ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI RUPARUPA PADA GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAÏVE BAYES

Indi Rahmawati; Dian Ade Kurnia; Khaerul Anam

Authors

Indi Rahmawati STMIK IKMI Cirebon, Indonesia
Dian Ade Kurnia STMIK IKMI Cirebon, Indonesia
Khaerul Anam STMIK IKMI Cirebon, Indonesia

Keywords:

Analisis Sentimen; Naïve Bayes; RupaRupa; Google Play Store; Text Mining

Abstract

Penelitian ini bertujuan untuk mengetahui kecenderungan sentimen ulasan pengguna aplikasi RupaRupa pada Google Play Store serta mengukur kemampuan algoritma Naïve Bayes dalam mengklasifikasikan sentimen positif dan negatif tersebut. Penelitian juga bertujuan untuk mengidentifikasi aspek layanan yang membentuk persepsi positif dan negatif pengguna terhadap aplikasi. Metode penelitian menggunakan teknik web scraping untuk mengumpulkan ulasan pengguna dari Google Play Store. Data yang diperoleh melalui proses pembersihan teks, normalisasi, dan penghapusan stopword sebelum dilakukan pembobotan kata menggunakan TF-IDF dilanjutkan dengan penerapan SMOTE (Synthetic Minority Over-sampling Technique) dan cross validation. Algoritma Naïve Bayes diterapkan sebagai metode klasifikasi sentimen, sedangkan evaluasi performa model dilakukan menggunakan confusion matrix. Dataset mencakup ulasan pengguna dari tahun 2019 hingga 2025 dengan variasi skor dan isi ulasan. Hasil penelitian menunjukkan bahwa ulasan positif mendominasi dataset, terutama terkait kecepatan layanan, kemudahan penggunaan, dan kualitas produk. Ulasan negatif sebagian besar berkaitan dengan masalah teknis seperti aplikasi yang lag, kendala checkout, dan keluhan pengiriman. Model Naïve Bayes menghasilkan performa yang stabil dalam memetakan sentimen pengguna. Identifikasi pola kata dan konteks ulasan memperlihatkan bahwa aspek teknis aplikasi memiliki pengaruh besar terhadap persepsi negatif, sedangkan aspek layanan dan pengalaman belanja mendukung sentimen positif. Temuan ini menegaskan bahwa analisis sentimen mampu memberikan gambaran terstruktur mengenai persepsi pengguna. Secara keseluruhan, penelitian ini menunjukkan bahwa Naïve Bayes dapat diterapkan untuk menganalisis sentimen ulasan aplikasi e-commerce secara efektif dan memberikan wawasan yang berguna untuk evaluasi layanan serta pengembangan aplikasi RupaRupa. Penelitian ini memberikan kontribusi pada penerapan machine learning dalam memahami perilaku pengguna berdasarkan data teks.

References

Abu-Alsondos, I. A. (2023). The impact of business intelligence system (BIS) on quality of strategic decision-making. International Journal of Data and Network Science, 7(4), 1901–1912. https://doi.org/10.5267/j.ijdns.2023.7.003

Adiningtyas, H., & Auliani, A. S. (2024). Sentiment analysis for mobile banking service quality measurement. Procedia Computer Science, 234, 40–50. https://doi.org/10.1016/j.procs.2024.02.150

Aftab, F., Bazai, S. U., Marjan, S., Baloch, L., Aslam, S., Amphawan, A., & Neo, T. K. (2023). A Comprehensive Survey on Sentiment Analysis Techniques. International Journal of Technology, 14(6), 1288–1298. https://doi.org/10.14716/ijtech.v14i6.6632

Amirkhalili, Y., & Wong, H. Y. (2025). Banking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews. http://arxiv.org/abs/2503.11861

Ashbaugh, L., & Zhang, Y. (2024). A Comparative Study of Sentiment Analysis on Customer Reviews Using Machine Learning and Deep Learning. Computers, 13(12). https://doi.org/10.3390/computers13120340

Asosiasi Penyelenggara Jasa Internet Indonesia. (n.d.). Retrieved November 16, 2025, from https://apjii.or.id/

Ataei, P., Regula, S., Staegemann, D., & Malgaonkar, S. (2024). Filtering Useful App Reviews Using Naïve Bayes—Which Naïve Bayes? AI (Switzerland), 5(4), 2237–2259. https://doi.org/10.3390/ai5040110

