Kurniasari, Dian and Su'admaji, Arif and Lumbanraja, Favorisen R and Warsono, Warsono (2025) Evaluating User Satisfaction in the Halodoc Application Using a Hybrid CNN-BiLTSM Model for Sentiment Analysis. Jurnal Teknik Informatika, 18 (2). pp. 209-225. ISSN 2549-7901

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Abstract

The growing demand for digital healthcare services in Indonesia has driven the adoption of Online Healthcare Applications (OHApps) such as Halodoc. Despite over 65 million users, maintaining user satisfaction remains a challenge. This study employs sentiment analysis using a hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) model to classify user review ratings. A dataset of 10,000 Google Play Store reviews was divided into COVID-19 and post-pandemic segments. The methodology includes data collection, pre processing, and dataset segmentation for training, validation, and testing. Results indicate that the CNN-BiLSTM model surpasses traditional machine learning by combining CNN’s feature extraction with BiLSTM’s The abstract is a synopsis of the work containing the problems studied, research purpose, information, and methods used to solve problems and conclusions. Articles must be submitted in print ready format and are limited to a minimum of ten (10) pages and a maximum of twelve (12) pages. Abstract is a synopsis of the work that contains the issues studied, the research purpose, the information and methods used to solve the problem, and the research conclusion. Abstracts are limited to 200 words and should not contain references, mathematical equations, figures, and tables. The font size for abstracts, keywords, and an article body is 11pt. Keywords are no more than six (6) words, but the minimum is three (3) words. Keywords: Web, Asset Management, CodeIgniter, Bootstrap long-term dependency capture, achieving 98.71% accuracy on COVID19 data and 98.16% post-pandemic. Additionally, the model demonstrates strong performance across other key evaluation metrics, with precision, recall, and F1-score. Misclassification analysis highlights minor errors, particularly in ratings 4 and 5. These findings help healthcare providers enhance digital services by identifying user concerns, improving platform features, and optimizing customer engagement. Beyond healthcare, this approach has real-world applications in e-commerce and financial services, where sentiment analysis informs user experience improvements.

Item Type:	Article
Subjects:	Q Science > QA Mathematics
Divisions:	Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Matematika
Depositing User:	DIAN KURNIASARI
Date Deposited:	17 Apr 2026 02:42
Last Modified:	17 Apr 2026 02:42
URI:	http://repository.lppm.unila.ac.id/id/eprint/54828

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