Sentiment Analysis of MobileJKN Application Reviews Using Neural Network Algorithm

Muhammad Daffa Ayyasy; Rudi Kurniawan; Yudhistira Arie Wijaya

doi:10.59934/jaiea.v4i3.999

Authors

Muhammad Daffa Ayyasy STMIK IKMI Cirebon
Rudi Kurniawan STMIK IKMI Cirebon
Yudhistira Arie Wijaya STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i3.999

Keywords:

Sentiment Review, Neural Network, MobileJKN, Sampling Technique, Sentiment Classification

Abstract

The advancement of information technology has encouraged the use of user data to improve digital services, particularly in health-related applications such as MobileJKN, developed by BPJS Kesehatan Indonesia. This research conducts sentiment analysis on user reviews of MobileJKN from the Google Play Store, aiming to identify key areas for improvement based on user perceptions. A Deep Learning approach is utilized, with Neural Networks as the primary model and Altair AI Studio as the main data processing tool. Following the Knowledge Discovery in Databases (KDD) methodology, the study involves various preprocessing stages including case folding, tokenization, filtering, stopword removal, and stemming, using the Kamus Besar Bahasa Indonesia (KBBI) to standardize local language terms. After preprocessing, clustering and classification are performed to extract sentiment patterns. The most frequently mentioned keywords “register,” “app,” “number,” “sign in,” and “verify” highlight common user concerns. The sentiment classification model achieved a 100% accuracy rate, with the Shuffled Sampling technique and a 90:10 training-testing ratio yielding optimal results. These findings demonstrate the effectiveness of Neural Networks in analyzing sentiment within health applications, providing valuable insights for developers seeking to enhance MobileJKN’s performance and user satisfaction. The study also offers a practical reference for future sentiment analysis research in the Indonesian digital health context.

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References

Acito, F. (2023). Neural Networks BT - Predictive Analytics with KNIME: Analytics for Citizen Data Scientists (F. Acito (ed.); pp. 229–254). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-45630-5_11

Airikkala, E., Laaksonen, M., Halkoaho, A., & Kaunonen, M. (2023). Perception of inherited risk in type 2 diabetes: a systematic review. Frontiers in Public Health, 11(December). https://doi.org/10.3389/fpubh.2023.1293874

Al-Deen, H. S. S., Zeng, Z., Al-Sabri, R., & Hekmat, A. (2021). An improved model for analyzing textual sentiment based on a deep neural network using multi-head attention mechanism. Applied System Innovation, 4(4). https://doi.org/10.3390/asi4040085

Antons, D., Grünwald, E., Cichy, P., & Salge, T. O. (2020). The application of text mining methods in innovation research: current state, evolution patterns, and development priorities. R and D Management, 50(3), 329–351. https://doi.org/10.1111/radm.12408

Aritonang, P. A., Johan, M. E., & Prasetiawan, I. (2022). Aspect-Based Sentiment Analysis on Application Review using CNN (Case Study : Peduli Lindungi Application). Ultima Infosys : Jurnal Ilmu Sistem Informasi, 13(1), 54–61.

Astuti, W., Kurniawan, R., & Wijaya, A. Y. (2024). Analisis Sentimen Ulasan Instagram Di Google Play Store Menggunakan Algoritma Naïve Bayes. Jurnal Informatika Dan Rekayasa Perangkat Lunak, 7(6), 3314–3319. https://doi.org/10.36040/jati.v7i6.8178

Aufa, R. N., Prasetiyowati, S. S., & Sibaroni, Y. (2023). The Effect of Feature Weighting on Sentiment Analysis TikTok Application Using The RNN Classification. Building of Informatics, Technology and Science (BITS), 5(1), 345–353. https://doi.org/10.47065/bits.v5i1.3597

Chahid, I., Elmiad, A. K., & Badaoui, M. (2023). Data Preprocessing For Machine Learning Applications in Healthcare: A Review. 2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA), 1–6. https://doi.org/10.1109/SITA60746.2023.10373591

Chakraborty, S., Islam, S. K. H., & Samanta, D. (2022). Introduction to Data Mining and Knowledge Discovery BT - Data Classification and Incremental Clustering in Data Mining and Machine Learning (S. Chakraborty, S. H. Islam, & D. Samanta (eds.); pp. 1–22). Springer International Publishing. https://doi.org/10.1007/978-3-030-93088-2_1

Hadwan, M., Al-Sarem, M., Saeed, F., & Al-Hagery, M. A. (2022). An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique. Applied Sciences (Switzerland), 12(11). https://doi.org/10.3390/app12115547

Haron, N. H. bin. (2022). Stratified sampling using cluster analysis. AIP Conference Proceedings, 2472(1), 50012. https://doi.org/10.1063/5.0092740

Hasanah, K. (2024). Comparison of Sentiment Analysis Model for Shopee Comments on Google Play Store. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 13(1), 21–30. https://doi.org/10.32736/sisfokom.v13i1.1916

Heisler, G. V., & Assunção, J. V. C. (2024). A Aplicação do Processo de KDD aos Dados da COVID-19: Um Estudo de Caso no Rio Grande do Sul, Brasil. 91–100. https://doi.org/10.5753/erbd.2024.238871

Irwandi, Santoso, S., Sakroni, Lukitasari, M., & Hasan, R. (2022). School-community Collaboration in Inquiry-based Learning to Strengthen Religious Character and Improve Learning Outcome of Students. International Journal of Instruction, 15(3), 913–930. https://doi.org/10.29333/iji.2022.15349a

Ito, T., Tsubouchi, K., Sakaji, H., Yamashita, T., & Izumi, K. (2020). Word-level contextual sentiment analysis with interpretability. AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 4231–4238. https://doi.org/10.1609/aaai.v34i04.5845

Jamil, R., Ashraf, I., Rustam, F., Saad, E., Mehmood, A., & Choi, G. S. (2021). Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model. PeerJ Computer Science, 7, 1–24. https://doi.org/10.7717/peerj-cs.645

Joseph, V. R. (2022). Optimal ratio for data splitting. Statistical Analysis and Data Mining, 15(4), 531–538. https://doi.org/10.1002/sam.11583

Krosuri, L. R., & Aravapalli, R. S. (2023). Novel heuristic-based hybrid ResNeXt with recurrent neural network to handle multi class classification of sentiment analysis. Machine Learning: Science and Technology, 4(1). https://doi.org/10.1088/2632-2153/acc0d5

Priyadarshini, I., & Cotton, C. (2021). A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis. Journal of Supercomputing, 77(12), 13911–13932. https://doi.org/10.1007/s11227-021-03838-w

Putra, R. A., Novita, R., Ahsyar, T. K., & Zarnelly. (2024). Implementation of Classification Algorithm for Sentiment Analysis: Measuring App User Satisfaction. Teknika, 13(2), 204–212. https://doi.org/10.34148/teknika.v13i2.827

Qureshi, A. A., Ahmad, M., Ullah, S., Yasir, M. N., Rustam, F., & Ashraf, I. (2023). Performance evaluation of machine learning models on large dataset of android applications reviews. Multimedia Tools and Applications, 82(24), 37197–37219. https://doi.org/10.1007/s11042-023-14713-6

Wahyudi, D., & Sibaroni, Y. (2022). Deep Learning for Multi-Aspect Sentiment Analysis of TikTok App using the RNN-LSTM Method. Building of Informatics, Technology and Science (BITS), 4(1), 169–177. https://doi.org/10.47065/bits.v4i1.1665