ANALISIS SENTIMEN PENGGUNA APLIKASI MYVALUE KOMPAS GRAMEDIA PADA ULASAN GOOGLE PLAY  MENGGUNAKAN ALGORITMA NAÏVE BAYES

Toebagus Iqbal; Dian Ade Kurnia; Yudhistira Arie Wijaya; Edi Tohidi; Edi Wahyudin

Authors

Toebagus Iqbal STMIK IKMI Cirebon, Indonesia
Dian Ade Kurnia STMIK IKMI Cirebon, Indonesia
Yudhistira Arie Wijaya STMIK IKMI Cirebon, Indonesia
Edi Tohidi STMIK IKMI Cirebon, Indonesia
Edi Wahyudin STMIK IKMI Cirebon, Indonesia

Keywords:

Analisis Sentimen, Naïve Bayes, TF-IDF, ulasan Google Play, MyValue

Abstract

Penelitian ini menganalisis sentimen pengguna aplikasi MyValue Kompas Gramedia melalui ulasan Google Play untuk memahami persepsi pengguna terhadap layanan aplikasi. Tujuan penelitian adalah mengidentifikasi sentimen positif dan negatif menggunakan pendekatan yang sistematis. Penelitian menggunakan algoritma Naïve Bayes dengan tahapan pengumpulan data ulasan, pembersihan teks, tokenisasi, penghapusan stopword, stemming, pembobotan TF-IDF, penyeimbangan data menggunakan SMOTE, dan evaluasi model dengan k-fold cross validation. Hasil menunjukkan bahwa algoritma Naïve Bayes mampu memberikan performa klasifikasi yang tinggi, ditunjukkan dengan nilai akurasi, presisi, recall, dan F1-score yang stabil pada ulasan pengguna. Model dapat mengenali pola sentimen yang muncul dalam teks ulasan, termasuk kecenderungan keluhan pengguna terkait performa aplikasi serta apresiasi terhadap fitur tertentu. Temuan penelitian menunjukkan bahwa kombinasi metode text mining dan Naïve Bayes efektif untuk memahami opini pengguna berbahasa Indonesia dalam konteks aplikasi digital. Penelitian ini memberikan gambaran menyeluruh mengenai kualitas layanan berdasarkan sentimen pengguna serta dapat menjadi dasar pengambilan keputusan bagi pengembang dalam meningkatkan fitur aplikasi

References

Aji, A. F., Winata, G. I., Koto, F., Cahyawijaya, S., Romadhony, A., Mahendra, R., Kurniawan, K., Moeljadi, D., Prasojo, R. E., Baldwin, T., Lau, J. H., & Ruder, S. (n.d.). One Country , 700 + Languages : NLP Challenges for Underrepresented Languages and Dialects in Indonesia.

Aljrees, T., Umer, M., Saidani, O., Almuqren, L., Ishaq, A., Alsubai, S., Eshmawi, A. A., & Ashraf, I. (2024). Contradiction in text review and apps rating: Prediction using textual features and transfer learning. PeerJ Computer Science, 10, e1722. https://doi.org/10.7717/peerj-cs.1722

Andono, P. N., & Pramunendar, R. A. (2023). Performance Evaluation of Classification Algorithm for Movie Review Sentiment Analysis. 22(1), 7–14. https://doi.org/10.47839/ijc.22.1.2873

Anggara, M. B., Informasi, F. T., & Bandung, U. B. (2025). PERBANDINGAN NAÏVE BAYES DAN SVM DALAM ANALISIS SENTIMEN. 20, 32–42.

Anti, R. S., & Ruhyana, N. (2024). ANALYSIS OF PUBLIC SENTIMENT TOWARDS 2024 PRESIDENTIAL CANDIDACY USING NAÏVE BAYES ALGORITHM. 7(1).

Asri, Y., Kuswardani, D., Suliyanti, W. N., & Manullang, Y. O. (2025). Sentiment analysis based on Indonesian language lexicon and IndoBERT on user reviews PLN mobile application. 38(1), 677–688. https://doi.org/10.11591/ijeecs.v38.i1.pp677-688

Astuti, Y., Ruldeviyani, Y., Salbari, F., & Prayogi, A. (2026). JURNAL RESTI. 5(158), 389–396.

Bordoloi, M., & Biswas, S. K. (2023). Sentiment analysis: A survey on design framework, applications and future scopes. Artificial Intelligence Review. https://doi.org/10.1007/s10462-023-10442-2

Bousdekis, A., Lepenioti, K., & Apostolou, D. (2021). A Review of Data-Driven Decision-Making Methods for Industry 4 . 0 Maintenance Applications.

Cahyawijaya, S., Lovenia, H., Aji, A. F., Winata, G. I., Wilie, B., Koto, F., Mahendra, R., Wibisono, C., Romadhony, A., Vincentio, K., Santoso, J., Moeljadi, D., Nityasya, M. N., Adilazuarda, M. F., & Ignatius, R. (2022). NusaCrowd : Open Source Initiative for Indonesian NLP Resources.

