Evaluation of Machine Learning Algorithms for Classifying User Perceptions of a Child Health Monitoring Application
DOI:
https://doi.org/10.31294/Keywords:
Machine learning, User perception, Child health monitoringAbstract
Supporting children’s early development requires consistent attention, ensuring their growth aligns with health standards. PrimaKu is one of the mobile applications developed by the Indonesian Pediatric Society. That application was created to assist parents in recording developmental milestones, monitoring immunization schedules, and accessing practical health information. This study investigates user perceptions of the application by analyzing publicly available reviews and ratings from the Google Play Store. Four supervised machine learning algorithms were applied to classify the sentiment expressed in the reviews: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. Among the models tested, SVM achieved the highest classification accuracy (81%), followed by Random Forest (77%), Decision Tree (74%), and Naive Bayes (73%). Precision, recall, and F1-score were also used to evaluate the performance of each model. The results highlight the relevance of machine learning in capturing and interpreting user sentiment toward digital health tools. Further exploration of deep learning architectures is encouraged to enhance classification accuracy and understanding of features.
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Abidin, Z., Junaidi, A., & Wamiliana. (2024). Text Stemming and Lemmatization of Regional Languages in Indonesia: A Systematic Literature Review. Journal of Information Systems Engineering and Business Intelligence, 10(2), 217–231. https://doi.org/10.20473/jisebi.10.2.217-231
Adegbehingbe, O. D., Akinsemoyin, Z., Okunade, J. O., Arbor, A., Thompson, K. E., Akoriola, T. T., Adebayo, B. R., Etim, M. R., & Chinonyerem, C. A. (2024). Flood Prognosis Using Aggregation Machine Learning. International Journal of African Innovation and Multidiciplinary, 6(2). https://doi.org/10.70382/mejaimr.v6i2.00
Anwar, A. S., Sayem, A., Shabbir, A., Taslima, N., Sikder, A. R., & Sidhu, G. S. (2024). Analyzing Enterprise Data Protection and Safety Risks in Cloud Computing Using Ensemble Learning International Journal on Recent and Innovation Trends in Computing and Communication Analyzing Enterprise Data Protection and Safety Risks in Cloud Computing Using Ensemble Learning. Article in International Journal on Recent and Innovation Trends in Computing and Communication. http://www.ijritcc.org
Arsad, I. K., Setyohadi, D. B., & Mudjihartono, P. (2021). E-commerce online review for detecting influencing factors users perception. Bulletin of Electrical Engineering and Informatics, 10(6), 3156–3166. https://doi.org/10.11591/eei.v10i6.3182
Carrillo, E., González, M., Parrilla, R., & Tarrega, A. (2023). Classification trees as machine learning tool to explore consumers’ purchasing decision pathway. A case-study on parent’s perception of baby food jars. Food Quality and Preference, 109. https://doi.org/10.1016/j.foodqual.2023.104916
Daraghmi, E. Y., Qadan, S., Daraghmi, Y. A., Yousuf, R., Cheikhrouhou, O., & Baz, M. (2024). From Text to Insight: An Integrated CNN-BiLSTM-GRU Model for Arabic Cyberbullying Detection. IEEE Access, 12, 103504–103519. https://doi.org/10.1109/ACCESS.2024.3431939
Heritage Samuel, A. (2024). A Comparative Evaluation of Machine Learning Algorithms for Predictive Modeling. https://www.researchgate.net/publication/385945679
Kaluarachchi, T., Reis, A., & Nanayakkara, S. (2021). A review of recent deep learning approaches in human-centered machine learning. In Sensors (Vol. 21, Issue 7). MDPI AG. https://doi.org/10.3390/s21072514
Lu, J., Xie, X., & Xiong, Y. (2024). Multi-view hypergraph regularized Lp norm least squares twin support vector machines for semi-supervised learning. Pattern Recognition, 156. https://doi.org/10.1016/j.patcog.2024.110753
Ong, A. K. S., Chuenyindee, T., Prasetyo, Y. T., Nadlifatin, R., Persada, S. F., Gumasing, M. J. J., German, J. D., Robas, K. P. E., Young, M. N., & Sittiwatethanasiri, T. (2022). Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana.” International Journal of Environmental Research and Public Health, 19(10). https://doi.org/10.3390/ijerph19106111
Peretz, O., Koren, M., & Koren, O. (2024). Naive Bayes classifier – An ensemble procedure for recall and precision enrichment. Engineering Applications of Artificial Intelligence, 136. https://doi.org/10.1016/j.engappai.2024.108972
Ren, G., Wang, Y., Ning, J., & Zhang, Z. (2020). Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 237. https://doi.org/10.1016/j.saa.2020.118407
Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random Forest Algorithm Overview. Babylonian Journal of Machine Learning, 2024, 69–79. https://doi.org/10.58496/bjml/2024/007
Schickedanz, A. (2020). Evolving Roles for Health Care in Supporting Healthy Child Development HHS Public Access. In Future Child (Vol. 30, Issue 2).
Siino, M., Tinnirello, I., & La Cascia, M. (2024). Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on Transformers and traditional classifiers. Information Systems, 121. https://doi.org/10.1016/j.is.2023.102342
Yousafzai, S. N., Shahbaz, H., Ali, A., Qamar, A., Nasir, I. M., Tehsin, S., & Damaševičius, R. (2024). X-News dataset for online news categorization. International Journal of Intelligent Computing and Cybernetics. https://doi.org/10.1108/IJICC-04-2024-0184
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Copyright (c) 2025 Eka Rahmawati, Adi Wibowo, Budi Warsito (Author)
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