CLASSIFICATION OF USER REVIEW SENTIMENT TOWARD PAYLATER SERVICES ON THE KREDIVO AND AKULAKU APPS USING NAÏVE BAYES

Sang Dara Parameswari; Muharman Lubis; Sinung Suakanto

doi:10.62567/micjo.v3i3.2727

Authors

Sang Dara Parameswari Telkom University
Muharman Lubis Telkom University
Sinung Suakanto Telkom University

DOI:

https://doi.org/10.62567/micjo.v3i3.2727

Keywords:

Sentiment Analysis, Paylater, Kredivo, Akulaku, Multinomial Naïve Bayes

Abstract

PayLater services are one of the rapidly growing digital financial innovations widely utilised in fintech apps in Indonesia, including Kredivo and Akulaku. User reviews on the Google Play Store reflect a range of experiences, from satisfaction with the ease of use of the service to complaints regarding bills, interest rates, late payment fees, credit limits, and app performance. This study aims to classify the sentiment of user reviews regarding PayLater services on the Kredivo and Akulaku apps using the Multinomial Naïve Bayes algorithm. Data was collected via web scraping from the Google Play Store and automatically labelled based on user ratings, with ratings of 1-2 classified as negative sentiment and ratings of 4-5 as positive sentiment, whilst a rating of 3 was excluded as it was considered ambiguous. Following a preprocessing stage comprising cleaning, case folding, tokenisation, stopword removal, and stemming, as well as feature extraction using TF-IDF, 3,652 reviews were obtained with a training-to-test data split ratio of 80:20. The results indicate that positive sentiment dominates the dataset at 56.49%, whilst negative sentiment accounts for 43.51%. Analysis by application revealed that Kredivo was dominated by positive sentiment (68.20%), whilst Akulaku was dominated by negative sentiment (51.70%). The Naïve Bayes multinomial model achieved an accuracy of 84.13%, with average precision, recall, and F1-score values of 0.84, demonstrating good and balanced classification performance across both sentiment classes.

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References

Afif, R., & Nugroho, K. (2025). Analisis Sentimen dan Prediksi Ulasan Pada Aplikasi Info BMKG.

Aghnia, R., Wiguna, R., & Rifai, A. I. (2021). Analisis Text Clustering Masyarakat Di Twitter Mengenai Omnibus Law Menggunakan Orange Data Mining. Journal of Information Systems and Informatics, 3(1). http://journal-isi.org/index.php/isi

Aji, S., & Fatma Wati, F. (2020). Analisa Sentimen Terhadap Review Fintech Dengan Metode Naive Bayes Classifier Dan K-Nearest Neighbor. Jurnal Sains Dan Manajemen, 8(1).

Amrie, S., Kurniawan, S., Windiatmaja, J. H., & Ruldeviyani, Y. (2022). Analysis of Google Play Store’s Sentiment Review on Indonesia’s P2P Fintech Platform. https://doi.org/10.1109/DELCON54057.2022.9753108

Ariany, F., Tri Prastowo, A., Ahmad, I., & Jafar Adrian, Q. (2026). Algoritma Naïve Bayes dalam Analisis Komperatif Sentimen Dompet Digital (Vol. 20, Number 1).

Fahrezi Putra Ichsansyah, R., & Korespondensi, P. (2026). PERBANDINGAN ALGORITMA NAÏVE BAYES DAN RANDOM FOREST DALAM KLASIFIKASI SENTIMEN ULASAN PENGGUNA KREDIVO DI PLAY STORE COMPARISON OF NAÏVE BAYES AND RANDOM FOREST ALGORITHMS IN SENTIMENT CLASSIFICATION OF KREDIVO USER REVIEWS ON THE PLAY STORE. 13(2), 297–308.

Hamad, M. M., Salih, M. A., & Jaleel, R. A. (2021). Sentiment Analysis of Restaurant Reviews in Social Media using Naïve Bayes (Vol. 5). http://arqiipubl.com/ams

Helmayanti, S. A., Hamami, F., & Fa’rifah, R. Y. (2023). PENERAPAN ALGORITMA TF-IDF DAN NAÏVE BAYES UNTUK ANALISIS SENTIMEN BERBASIS ASPEK ULASAN APLIKASI FLIP PADA GOOGLE PLAY STORE. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 4(3), 1822–1834. https://doi.org/10.35870/jimik.v4i3.415

