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All Journal Jurnal Insan Pendidikan dan Sosial Humaniora Atmosfer: Jurnal Pendidikan, Bahasa, Sastra, Seni, Budaya, Dan Sosial Humaniora Fonologi: Jurnal Ilmuan Bahasa dan Sastra Inggris Sintaksis: Publikasi Para Ahli Bahasa dan Sastra Inggris Semantik : Jurnal Riset Ilmu Pendidikan, Bahasa dan Budaya Pragmatik : Jurnal Rumpun Ilmu Bahasa dan Pendidikan Misterius: Publikasi Ilmu Seni dan Desain Komunikasi Visual Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Jurnal Pendidikan dan Sosial Humaniora
Humannisa Rubina Lestari
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Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media

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Analisis Sentimen Ulasan Aplikasi HeyJapan di Google Play Store Menggunakan Algoritma NLP Jasmine Aulia Mumtaz; Kinaya Khairunnisa Komariansyah; Wildan Holik; Muhammad Galuh Gumelar; Reza Pratama; Humannisa Rubina Lestari
Pragmatik : Jurnal Rumpun Ilmu Bahasa dan Pendidikan  Vol. 3 No. 3 (2025): Juli: Pragmatik : Jurnal Rumpun Ilmu Bahasa dan Pendidikan
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/pragmatik.v3i3.1801

Abstract

Digital learning applications like HeyJapan are increasingly popular. User reviews on platforms such as Google Play Store contain valuable information on user perceptions and experiences. To process this information systematically, this study employs a Natural Language Processing (NLP) approach to analyze sentiment toward the HeyJapan application. Data was collected using web scraping techniques with Python and the google play scraper library, resulting in 1,000 latest user reviews. The analysis included data collection, preprocessing, sentiment labeling using TextBlob, visualization, modeling with Logistic Regression, and evaluation. After preprocessing, 923 valid reviews were classified into three sentiment categories based on polarity which are positive, neutral, and negative. Results showed 71.4% of reviews positive, 26.1% neutral, and 2.5% negative. Visualizations in pie charts and word clouds provided an overview of user perceptions. Modeling with TF-IDF and Logistic Regression achieved 88% accuracy with the highest f1-score in the positive sentiment category. Evaluation indicates the model is fairly reliable in classifying sentiments, especially for positive and neutral categories, though negative sentiment classification needs improvement. This study shows the NLP approach can evaluate user perceptions of educational applications based on reviews and serve as a basis for improving foreign language learning app quality.
Co-Authors Abiyyu Umar Thoyyib Adli Farizi Akhdan Ravi Andaman Apik Banyubasa Bennefit Christy Saragih Cahya Rudansah Cindy Valencia Cynthia Octavania Putri Salma Dimas Akbar Tama Fadhlan Zaki Darmansyah Fatih Kawakib Kartono Ferrol Azki Mashudi Fikri Saputra Gandi Abetnego Simangunsong Ghaeril Juniawan Parel Hakim Glorian Hilarius Kiantin Beda Hafidz Faqih Dinillah Hassan Narallah Matauq Icha Maulidya Indra Maki Wiguna Iqbal Fadhila Rachman Iqna Raidan Abdurrahman Jasmine Aulia Mumtaz Jonathan Cristiano Rabika Kayla Attya Ramadhani Kharisma Nur Devyanti Kinaya Khairunnisa Komariansyah Mochammad Alwan Al Ataya Muhammad Alif Vidi Muhammad Anggi Lubis Muhammad Galuh Gumelar Muhammad Ilham Nurfajri Muhammad Rafi Rusafni Muhammad Yordi Septian Nafis Faturrahman Nanda Wijaya Putra Nathanael Jonathan Feryanto Nurbadillah Nurbadillah Pandu Wicaksono R Bramaditya Ario Wirawisesa Raihan Yuanda Rangga Wasita Ningrat Reza Pratama Salman Al-Farisi Saniyyah Wafa Nurjihan Shinta Arafah Hidayanti Syah Bintang Syelly Nawilana Imroatus Sholikha Syifa Nursaadah Tejo Mulyono Wildan Holik