EVALUATION OF USER PERCEPTIONS AND SATISFACTION THROUGH SENTIMENT ANALYSIS NEWS APPLICATIONS WITH NAIVE BAYES

Authors

  • Aldiansyah Kusuma Universitas Bina Sarana Informatika
  • Diaz Aditya Yudha Universitas Bina Sarana Informatika
  • Muhammad Bahril Afwa Universitas Bina Sarana Informatika
  • Hanafi Eko Darono Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.33480/techno.v20i2.7356

Keywords:

indoBERT, Naive Bayes, Online News Application , Sentiment Analysis , SMOTE

Abstract

The development of digital technology has driven the transformation of mass media into online news platforms such as Detikcom, Kompas.id, and CNN Indonesia. Competition among these news applications has created the need to evaluate user perceptions of service quality. This study aims to analyze user sentiment toward the three news applications based on reviews from the Google Play Store. The methods employed include web scraping, text pre-processing, labeling using the IndoBERT model, feature extraction with the TF-IDF method, and sentiment classification with the Naive Bayes algorithm. To address class imbalance in the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that the Naive Bayes model achieved high accuracy, namely 88.5% for Kompas.id, 88.8% for Detikcom, and 90.8% for CNN Indonesia. The analysis also revealed that positive reviews are more dominant, although recurring criticisms were identified regarding advertisements and technical performance of the applications. The use of Generative AI further assisted in automatically summarizing opinions and sentiment patterns. These findings provide valuable insights for developers in enhancing user experience and refining the features of digital news applications

References

Arlovin, T., Kusrini, & Kusnawi. (2024). Analisis Sentimen Review Pengguna Aplikasi Fizzo Novel Di Google Play Menggunakan Algoritma Naive Bayes. Jurnal Informatika Teknologi Dan Sains (Jinteks), 6(1), 65–70. https://doi.org/10.51401/jinteks.v6i1.3909

Br Sinulingga, J. E., & Sitorus, H. C. K. (2024). Analisis Sentimen Opini Masyarakat terhadap Film Horor Indonesia Menggunakan Metode SVM dan TF-IDF. Jurnal Manajemen Informatika (JAMIKA), 14(1), 42–53. https://doi.org/10.34010/jamika.v14i1.11946

Ernianti Hasibuan, & Elmo Allistair Heriyanto. (2022). Analisis Sentimen Pada Ulasan Aplikasi Amazon Shopping Di Google Play Store Menggunakan Naive Bayes Classifier. Jurnal Teknik Dan Science, 1(3), 13–24. https://doi.org/10.56127/jts.v1i3.434

Fatkhudin, A., Artanto, F. A., & Safli, N. A. (2024). Decision Tree Berbasis SMOTE Dalam Analisis Sentimen Penggunaan Artificial Intelligence Untuk Skripsi. REMIK: Riset Dan E …, 8(April), 494–505. Retrieved from https://www.jurnal.polgan.ac.id/index.php/remik/article/view/13531%0Ahttps://www.jurnal.polgan.ac.id/index.php/remik/article/download/13531/2453

Haas, J., Yolland, W., & Rabus, B. (2022). Inducing Early Neural Collapse in Deep Neural Networks for Improved Out-of-Distribution Detection. 1–19. Retrieved from http://arxiv.org/abs/2209.08378

Hapsari, S. K., & Priliantini, A. (2025). PROSES PRODUKSI BERITA PADA LAMAN. 7(1), 57–73.

Kusnia, U., & Kurniawan, F. (2022). Analisis Sentimen Review Aplikasi Media Berita Online Pada Google Play menggunakan Metode Algoritma Support Vector Machines (SVM) Dan Naive Bayes INFO ARTIKEL ABSTRAK. Jurnal Keilmuan Dan Aplikasi Teknik Informatika, 14(1)(36), 24–25. Retrieved from https://doi.org/10.35891/explorit

Larasati, F. A., Ratnawati, D. E., & Hanggara, B. T. (2022). Analisis Sentimen Ulasan Aplikasi Dana dengan Metode Random Forest. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 6(9), 4305–4313.

Nadira, A., Setiawan, N. Y., & Purnomo, W. (2023). Analisis Sentimen Pada Ulasan Aplikasi Mobile Banking Menggunakan Metode Naïve Bayes Dengan Kamus Inset. Indexia, 5(01), 35. https://doi.org/10.30587/indexia.v5i01.5138

Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. Jurnal Sains Komputer & Informatika (J-SAKTI, 5(2), 697–711.

Nurtikasari, Y., Syariful Alam, & Teguh Iman Hermanto. (2022). Analisis Sentimen Opini Masyarakat Terhadap Film Pada Platform Twitter Menggunakan Algoritma Naive Bayes. INSOLOGI: Jurnal Sains Dan Teknologi, 1(4), 411–423. https://doi.org/10.55123/insologi.v1i4.770

Rina Noviana, & Isram Rasal. (2023). Penerapan Algoritma Naive Bayes Dan Svm Untuk Analisis Sentimen Boy Band Bts Pada Media Sosial Twitter. Jurnal Teknik Dan Science, 2(2), 51–60. https://doi.org/10.56127/jts.v2i2.791

Samiaji, A., Hananto, B., & Kom, S. (2022). Analisis Sentimen Review Aplikasi Berita Online Pada Google Play Menggunakan Metode Naïve Bayes Studi Kasus: Tribunnews.Com. Seminar Nasional Mahasiswa Ilmu Komputer Dan Aplikasinya (SENAMIKA), (2), 733–743.

Septiani, D., & Isabela, I. (2022). Analisis Term Frequency Inverse Document Frequency (Tf-Idf) Dalam Temu Kembali Informasi Pada Dokumen Teks. SINTESIA: Jurnal Sistem Dan Teknologi Informasi Indonesia, 1(1), 81–88.

Tanggraeni, A. I., & Sitokdana, M. N. N. (2022). Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(2), 785–795. https://doi.org/10.35957/jatisi.v9i2.1835

Tarwoto, Nugroho, R., Azka, N., & Graha, W. S. R. (2025). Analisis Sentimen Ulasan Aplikasi Mobile JKN di Google PlayStore Menggunakan IndoBERT. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 9(2), 495–505. https://doi.org/10.35870/jtik.v9i2.3340

Downloads

Published

2025-09-30

How to Cite

Kusuma, A., Diaz Aditya Yudha, Muhammad Bahril Afwa, & Hanafi Eko Darono. (2025). EVALUATION OF USER PERCEPTIONS AND SATISFACTION THROUGH SENTIMENT ANALYSIS NEWS APPLICATIONS WITH NAIVE BAYES. Jurnal Techno Nusa Mandiri, 20(2), 167–178. https://doi.org/10.33480/techno.v20i2.7356