Aubaid, A. M., Mishra, A., & Mishra, A. (2024). Machine learning and rule-based embedding techniques for classifying text documents. International Journal of System Assurance Engineering and Management, 15(12), 5637–5652. https://doi.org/10.1007/s13198-024-02555-w

Ayash, L., Algarni, A., & Alqahtani, O. (2025). Advancements in feature selection and extraction methods for text mining: a review. Discover Applied Sciences, 7(8). https://doi.org/10.1007/s42452-025-07587-w

Blanquero, R., Carrizosa, E., Ramírez-Cobo, P., & Sillero-Denamiel, M. R. (2021). Variable selection for Naïve Bayes classification. Computers and Operations Research, 135, 105456. https://doi.org/10.1016/j.cor.2021.105456

Bordoloi, M., & Biswas, S. K. (2023). Sentiment analysis: A survey on design framework, applications and future scopes. Artificial Intelligence Review, 56(11), 12505–12560. https://doi.org/10.1007/s10462-023-10442-2

Carvalho, M., Pinho, A. J., & Brás, S. (2025). Resampling approaches to handle class imbalance: a review from a data perspective. Journal of Big Data, 12(1). https://doi.org/10.1186/s40537-025-01119-4

Catelli, R., Pelosi, S., & Esposito, M. (2022). Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian. Electronics (Switzerland), 11(3). https://doi.org/10.3390/electronics11030374

Çekik, R. (2025). Effective Text Classification Through Supervised Rough Set-Based Term Weighting. Symmetry, 17(1). https://doi.org/10.3390/sym17010090

Chai, C. P. (2023). Comparison of text preprocessing methods. Natural Language Engineering, 29(3), 509–553. https://doi.org/DOI: 10.1017/S1351324922000213

Chan, K. H., & Im, S. K. (2022). Sentiment analysis by using Naïve-Bayes classifier with stacked CARU. Electronics Letters, 58(10), 411–413. https://doi.org/10.1049/ell2.12478

Chen, H., Hu, S., Hua, R., & Zhao, X. (2021). Improved naive Bayes classification algorithm for traffic risk management. Eurasip Journal on Advances in Signal Processing, 2021(1). https://doi.org/10.1186/s13634-021-00742-6

Chowdhury, S., & Alzarrad, A. (2023). Applications of Text Mining in the Transportation Infrastructure Sector: A Review. Information (Switzerland), 14(4), 1–24. https://doi.org/10.3390/info14040201

Conciatori, M., Tran, N. T. C., Diez, Y., Valletta, A., Segalini, A., & Lopez Caceres, M. L. (2024). Plant Species Classification and Biodiversity Estimation from UAV Images with Deep Learning. Remote Sensing, 16(19), 1–24. https://doi.org/10.3390/rs16193654

Cui, J., Wang, Z., Ho, S. B., & 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

Dąbrowski, J., Letier, E., Perini, A., & Susi, A. (2022). Analysing app reviews for software engineering: a systematic literature review. Empirical Software Engineering, 27(2). https://doi.org/10.1007/s10664-021-10065-7

Davoodi, L., Mezei, J., & Heikkilä, M. (2025). Aspect-based sentiment classification of user reviews to understand customer satisfaction of e-commerce platforms. In Electronic Commerce Research (Issue 0123456789). Springer US. https://doi.org/10.1007/s10660-025-09948-4

Daza, A., González Rueda, N. D., Aguilar Sánchez, M. S., Robles Espíritu, W. F., & Chauca Quiñones, M. E. (2024). Sentiment Analysis on E-Commerce Product Reviews Using Machine Learning and Deep Learning Algorithms: A Bibliometric Analysisand Systematic Literature Review, Challenges and Future Works. International Journal of Information Management Data Insights, 4(2). https://doi.org/10.1016/j.jjimei.2024.100267

Defit, S., Windarto, A. P., & Alkhairi, P. (2024). Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization. Telematika, 17(1), 52–67. https://doi.org/10.35671/telematika.v17i1.2824

Felix, A., & Rembulan, G. D. (2023). Analysis of Key Factors for Improved Customer Experience, Engagement, and Loyalty in the E-Commerce Industry in Indonesia. APTISI Transactions on Technopreneurship, 5(2Sp), 196–208. https://doi.org/10.34306/att.v5i2sp.350

Gan, S., Shao, S., Chen, L., Yu, L., & Jiang, L. (2021). Adapting hidden naive bayes for text classification. Mathematics, 9(19). https://doi.org/10.3390/math9192378