Indriani, F., & Wibowo, W. (2022). Sentiment analysis data by ShopeePayLater Twitter’s opinion using naïve Bayes classifier. AIP Conference Proceedings, 2668, 70018. https://doi.org/10.1063/5.0111732

Iqbal, M., Afdal, M., & Novita, R. (2024). Implementasi Algoritma Support Vector Machine Untuk Analisa Sentimen Data Ulasan Aplikasi Pinjaman Online di Google Play Store. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(4), 1244–1252. https://doi.org/10.57152/malcom.v4i4.1435

Kurniawan, Y. I., Sidiq, R. H., Widadi, A. D. U., Yubiharto, A. R. A., Gibran, A. K., Aditama, M. R., & Laksono, F. A. T. (2025). Naive Bayes Classifier with SMOTE for Sentiment Analysis of Blibli App Reviews on The Google Play Store. Jurnal Penelitian Inovatif, 5(3), 2675–2688. https://doi.org/10.54082/jupin.1842

Mubarokah, A. S. (2025). PENERAPAN ALGORITMA NAÏVE BAYES UNTUK ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI ADAKAMI DI GOOGLE PLAY STORE. Jurnal Informatika Dan Teknik Elektro Terapan, 13(3). https://doi.org/10.23960/jitet.v13i3.6191

Parameswari, S. D., Lubis, M., Suakanto, S., Ramadhan, Y. Z., Amanah, R. N., Dila, R. A., Jurusan, ), & Informasi, S. (2025). Studi Perbandingan Naïve Bayes dan Support Vector Machine (SVM) dalam Analisis Sentimen Pengguna Metaverse. Jurnal Teknologi Dan Manajemen Industri Terapan (JTMIT), 4(3).

Penelitian, J., Masyarakat, P., Soliha, A. N., Munandar, T. A., & Yasir, M. (2023). Sentiment Analysis of the Use of Digital Banking Service Applications in Google Play Store Reviews Using Naïve Bayes Method 1. International Journal of Information Technology and Computer Science Applications.

Rahman, Z., Sakinah, P., Hendra, Y., Satria, B., Maulana, F., & Ayun, A. Q. (2024). Sentiment Analysis of Gojek App Reviews on Google Play Store with Natural Language Processing Using Naive Bayes’ Algorithm-Zumardi Rahman et.al Sentiment Analysis of Gojek App Reviews on Google Play Store with Natural Language Processing Using Naive Bayes’ Algorithm. 06. https://doi.org/10.54209/jatilima.v6i03.1189

Rahmi Anissa, C., Ditha Tania, K., & Kurnia Sari, W. (2025). Sentiment Analysis on Google Play Store Reviews to Measure User Perception of the Gojek Application Using CNN. In Journal of Applied Informatics and Computing (JAIC) (Vol. 9, Number 6). http://jurnal.polibatam.ac.id/index.php/JAIC

Ryando, M. B., Iqbal, M., & Syahidah, K. (2025). Analisis Text Mining terhadap Penggunaan Paylater Menggunakan Naïve Bayes Classifier. Jurnal Tekno Insentif, 19(2), 168–184. https://doi.org/10.36787/jti.v19i2.2065

Safira, A., Masyarakat…, A. S., & Hasan, F. N. (2023). ANALISIS SENTIMEN MASYARAKAT TERHADAP PAYLATER MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER. Jurnal Sistem Informasi, 5(1).

Sidik, F., Suhada, I., Anwar, A. H., & Hasan, F. N. (2022). Analisis Sentimen Terhadap Pembelajaran Daring Dengan Algoritma Naive Bayes Classifier. Jurnal Linguistik Komputasional (JLK), 5(1), 34. https://doi.org/10.26418/jlk.v5i1.79

Susanti, S., Tazidan, A., & Nuryawan, O. (2026). Analisis Sentimen terhadap Pinjaman Online Kredivo Menggunakan Algoritma Naïve Bayes dan SVM dengan Metode SEMMA.

Wahyuta Kusuma, F., Dermawan, P., Samsie, I., Salman, N., Aminah Dinayati Ghani, S., Rahma Dani Rachman, S., & Author, K. (2024). ONLINE DENGAN METODE SUPPORT VECTOR MACHINE (SVM). 4(1).

Wijaya, N., & Panjaitan, E. S. (2024). Analisis Sentimen Ulasan Aplikasi Instagram di Google Play Store: Pendekatan Multinomial Naive Bayes dan Berbasis Leksikon. Building of Informatics, Technology and Science (BITS), 6(2), 921–929. https://doi.org/10.47065/bits.v6i2.5615