Cui, J., Wang, Z., Ho, S., & Cambria, E. (2023). Survey on sentiment analysis : evolution of research methods and topics. In Artificial Intelligence Review (Vol. 56, Issue 8). Springer Netherlands. https://doi.org/10.1007/s10462-022-10386-z

Faisti, M. J., Kusumodestoni, R. H., Wahyu, G., & Wibowo, N. (2025). Mental Health Classification Using Naïve Bayes and Random Forest Algorithms. 9(4), 1740–1750.

Fiddin, F., Hidayat, T., & Djamaludin, D. (2025). Analisis Sentimen Ulasan Produk Sayur di Tokopedia Menggunakan Model Support Vector Machine Dengan Representasi TF-IDF. Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika Dan Komputer), 24(2), 162–172. https://doi.org/10.53513/jis.v24i2.11479

Fikriah, F. K., Devi, A., & Ariyanto, P. (2025). NAÏVE BAYES AND SUPPORT VECTOR MACHINE BASED ON OPTIMIZATION FOR PUBLIC SENTIMENT ANALYSIS POST-2024 ELECTION. 8(2), 148–152. https://doi.org/10.33387/jiko.v8i2.10147

Gasparetto, A., Marcuzzo, M., & Zangari, A. (2022). A Survey on Text Classification Algorithms : From Text to Predictions. 1–39.

Hameed, Q. A. (2025). Leveraging Traditional Machine Learning and TF-IDF for Robust Fake News Detection. International Journal of Computational and Electronic Aspects in Engineering, 6(3), 143–152. https://doi.org/10.26706/ijceae.6.3.20250605

Hardiansyah, D., Abdul, R. Z., & Said, M. (2024). The Classification Method is Used for Sentiment Analysis in My Telkomsel. 8(2).

He, B., Zhu, L., Wang, X., Zhang, H., & Shi, J. (2022). Research on Text Classification based on Deep Learning. Scientific Journal of Technology, 4(7), 119–128. https://doi.org/10.54691/sjt.v4i7.1286

Id, Y. H., Id, D. M., & Yigal, Y. (2020). The influence of preprocessing on text classification using a bag-of-words representation. 1–22. https://doi.org/10.1371/journal.pone.0232525

Kaur, G., & Sharma, A. (2023). A deep learning ‑ based model using hybrid feature extraction approach for consumer sentiment analysis. Journal of Big Data. https://doi.org/10.1186/s40537-022-00680-6

Kim, J., & Chung, J. (2024). Analysis of Service Quality in Smart Running Applications Using Big Data Text Mining Techniques. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 3352–3369. https://doi.org/10.3390/jtaer19040162

Kumar, R., & Goswami, B. K. (2024). Naive Bayes in Focus : A Thorough Examination of its Algorithmic Foundations and Use Cases. 9(5).

Letier, E., Perini, A., & Susi, A. (2022). Analysing app reviews for software engineering :

Li, Q., Peng, H. A. O., Li, J., Xia, C., Yang, R., Sun, L., Yu, P. S., & He, L. (2022). A Survey on Text Classification : From Traditional. 13(2). https://doi.org/10.1145/3495162

Lin, C. H., & Nuha, U. (2023). Sentiment analysis of Indonesian datasets based on a hybrid deep ‑ learning strategy. Journal of Big Data. https://doi.org/10.1186/s40537-023-00782-9

Luthfi, M., Martanto, F., & Istiono, W. (2024). Sentiment Analysis of M-Paspor App Reviews Using Multinomial Naive Bayes. 11(10), 311–326. https://doi.org/10.33168/JLISS.2024.1017

Malik, N., & Bilal, M. (2024). Natural language processing for analyzing online customer reviews : a survey , taxonomy , and open research challenges. 1–38. https://doi.org/10.7717/peerj-cs.2203

Mallavarapu, R. (2025). Accuracy and Execution Time in Fake News Detection : A Comparative Analysis of Logistic Regression and Naïve Bayes. February.

Mao, Y., Liu, Q., & Zhang, Y. (2024). Journal of King Saud University - Computer and Sentiment analysis methods , applications , and challenges : A systematic literature review. Journal of King Saud University - Computer and Information Sciences, 36(4), 102048. https://doi.org/10.1016/j.jksuci.2024.102048

March, V. N., Shafiq, M., Ng, H., Tzen, T., Yap, V., & Goh, V. T. (2022). Performance of Sentiment Classifiers on Tweets of Different Clothing Brands. 1(1).

