sentimen Twitter Terhadap Konflik Di Papua Menggunakan Perbandingan Naive Bayes Dan Svm’, JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, no. 2, pp. 1197–1208, 2025.
A. Alfando and R. Hayami, ‘Klasifikasi Teks Berita Berbahasa Indonesia Menggunakan Machine Learning Dan Deep Learning: Studi Literatur’, JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 1, pp. 681–686, 2023.
N. Nurwanda, N. Suarna, and W. Prihartono, ‘Penerapan Nlp (Natural Language Processing) Dalam Analisis Sentimen Pengguna Telegram Di Playstore’, JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 2, pp. 1841–1846, 2024.
S. Wijaya and S. Hariyanto, ‘Perancangan Chatbot Dengan Metode Natural Languange Processing (Nlp) Dalam Proses Booking Order Di Carwash Park Tangcity’, Akselerator: Jurnal Sains Terapan dan Teknologi, vol. 5, no. 1, pp. 33–45, 2024.
S. Rabbani, D. Safitri, N. Rahmadhani, A. A. F. Sani, and M. K. Anam, ‘Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM: Comparative Evaluation of SVM Kernels for Sentiment Classification in Fuel Price Increase Analysis’, MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 2, pp. 153–160, 2023.
S. B. Kotsiantis, ‘Supervised Machine Learning: A Review of Classification Techniques’, Informatica University of Peloponnese, pp. 260–265, 2007.
L. Suryani and K. Edy, ‘Pengembangan Aplikasi “Lost & Found” Berbasis Android Dengan Menggunakan Metode Term Frequency–Inverse Document Frequency (Tf-Idf) Dan Cosine Similarity’, Electro Luceat, vol. 6, no. 2, pp. 190–204, 2020.
K. Huda, S. D. Pohan, and Y. Herlina, ‘Penerapan Pembobotan Term Frequency-Inverse Document Frequency Dan Algoritma K-Nearest Neighbor Untuk Analisis Ulasan Hotel Di Situs Tripadvisor’, Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3, 2024.
R. R. Putra, N. A. Putri, and A. D. Putra, ‘Teknik Cosine Similarity dan TF-IDF dalam Analisis Data’, Serasi Media Teknologi, 2024.
E. M. W. Runturamby, V. P. Rantung, and K. Santa, ‘Peringkas Teks Otomatis Berita Online Komisi Pemilihan Umum Menggunakan Algoritma K-Means Clustering’, Prosiding SISFOTEK, vol. 8, no. 1, pp. 299–312, 2024.
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