Analisis Sentimen Pengguna Aplikasi Gojek Menggunakan Metode Long Short Term – Memory (LSTM)
Article Sidebar
Abstract Views: 76
File Views: 52
Main Article Content
Abstract
The rapid development of digital service applications such as Gojek has generated a large number of user reviews on the Google Play Store containing opinions, criticisms, and user experiences. These reviews represent unstructured data that can be utilized to understand user perceptions through sentiment analysis. This study aims to analyze the sentiment of Gojek user reviews using the Long Short-Term Memory (LSTM) method. The research data were collected through web scraping from the Google Play Store, consisting of 1,000 Gojek user reviews. The data underwent several preprocessing stages, including case folding, cleaning, normalization, tokenization, stopword removal, and stemming. Subsequently, the data were transformed into numerical representations, divided into training and testing datasets, and processed using the Long Short-Term Memory (LSTM) method with the assistance of Google Colab. The results showed that, out of 1,000 analyzed reviews, 540 were classified as positive, 270 as negative, and 190 as neutral sentiments. Based on the confusion matrix evaluation, the model achieved an accuracy of 63.00%, a precision of 63.00% for the negative class and 0.00% for the positive class, a recall of 100.00% for the negative class and 0.00% for the positive class, and an F1-score of 77.00% for the negative class and 0.00% for the positive class. These findings indicate that the LSTM method can be applied to classify sentiment in Indonesian-language text. However, the model's performance is still affected by class imbalance, suggesting that further optimization is required to improve classification performance.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Adelia, S., Kurniawan, F., Milanda, E., Santari, J., Kesuma, D. T., & Silvia, E. (2023). Analisis Sentimen Belajar Programming Pada Media Sosial Youtube Menggunakan Algoritma Klasifikasi Naive Bayes. Journal of Information Technology Ampera, 4(3), 254–264. https://doi.org/10.51519/journalita.v4i3.430
Arisandi, W., & Anggai, S. (2025). Analisis Sentimen Ulasan Pengguna Aplikasi Media Sosial X Di Play Store Menggunakan Algoritma Long Short- Term Memory ( LSTM ) Dan Gated Recurrent Unit ( GRU ). IX(September), 63–72.
Hendrawan, A., & Sela, E. I. (2024). Jurnal Indonesia : Manajemen Informatika dan Komunikasi Analisis Sentimen Komentar Youtube Tentang Resesi Global 2023 Menggunakan LSTM Abstrak Jurnal Indonesia : Manajemen Informatika dan Komunikasi. 5(1), 587–593.
Long, U., Term, S., & Lstm, M. (2025). SENTIMENT ANALYSIS OF MYPERTAMINA APPLICATION USER REVIEWS. 8, 1254–1262.
Saputra, A., Cipta, R., Hariyono, S., & Saraswati, N. M. (2023). Analisis Sentimen Pengguna Aplikasi MyPertamina Menggunakan Algoritma Bidirectional Long Short Term Memory. 156–163. https://doi.org/10.30864/eksplora.v13i2.973
Satyo, A., & Karno, B. (2020). Analisis Data Time Series Menggunakan LSTM ( Long Short Term Memory ) dan ARIMA ( Autocorrelation Integrated Moving Average ) dalam Bahasa Python. XI(1), 1–7.
Soesana, A., Subakti, H., Karwanto, Fitri, anisa, Kuswandi, S., Sastri, L., Ilham, F., aswan, N., Hasibuan, F. artauli, & Lestari, H. (2023). Metodologi Penelitian Kuantitatif.
Studi, P., Informatika, T., & Raya, U. P. (2024). Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi Gojek. 4(1), 70–82.
Technology, I. (2025). Analisis Sentimen Media Sosial Menggunakan Algoritma BERT dan LSTM. 1(3), 85–92.
Thomas, S., Yuliana, & Noviyanti. P. (2021). Study Analisis Metode Analisis Sentimen pada YouTube. Journal of Information Technology, 1(1), 1–7. https://doi.org/10.46229/jifotech.v1i1.201
Agustina, V., & Herliana, A. (2025). Analisis Sentimen Publik atas Kebijakan Efisiensi Anggaran 2025 dengan Text Mining dan Natural Language Processing Jurnal Media Informatika [ JUMIN ]. 6(3), 2182–2194.
Ma, H., Wibawa, A. P., & Akbar, M. I. (2020). Klasifikasi artikel ilmiah dengan berbagai skenario preprocessing. 2(2), 70–78.
Tamlica, M. A., Irawan, B (2026)., Setiabudi, U. M., Diponegoro, J. P., & Km, N. (n.d.). Google Play Store Menggunakan Algoritma Long Short-Term Memory ( LSTM ). 14(1).
Yudistira, F., & Isnain, A. R. (2025). Analisis Sentimen Terhadap Seleksi CPNS Tahun 2024 Berbasis Media Sosial X Menggunakan Algoritma Naïve Bayes Program Studi Informatika , Fakultas Teknik dan Ilmu Komputer , Universitas Teknokrat Indonesia , Sentiment Analysis Of The 2024 Cpns Selection Based On Social Media X Using The Naïve Bayes Algorithm. 5(3), 887–897.
Ramadhan, N. R., & Hendrastuty, N. (2024). Perbandingan Algoritma Naïve Bayes dan LSTM untuk Analisis Sentimen Terhadap Opini Masyarakat Tentang Sandwich Generation. 6(3), 1677–1687. https://doi.org/10.47065/bits.v6i3.6385
Metode, M., & Mining, T. (2025). El-Mujtama : Jurnal Pengabdian Masyarakat El-Mujtama : Jurnal Pengabdian Masyarakat. 5(4), 706–713. https://doi.org/10.47467/elmujtama.v5i4.8615
Suriah, G. K., Hadiansah, R., Murfi, H., Ardaneswari, G., Cahya, L. D., & Nurhaniifah, F. (2022). Analysis of Performance Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) for Lifelong Learning on Indonesian Sentiment Analysis. In Advanced Intelligent Systems for Sustainable Development (AI2SD'2020) (pp. 990–1003). Springer.
Indarwati, K. D., & Februariyanti, H. (2023). Analisis sentimen terhadap kualitas pelayanan aplikasi Gojek menggunakan metode Naive Bayes Classifier. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 10(1)