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All Journal International Journal of Electrical and Computer Engineering Faktor Exacta STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology International Journal Multidisciplinary Science
Achmad Benny Mutiara
Gunadarma University
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : Journal of Applied Data Sciences

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A Lexicon-Based Long Short-Term Memory (LSTM) Model for Sentiment Analysis to Classify Halodoc Application Reviews on Google Playstore Rina Refianti; Achmad Benny Mutiara; Ryan Arya Putra
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.160

Abstract

The development of information and communication technology is developing very quickly, has made many new breakthroughs. One of these technological advances is in the health sector, the creation of telemedicine applications. During the Covid-19 pandemic, it is difficult for people to get access to health. Therefore, telemedicine applications are needed. Halodoc is one of the telemedicine applications that has successfully become the top health application on the Google PlayStore. The application has been used by more than ten million users throughout Indonesia and received a rating of 4.6. To be able to see ratings and satisfaction from the public, user reviews are needed. The very large number of reviews often contain errors, making them difficult to decipher. Based on this, this research aims to create a web application, which can classify user reviews of the Halodoc application, using a proposed lexicon-based Long Short-Term Memory (LSTM) Model. Application is built using the Flask framework and the Python programming language. Models are created and trained using the TensorFlow library. The results of the model evaluation get an accuracy of 85.3% with an average precision value of 85.3%, a recall value of 85.6% and an f1-score of 85.3%. The proposed LSTM model can be used to classify Halodoc review sentiment classes.
Co-Authors Adam Huda Nugraha Adang Suhendra Asep Juarna Dewi Agushinta R. Hustinawaty Hustinawaty, Hustinawaty Millati Izzatillah Rifiana Arief Rina Refianti Rina Refianti Ryan Arya Putra Sayidati Karima Tubagus Maulana Kusuma Zekri Fitra Ramadhan