Implementasi Metode Naive Bayes Untuk Klasifikasi Ulasan Pada Aplikasi Telegram
Abstract
Ulasan aplikasi merupakan salah satu bentuk ekspresi pengguna terhadap aplikasi yang digunakannya. Ulasan tertulis biasanya berupa kritik, saran atau sekedar perasaan pengguna setelah menggunakan aplikasi. Salah satu aplikasi yang memiliki banyak ulasan penggunanya adalah Telegram. Telegram sendiri merupakan platform komunikasi instan yang dapat diunduh melalui Google Play Store. Penelitian ini bertujuan untuk mengklasifikasikan teks dengan dataset 5.000 ulasan aplikasi Telegram di Google Play Store ke dalam kelompok yang memiliki nilai positif, negative, atau netral. Penelitian ini menerapkan metode TF-IDF dan Naive Bayes Classifier untuk pembobotan dan pengklasifikasian data. Hasil pengujian terhadap data test dengan menggunakan metode Naive Bayes Classifier menghasilkan nilai akurasi sebesar 85,6 %, presisi 86,8%, recall 85,7% dan f1-score 85,5%.
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Biswas, C., Mallick, R., Paul, S., & Mukherjee, D. (2023). Solution to Web Scraping. 2023 11th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON), 1–5. https://doi.org/10.1109/IEMECON56962.2023.10092327
Bublyk, M., Vysotska, V., Chyrun, L., Slava, O., Panasyuk, V., & Shevchenko, M. (2023). Features of Big Data Analysis for Apps Sales via Google Play Store. 2023 IEEE 18th International Conference on Computer Science and Information Technologies (CSIT), 1–6. https://doi.org/10.1109/CSIT61576.2023.10324075
Bykov, I. A., Medvedeva, M. V, & Hradziushka, A. A. (2021). Anonymous Communication Strategy in Telegram: Toward Comparative Analysis of Russia and Belarus. 2021 Communication Strategies in Digital Society Seminar (ComSDS), 14–17. https://doi.org/10.1109/ComSDS52473.2021.9422858
Chandra, A., & Roy, S. (2023). On the Detection of Alzheimer’s Disease using Naïve Bayes Classifier. 2023 International Conference on Microwave, Optical, and Communication Engineering (ICMOCE), 1–4. https://doi.org/10.1109/ICMOCE57812.2023.10166516
Dalaorao, G. A., Sison, A. M., & Medina, R. P. (2019). Integrating Collocation as TF-IDF Enhancement to Improve Classification Accuracy. 2019 IEEE 13th International Conference on Telecommunication Systems, Services, and Applications (TSSA), 282–285. https://doi.org/10.1109/TSSA48701.2019.8985458
El-Sayed, A. A., Mahmood, M. A. M., Meguid, N. A., & Hefny, H. A. (2015). Handling autism imbalanced data using synthetic minority over-sampling technique (SMOTE). 2015 Third World Conference on Complex Systems (WCCS), 1–5. https://doi.org/10.1109/ICoCS.2015.7483267
Fadil, I., Helmiawan, M. A., Supriadi, F., Saeppani, A., Sofiyan, Y., & Guntara, A. (2022). Waste Classifier using Naive Bayes Algorithm. 2022 10th International Conference on Cyber and IT Service Management (CITSM), 1–5. https://doi.org/10.1109/CITSM56380.2022.9935894
Hairani, H., Anggrawan, A., Wathan, A. I., Latif, K. A., Marzuki, K., & Zulfikri, M. (2021). The Abstract of Thesis Classifier by Using Naive Bayes Method. 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), 312–315. https://doi.org/10.1109/ICSECS52883.2021.00063
Jawad Soumik, M. M., Salvi Md Farhavi, S., Eva, F., Sinha, T., & Alam, M. S. (2019). Employing Machine Learning techniques on Sentiment Analysis of Google Play Store Bangla Reviews. 2019 22nd International Conference on Computer and Information Technology (ICCIT), 1–5. https://doi.org/10.1109/ICCIT48885.2019.9038348
Kusnawi, K., & Hendra Wijaya, A. (2021). Sentiment Analysis of Pancasila Values in Social Media Life Using the Naive Bayes Algorithm. 2021 International Seminar on Application for Technology of Information and Communication (ISemantic), 96–101. https://doi.org/10.1109/iSemantic52711.2021.9573194
Lee, H., Jung, S., Kim, M., & Kim, S. (2017). Synthetic minority over-sampling technique based on fuzzy c-means clustering for imbalanced data. 2017 International Conference on Fuzzy Theory and Its Applications (IFUZZY), 1–6. https://doi.org/10.1109/iFUZZY.2017.8311793
Samanvitha, S., Bindiya, A. R., Sudhanva, S., & Mahanand, B. S. (2021). Naïve Bayes Classifier for depression detection using text data. 2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT), 418–421. https://doi.org/10.1109/ICEECCOT52851.2021.9708014
Sindhuja, M., Nitin, K. S., & Devi, K. S. (2023). Twitter Sentiment Analysis using Enhanced TF-DIF Naive Bayes Classifier Approach. 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), 547–551. https://doi.org/10.1109/ICCMC56507.2023.10084106
Sumanto, Sugiarti, Y., Supriyatna, A., Carolina, I., Amin, R., & Yani, A. (2021). Model Naïve Bayes Classifiers For Detection Apple Diseases. 2021 9th International Conference on Cyber and IT Service Management (CITSM), 1–4. https://doi.org/10.1109/CITSM52892.2021.9588801
Vijay, V., & Verma, P. (2023a). Variants of Naïve Bayes Algorithm for Hate Speech Detection in Text Documents. 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), 18–21. https://doi.org/10.1109/AISC56616.2023.10085511
Vijay, V., & Verma, P. (2023b). Variants of Naïve Bayes Algorithm for Hate Speech Detection in Text Documents. 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), 18–21. https://doi.org/10.1109/AISC56616.2023.10085511
Yerlekar, A., Mungale, N., & Wazalwar, S. (2021). A multinomial technique for detecting fake news using the Naive Bayes Classifier. 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), 1–5. https://doi.org/10.1109/ICCICA52458.2021.9697244
Yuda Lesmana, R., Andarsyah, R., Sariasih No, J., Bandung, K., & Barat, J. (2022). Model Klasifikasi Multinomial Naïve Bayes Untuk Analisis Sentiment Terkait Non-Fungible Token. In Jurnal Teknik Informatika (Vol. 14, Issue 3).
Published
2023-12-25
Section
Articles
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