Herhausen, D., Ludwig, S., Abedin, E., Haque, N. U., & de Jong, D. (2025). From words to Insights: Text analysis in business research. Journal of Business Research, 198(May), 115491. https://doi.org/10.1016/j.jbusres.2025.115491

Heti Aprilianti, Khothibul Umam, & Maya Rini Handayani. (2025). Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews. Journal of Applied Informatics and Computing, 9(3), 920–927. https://doi.org/10.30871/jaic.v9i3.9482

Hou, R., Ye, X., Zaki, H. B. O., & Omar, N. A. B. (2023). Marketing Decision Support System Based on Data Mining Technology. Applied Sciences (Switzerland), 13(7). https://doi.org/10.3390/app13074315

Islam, M. S., Kabir, M. N., Ghani, N. A., Zamli, K. Z., Zulkifli, N. S. A., Rahman, M. M., & Moni, M. A. (2024). “Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach.” In Artificial Intelligence Review (Vol. 57, Issue 3). Springer Netherlands. https://doi.org/10.1007/s10462-023-10651-9

Jain, S., Jain, S. K., & Vasal, S. (2024). An Effective TF-IDF Model to Improve the Text Classification Performance. Proceedings - 2024 13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024, 1066–1069. https://doi.org/10.1109/CSNT60213.2024.10545818

Jakha, H., El Houssaini, S., El Houssaini, M. A., Ajjaj, S., & Hadir, A. (2025). Optimizing Sentiment Analysis in Multilingual Balanced Datasets: A New Comparative Approach to Enhancing Feature Extraction Performance with ML and DL Classifiers. Applied System Innovation, 8(4), 1–23. https://doi.org/10.3390/asi8040104

Kaup, M., Wiktorowska-Jasik, A., Smacki, A., & Baszak, K. (2024). Information systems and technologies supporting decision-making processes in logistics companies. Procedia Computer Science, 246(C), 5506–5515. https://doi.org/10.1016/j.procs.2024.09.699

Kawan Lama Group - Bisnis Multisektor di Indonesia. (n.d.). Retrieved November 16, 2025, from https://www.kawanlamagroup.com/

KDD Process/Overview. (n.d.). Retrieved November 21, 2025, from https://www2.cs.uregina.ca/~dbd/cs831/notes/kdd/1_kdd.html

Leandro, J. O., & Fianty, M. I. (2025). Evaluation of Sentiment Analysis Methods for Social Media Applications: A Comparison of Support Vector Machines and Naïve Bayes. International Journal on Informatics Visualization, 9(2), 796–807. https://doi.org/10.62527/joiv.9.2.2905

Li, H., Yu, B. X. B., Li, G., & Gao, H. (2023). Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, 104707. https://doi.org/https://doi.org/10.1016/j.tourman.2022.104707

Lotfi, C., Srinivasan, S., Ertz, M., & Latrous, I. (2021). Web Scraping Techniques and Applications: A Literature Review. Scrs Conference Proceedings on Intelligent Systems, January, 381–394. https://doi.org/10.52458/978-93-91842-08-6-38

Lubis, A. R., Nasution, M. K. M., Sitompul, O. S., & Zamzami, E. M. (2022). The feature extraction for classifying words on social media with the Naïve Bayes algorithm. IAES International Journal of Artificial Intelligence, 11(3), 1041–1048. https://doi.org/10.11591/ijai.v11.i3.pp1041-1048

Mahmood, A. T., Kamaruddin, S. S., Naser, R. K., & Nadzir, M. M. (2020). A combination of lexicon and machine learning approaches for sentiment analysis on facebook. Journal of System and Management Sciences, 10(3), 140–150. https://doi.org/10.33168/JSMS.2020.0310

Malik, T., Hanif, N., Tahir, A., Abbas, S., Hanif, M. S., Tariq, F., Ansari, S., Abbasi, Q. H., & Imran, M. A. (2023). Crowd Control, Planning, and Prediction Using Sentiment Analysis: An Alert System for City Authorities. Applied Sciences (Switzerland), 13(3). https://doi.org/10.3390/app13031592

Mao, Y., Liu, Q., & Zhang, Y. (2024). 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

Massenon, R., Gambo, I., Ogundokun, R. O., Ogundepo, E. A., Srivastava, S., Agarwal, S., & Pak, W. (2024). Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools. PeerJ Computer Science, 10, 1–60. https://doi.org/10.7717/peerj-cs.2401

