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All Journal Sistemasi: Jurnal Sistem Informasi INTECOMS: Journal of Information Technology and Computer Science Jurnal Teknologi Sistem Informasi dan Aplikasi JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science
Anofrizen Anofrizen
Fakultas Sains dan Teknologi Universitas Islam Negeri (UIN) Sultan Syarif Kasim Riau
Computer Science & IT Education Electrical & Electronics Engineering

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Journal : Building of Informatics, Technology and Science

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Analisis Sentimen Masyarakat Menggunakan Algoritma Long Short Term Memory (LSTM) Pada Ulasan Aplikasi Halodoc Yulianti, Nelvi; Afdal, M; Jazman, Muhammad; Megawati, Megawati; Anofrizen, Anofrizen
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7243

Abstract

Halodoc is a digital healthcare platform that provides users with convenient access to medical services online. This study aims to analyze public sentiment toward the Halodoc application based on 1,416 user reviews collected during the period from July to September 2024. The reviews are categorized into three sentiment classes: positive, negative, and neutral, using the Long Short-Term Memory (LSTM) algorithm. Prior to classification, the Word2Vec technique is applied to transform the words in the reviews into numerical vector representations for processing by the model. The analysis revealed that a portion of the reviews expressed negative sentiments, mainly concerning delays in medication delivery and slow responses from customer service. Model performance evaluation shows that the implementation of the LSTM algorithm optimized with the Adam (Adaptive Moment Estimation) optimizer and a dropout rate of 0.2 achieved the highest accuracy of 89.40% and an F1-score of 88.63%. These results indicate that the model performs very well in classifying sentiments and can be used as a useful tool for understanding user satisfaction with the Halodoc application.
Co-Authors Adillah, Suci Nur Ahsyar, T. Khairil Ahsyar, Tengku Khairil Anmi, Fauzi Hidayatul Aprillia, Dhea Dwi Aprillia, Dhea Dwi Arif Marsal Asyraf, Fajri Muhammad Eki Saputra Hamzah, Muhammad Luthfi Harmaniola, Elsa Intan, Sri Jazman, Muhammad M Afdal Megawati - Mona Fronita, Mona Ningrum, Meriana Prihati Prananda, Yaldri Oktra Putra, Ahdika Sawiyya Raditya, Muhammad Zacky Raihan, Muhammad Ramadhan, Rifky Siregar, Syafril Syaifullah Syaifullah Yulianti, Nelvi Zarnelly Zarnelly