Perbandingan Metode Naive Bayes, Decision Tree, dan Support Vector Machine dalam Analisis Sentimen Pengguna terhadap Aplikasi E-Wallet Dana
DOI:
https://doi.org/10.62712/juktisi.v5i1.1267Keywords:
Sentiment Analysis, DANA, Naive Bayes, Decision Tree, Support Vector Machine, SMOTEAbstract
This research is motivated by the rapid growth of financial technology in Indonesia, where the DANA application has become the most popular digital wallet (e-wallet) with over 200 million registered users . The high usage of this application results in an abundance of reviews on the Google Play Store, representing both customer satisfaction and complaints . The problem addressed in this research is how to automatically process these textual reviews and determine the best classification method among the three tested Machine Learning algorithms . This research aims to analyze and compare the accuracy performance of Naive Bayes, Decision Tree, and Support Vector Machine (SVM) algorithms in classifying user sentiment . The method used in this research is a computational quantitative approach, utilizing a secondary data collection technique consisting of 50,000 reviews from the Google Play Store via Kaggle . The analysis process was conducted by applying five stages of text preprocessing, feature weighting using Term Frequency-Inverse Document Frequency (TF-IDF), handling data imbalance using the Synthetic Minority Over-sampling Technique (SMOTE), data splitting (80% training data and 20% testing data), and model evaluation using a Confusion Matrix . The results showed that the Naive Bayes algorithm had the most superior performance with an accuracy rate of 80%, followed by Decision Tree and Support Vector Machine (SVM), each obtaining an accuracy of 78% . Therefore, it can be concluded that the Naive Bayes algorithm is the most optimal and stable method for conducting sentiment analysis classification on e-wallet application review text data after the class distribution is equalized.
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[1] A. Z. Yonatan, “5 Platform E-Wallet Terpopuler di Indonesia,” 5 Platf. E-Wallet Terpopuler di Indones., 2025, [Online]. Available: https://goodstats.id/article/5-platform-e-wallet-terpopuler-di-indonesia-2025-R1GNX
[2] E. Kure, “Pengguna Platform Dana Tembus 200 Juta,” Pengguna Platf. Dana Tembus 200 Juta, 2025, [Online]. Available: https://investor.id/finance/387968/2024-pengguna-platform-dana-tembus-200-juta
[3] M. A. Sriani; Armansyah; Rambe, “Analisis Sentimen Pengguna Aplikasi Dana Menggunakan Metode Naïve Bayes,” Anal. Sentimen Pengguna Apl. Dana Menggunakan Metod. Naïve Bayes, 2024, [Online]. Available: https://www.researchgate.net/publication/385869493_Analisis_Sentimen_Pengguna_Aplikasi_Dana_Menggunakan_Metode_Naive_Bayes
[4] R. D. Cahyani and P. T. Prasetyaningrum, “Sentiment Analysis of User Reviews for AI Applications : Evaluating SVM , Logistic Regression , and Random Forest,” vol. 8, no. 1, pp. 1–27, 2026, doi: 10.63158/journalisi.v8i1.1366.
[5] N. V Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE : Synthetic Minority Over-sampling Technique,” vol. 16, pp. 321–357, 2022.
[6] C. Haryawan and Y. M. K. Ardhana, “Analisa Perbandingan Teknik Oversampling SMOTE,” JIRE (Jurnal Inform. Rekayasa Elektron., vol. 6, no. 1, pp. 73–78, 2023.
[7] N. Hadi and D. Sugiarto, “Analisis Sentimen Pembangunan IKN pada Media Sosial X Menggunakan Algoritma SVM , Logistic Regression dan Naïve Bayes,” vol. 10, no. 1, pp. 37–49, 2025, doi: 10.30591/jpit.v10i1.7106.
[8] A. Lowell, A. Lowell, K. Candra, and E. Indra, “Perbandingan Metode Support Vector Machine ( SVM ) Dan Naive Bayes Pada Analisis Sentimen Ulasan Aplikasi OVO JURNAL MEDIA INFORMATIKA [ JUMIN ],” vol. 6, no. 2, pp. 896–905, 2025.
[9] D. S. Nugroho, I. F. Hanif, and M. A. Hasbi, “Sentiment Analysis of Alleged 2024 Election Fraud Based on Tweets Using the Naïve Bayes Classifier Algorithm Analisis Sentimen Dugaan Pelanggaran Pemilu 2024 Berdasarkan Tweet Menggunakan Algoritma Naïve Bayes Classifier,” vol. 4, no. July, pp. 1169–1176, 2024.
[10] R. Artikel, N. Luh, P. Chandra, R. A. Rahman, R. Venyutzky, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning,” vol. 7, no. April, pp. 47–58, 2021.
[11] A. M. Wahid, Turino, K. A. Nugroho, D. Titi Safitri4, and F. S. Utomo, “Optimasi Logistic Regression dan Random Forest untuk Deteksi Berita Hoax Optimasi Logistic Regression dan Random Forest untuk Deteksi Berita Hoax Berbasis Hyperparameter Optimization of Logistic Regression and Random Forest for Hoax News Detection Using T,” J. Pendidik. dan Teknol. Indones., vol. 4, no. January, pp. 381–392, 2025.
[12] B. Irawan, A. Bahtiar, N. Bayes, G. P. Store, and N. Bayes, “PENERAPAN METODE NAIVE BAYES PADA ANALISIS SENTIMEN APLIKASI,” vol. 8, no. 1, pp. 759–766, 2024.
[13] B. Hakim, “ANALISA SENTIMEN DATA TEXT PREPROCESSING PADA DATA MINING DENGAN MENGGUNAKAN MACHINE LEARNING DATA TEXT PRE-PROCESSING SENTIMENT ANALYSIS IN DATA MINING USING MACHINE LEARNING School of Computer Science and Technology , Harbin Institute of Technology,” vol. 4, no. 2, pp. 16–22, 2021.
[14] R. Wati, S. Ernawati, and H. Rachmi, “Pembobotan TF-IDF Menggunakan Naïve Bayes Pada Sentimen Masyarakat Mengenai Isu Kenaikan BIPIH TF-IDF Weighting Using Naïve Bayes on Public Sentiment on The Issue of Rising BIPIH,” vol. 13, no. April, pp. 84–93, 2023.
[15] M. A. Hermawan, A. Faqih, G. Dwilestari, T. Informatika, and S. Informasi, “IMPLEMENTASI AKURASI MODEL NAIVE BAYES MENGGUNAKAN SMOTE DALAM ANALISIS SENTIMEN PENGGUNA APLIKASI BRIMO,” vol. 13, no. 1, 2025.
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