Miller, C., Portlock, T., Nyaga, D. M., & O’Sullivan, J. M. (2024). A review of model evaluation metrics for machine learning in genetics and genomics. Frontiers in Bioinformatics, 4(September), 1–13. https://doi.org/10.3389/fbinf.2024.1457619

Miller, G., & Spiegel, E. (2025). Guidelines for Research Data Integrity (GRDI). Scientific Data , 12(1), 1–8. https://doi.org/10.1038/s41597-024-04312-x

Mujahid, M., Kına, E. R. O. L., Rustam, F., Villar, M. G., Alvarado, E. S., De La Torre Diez, I., & Ashraf, I. (2024). Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering. Journal of Big Data, 11(1). https://doi.org/10.1186/s40537-024-00943-4

Nadira Alifia Ionendri, Feri Candra, & Afdi Rizal. (2025). News Classification using Natural Language Processing with TF-IDF and Multinomial Naïve Bayes. Journal of Applied Computer Science and Technology, 6(1), 37–45. https://doi.org/10.52158/jacost.v6i1.1099

Necula, S. C. (2023). Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior. Behavioral Sciences, 13(6). https://doi.org/10.3390/bs13060439

Nurhayati, N., Hartimar, L., Manza, Y., & ... (2025). Text Classification Using TF-IDF and Naïve Bayes: Case Study of MyXL App User Review Data. … of Technology and …, 2(2), 100–108. https://journal.technolabs.co.id/index.php/jotechcom/article/view/55%0Ahttps://journal.technolabs.co.id/index.php/jotechcom/article/download/55/55

Ogrizović, M., Drašković, D., & Bojić, D. (2024). Quality assurance strategies for machine learning applications in big data analytics: an overview. Journal of Big Data, 11(1). https://doi.org/10.1186/s40537-024-01028-y

Ou, G., He, Y., Fournier-Viger, P., & Huang, J. Z. (2022). A Novel Mixed-Attribute Fusion-Based Naive Bayesian Classifier. Applied Sciences (Switzerland), 12(20), 1–16. https://doi.org/10.3390/app122010443

Palomino, M. A., & Aider, F. (2022). Evaluating-the-Effectiveness-of-Text-PreProcessing-in-Sentiment-AnalysisApplied-Sciences-Switzerland.pdf. Mdpi, 12, 8765.

Phatcharathada, B., & Srisuradetchai, P. (2025). Randomized Feature and Bootstrapped Naive Bayes Classification. Applied System Innovation, 8(4). https://doi.org/10.3390/asi8040094

Priestley, M., O’Donnell, F., & Simperl, E. (2023). A Survey of Data Quality Requirements That Matter in ML Development Pipelines. Journal of Data and Information Quality, 15(2). https://doi.org/10.1145/3592616

Raharjana, I. K., Aprillya, V., Zaman, B., Justitia, A., & Fauzi, S. S. M. (2021). Enhancing software feature extraction results using sentiment analysis to aid requirements reuse. Computers, 10(3). https://doi.org/10.3390/computers10030036

Rahayu, S., Halawa, H. W., Abdillah, A. F., & Mujayanah, A. (2025). Pemanfaatan Big Data Analytics Untuk Analisis Pola Perilaku Konsumen E-Commerce Strategis. JUTECH : Journal Education and Technology, 6(1), 64–73. https://doi.org/10.31932/jutech.v6i1.4834

Rainio, O., Teuho, J., & Klén, R. (2024). Evaluation metrics and statistical tests for machine learning. Scientific Reports, 14(1), 1–14. https://doi.org/10.1038/s41598-024-56706-x

Rathi, R. N., & Mustafi, A. (2023). The importance of Term Weighting in semantic understanding of text: A review of techniques. Multimedia Tools and Applications, 82(7), 9761–9783. https://doi.org/10.1007/s11042-022-12538-3

ruparupa: Solusi Kebutuhan Rumah Terlengkap & Berkualitas. (n.d.). Retrieved November 16, 2025, from https://www.ruparupa.com/

Sang, V. M., Thanh, T. N. P., Gia, H. N., Nguyen Quoc, D., Long, K. Le, & Yen, V. P. T. (2024). Impact of user-generated content in digital platforms on purchase intention: the mediator role of user emotion in the electronic product industry. Cogent Business and Management, 11(1). https://doi.org/10.1080/23311975.2024.2414860

Senbel, S. (2021). Fast and Memory-Efficient TFIDF Calculation for Text Analysis of Large Datasets BT - Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices (H. Fujita, A. Selamat, J. C.-W. Lin, & M. Ali (Eds.); pp. 557–563). Springer International Publishing.