Maulana, F., Abdullah, M. A., & Sari, J. (2022). SENTIMENT ANALYSIS ON THE TWITTER PSSI PERFORMANCE USING TEXT MINING WITH THE NAÏVE BAYES ALGORITHM. 18(2), 211–216. https://doi.org/10.33480/pilar.v18i2.3938

Nur, M. A., Umar, N., Feng, Z., & Gani, H. (2025). EVALUATION OF INDOBERT AND ROBERTA : PERFORMANCE OF INDONESIAN LANGUAGE TRANSFORMER MODELS IN SENTIMENT. 8(0173), 121–127. https://doi.org/10.33387/jiko.v8i2.9988

Olalere, A. (2024). Implementation and Comparison of Deep Learning , and Naïve Bayes for Language Processing. XI(2321), 545–552. https://doi.org/10.51244/IJRSI

Orlu, G. U., Abdullah, R. Bin, Zaremohzzabieh, Z., Jusoh, Y. Y., Asadi, S., Qasem, Y. A. M., Nor, R., Nor, H., Mohd, W., & Nasir, M. (2023). A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era.

Palomino, M. A. (2022). applied sciences Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis.

Pota, M., Ventura, M., Fujita, H., & Esposito, M. (2021). Multilingual evaluation of pre-processing for BERT-based sentiment analysis of tweets. Expert Systems with Applications, 181, 115119. https://doi.org/https://doi.org/10.1016/j.eswa.2021.115119

Pramudja, S. E., Umaidah, Y., & Suharso, A. (2023). Implementation of Information Gain for Sentiment Analysis of PSE Policy using Naïve Bayes Algorithm. 7(2), 224–230.

Safarah, K. A., Inan, D. I., Juita, R., & Sirait, V. A. L. (2024). ANALYSING SERVICE QUALITY USING SENTIMENT ANALYSIS AND TOPIC MODELING : A CASE STUDY OF THE LIVIN MANDIRI APPLICATION. 8(2), 209–220.

Saka, H. K., & Prasetyaningrum, P. T. (2025). Sentiment Analysis and Classification of User Reviews of the ’ Access by KAI ’ Application Using Machine Learning Methods to Improve Service Quality. 7(2), 1418–1442. https://doi.org/10.51519/journalisi.v7i2.1099

Samudera, B. D., Al, H., & Aidilof, K. (2024). Sentiment Analysis of User Reviews on BSI Mobile and Action Mobile Applications on the Google Play Store Using Multinomial Naive Bayes Algorithm. 4(4), 101–112.

Semary, N. A., Ahmed, W., Amin, K., & Id, P. P. (2024). Enhancing machine learning-based sentiment analysis through feature extraction techniques. https://doi.org/10.1371/journal.pone.0294968

Shen, Q., Han, S., Han, Y., & Id, X. C. (2025). User review analysis of dating apps based on text mining. 1–19. https://doi.org/10.1371/journal.pone.0283896

Singgalen, Y. A. (2024). Jurnal Mantik Performance evaluation of SVM with synthetic minority over-sampling technique in sentiment classification. 8(1).

Sitorus, R. A., Zufria, I., & Utara, S. (2024). Application of the Naïve Bayes Algorithm in Sentiment Analysis of Using the Shopee Application on the Play Store 1,2. 53–66.

Sulaiman, C., Salamah, U. G., Oktavitati, R., Faizah, S., & Info, A. (2024). Sentiment Analysis and Classification of Public Opinion on Prabowo Subianto Using Naïve Bayes on Twitter. 03(02), 56–62. https://doi.org/10.56904/j-gers.v3i2.98

Tan, K. L., & Lee, C. P. (2023). applied sciences A Survey of Sentiment Analysis : Approaches , Datasets , and Future Research.

Trihapningsari, D., Widyasuri, A., Putri, M. A., & Fatihin, A. (2025). Sentiment Analysis of ChatGPT Exploration Based on Opinions on Platform X Using Naïve Bayes Algorithm. 4(July 2024), 94–101.

Vebriansyah, D. A., Komang, N., Yasari, K., & Samudra, D. I. (2025). Sentiment Analysis of KAI Access App Customer Reviews to Improve Customer Service Using Natural Language Processing.

Wang, K., Ding, Y., & Han, S. C. (2024). Graph neural networks for text classification : a survey. In Artificial Intelligence Review (Vol. 57, Issue 8). Springer Netherlands. https://doi.org/10.1007/s10462-024-10808-0

Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731–5780. https://doi.org/10.1007/s10462-022-10144-1

Winata, G. I., Aji, A. F., Cahyawijaya, S., Mahendra, R., Koto, F., Romadhony, A., Kurniawan, K., Moeljadi, D., Prasojo, R. E., Fung, P., Baldwin, T., & Lau, J. H. (2023). NusaX : Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages. 815–834.

Xu, Y., Cao, H., Du, W., & Wang, W. (2022). A Survey of Cross ‑ lingual Sentiment Analysis : Methodologies , Models and Evaluations. Data Science and Engineering, 7(3), 279–299. https://doi.org/10.1007/s41019-022-00187-3