Sheridan, P., Ahmed, Z., & Farooque, A. A. (2025). A Fisher’s Exact Test Justification of the TF–IDF Term-Weighting Scheme. The American Statistician, 1–11. https://doi.org/10.1080/00031305.2025.2539241

Shu, X., & Ye, Y. (2023). Knowledge Discovery: Methods from data mining and machine learning. Social Science Research, 110(April 2022), 102817. https://doi.org/10.1016/j.ssresearch.2022.102817

Sianturi, F. B., Sari, B. N., & Jamaludin, A. (2022). Analisis Pembelian Produk Menggunakan Metode Algoritma Apriori Pada Toko Grosir Gigra. Journal Article // Systematics, 4(1), 362–371. https://journal.unsika.ac.id/index.php/systematics/article/download/6182/3194

Styawati, S., Isnain, A. R., Hendrastuty, N., & Andraini, L. (2021). Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis. Jurnal Informatika: Jurnal Pengembangan IT, 6(1), 56–60. https://doi.org/10.30591/jpit.v6i1.3245

Sulich, A., & Kozar, Ł. J. (2023). Wearables and Sustainable Development: Exploring Future Implications for the Green Jobs and Green Labor Market. Procedia Computer Science, 225, 279–288. https://doi.org/10.1016/j.procs.2023.10.012

Suryatini, N. W., Juniman, P. T., & Riesardhy, A. W. (2025). User-Generated Content : A Systematic Literature Review ( SLR ) Research. 14(1).

Tan, K. L., Lee, C. P., & Lim, K. M. (2023). A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research. Applied Sciences (Switzerland), 13(7). https://doi.org/10.3390/app13074550

Tchakounté, F., Pagor, A. E. Y., Kamgang, J. C., & Atemkeng, M. (2020). CIAA-RepDroid: A fine-grained and probabilistic reputation scheme for android apps based on sentiment analysis of reviews. Future Internet, 12(9). https://doi.org/10.3390/FI12090145

Tsiu, S. V., Ngobeni, M., Mathabela, L., & Thango, B. (2025). Applications and Competitive Advantages of Data Mining and Business Intelligence in SMEs Performance: A Systematic Review. Businesses, 5(2), 22. https://doi.org/10.3390/businesses5020022

Vanacore, A., Pellegrino, M. S., & Ciardiello, A. (2024). Fair evaluation of classifier predictive performance based on binary confusion matrix. Computational Statistics, 39(1), 363–383. https://doi.org/10.1007/s00180-022-01301-9

Wen, Z., Chen, Y., Liu, H., & Liang, Z. (2024). Text Mining Based Approach for Customer Sentiment and Product Competitiveness Using Composite Online Review Data. Journal of Theoretical and Applied Electronic Commerce Research , 19(3), 1776–1792. https://doi.org/10.3390/jtaer19030087

Wibowo, J. A., Mawardi, V. C., & Sutrisno, T. (2024). Visualisasi Word Cloud Hasil Analisis Sentimen Berbasis Fitur Layanan Aplikasi Gojek Dengan Support Vector Machine. Jurnal Serina Sains, Teknik Dan Kedokteran, 2(1), 61–70. https://doi.org/10.24912/jsstk.v2i1.32058

Yusuf, C., Spacy, M., & Wordcloud, D. A. N. (2024). Visualisasi Kata Kunci Pemberitaan.

Zeng, G. (2025). Invariance Properties and Evaluation Metrics Derived from the Confusion Matrix in Multiclass Classification. Mathematics, 13(16). https://doi.org/10.3390/math13162609

Zhang, L. (2025). Features extraction based on Naive Bayes algorithm and TF-IDF for news classification. Plos One, 20(7 July), 1–17. https://doi.org/10.1371/journal.pone.0327347

Zhecheva, D., & NENKOV, N. (2022). Business demands for processing unstructured textual data – text mining techniques for companies to implement. Access Journal - Access to Science, Business, Innovation in the Digital Economy, 3(2), 107–120. https://doi.org/10.46656/access.2022.3.2(2)

Zhou, J., Ye, Z., Zhang, S., Geng, Z., Han, N., & Yang, T. (2024). Investigating response behavior through TF-IDF and Word2vec text analysis: A case study of PISA 2012 problem-solving process data. Heliyon, 10(16), e35945. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e35945