Sentiment Analysis on Google Play Store Reviews to Measure User Perception of the Gojek Application Using CNN
DOI:
https://doi.org/10.30871/jaic.v9i6.11084Keywords:
CNN, Gojek , Random Sampling, Sentiment AnalysisAbstract
This study was conducted to analyze sentiment towards user reviews from the Google Play Store regarding the Gojek application. The analysis aims to measure user perceptions using a Convolutional Neural Network (CNN). This study aims to understand user views on the Gojek application. By understanding user perceptions, the information obtained can be utilized by the company's service team to improve the quality of the application for users. User perceptions are grouped into three labels: positive, neutral, and negative. To produce an effective model, this study uses three data sharing ratios simultaneously with the same parameters: 90:10, 80:20, and 70:30. Due to the large amount of data, random sampling is needed to balance the data and thus increase accuracy in the data processing process. Model evaluation was carried out using a confusion matrix, precision, recall, and F1-Score. The results obtained with the highest accuracy of 84.29%. This study successfully demonstrates that CNN is able to process user review data well.
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[1] D. R. Alghifari, M. Edi, and L. Firmansyah, “Implementasi Bidirectional LSTM untuk Analisis Sentimen Terhadap Layanan Grab Indonesia,” Jurnal Manajemen Informatika (JAMIKA), vol. 12, pp. 89–99, 2022.
[2] S. A. Pohan, Samsudin, and F. H. Sibarani, “Analisis Sentimen Terhadap Aplikasi Maxim Menggunakan Algoritma Random Forest,” Jurnal of Science and Social Research, 2024.
[3] M. Iqrom, M.Afdal, R. Novita, M. Rahmawati, and T. K. Ahsyar, “Analisis Sentimen Aplikasi Gojek, Grab, dan Maxim Menggunakan Algoritma Support Vector Machine,” Jurnal Inovtek Polbeng-Seri Informatika, vol. 10, pp. 237–248, 2025.
[4] S. Heristian, M. Napiah, and W. Erawati, “Analisis Sentimen Ulasan Pelanggan Menggunakan Algoritma Naive Bayes pada Aplikasi Gojek,” Computer Science (CO-SCIENCE), vol. 5, pp. 35–41, 2025.
[5] R. A. Rahman, V. H. Pranatawijaya, and N. N. K. Sari, “Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi Gojek,” KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, vol. 4, pp. 70–82, 2024.
[6] P. Hadrian, F. Milichovsky, and P. Mracek, “The Concept of Strategic Control in Marketing Management in Connection to Measuring Marketing Performance,” Sustainability, vol. 13, no. 7, pp. 1–21, 2021.
[7] W. P. Dharmawan and F. Oktafani, “Analisis Faktor-Faktor yang Mempengaruhi Perubahan Perilaku Konsumen dalam Keputusan Pembelian Makanan pada Aplikasi Gojek,” SEIKO: Journal od Management & Business, vol. 5, pp. 130–140, 2022.
[8] S. Maesaroh, R. R. Lubis, L. N. Husna, R. Widyaningsih, R. Susilawati, and P. M. Yasmin, “Efektivitas Implementasi Manajemen Business Intelligence pada Industri 4.0,” ABDI JURNAL: ADI Bisnis Digital Interdisiplin Jurnal, vol. 3, 2022.
[9] V. K. Subroto and E. Endaryati, “Business Intelligence dan Kesuksesan Bisnis di Era Digital,” JURNAL MANAJEMEN SOSIAL EKONOMI (DINAMIKA), vol. 1, pp. 41–47, 2021.
[10] M. P. R. Putra and K. R. N. Wardani, “Penerapan Text Mining Dalam Menganalisis Kepribadian Pengguna Media Sosial,” JUTIM (Jurnal Teknik Informatika Musirawas), vol. 5, no. 1, pp. 63–71, 2020.
[11] A. Raup, W. Ridwan, Y. Khoeriyah, Supiana, and Q. Y. Zaqiah, “Deep Learning dan Penerapannya dalam Pembelajaran,” JIPP (Jurnal Ilmiah Ilmu Pendidikan), vol. 5, no. 9, pp. 3258–3267, 2022.
[12] F. P. Rachman and H. Santoso, “Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Language Processing,” Jurnal Teknologi dan Manajemen Informatika, vol. 7, no. 2, pp. 103–112, 2021.
[13] U. S. Ramadhani and N. L. Marpaung, “Klasifikasi Jamur Berdasarkan Genus dengan Menggunakan Metode CNN,” Jurnal Informatika: Jurnal Pengembangan IT (JPIT), vol. 8, no. 2, pp. 169–173, 2023.
[14] H. Utami, “Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan Metode Recurrent Neural Network,” IJAS: Indonesian Journal of Applied Statistics, vol. 5, no. 1, pp. 31–38, 2022.
[15] A. D. A. Putra and S. Juanita, “Analisis Sentimen Pada Ulasan Pengguna Aplikasi Bibit dan Bareksa Dengan Algoritma KNN,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 8, no. 2, pp. 636–646, 2021.
[16] O. I. Gifari, Muh. Adha, I. R. Hendrawan, and F. F. S. Durrand, “Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine,” JIFOTECH (Journal Of Information Technology), vol. 2, no. 1, pp. 36–40, 2022.
[17] A. Muhammadin and I. A. Sobari, “Analisis Sentimen Pada Ulasan Aplikasi Kredivo Dengan Algoritma SVM dan NBC,” Reputasi: Jurnal Rekayasa Perangkat Lunak, vol. 2, no. 2, pp. 85–91, 2021.
[18] K. L. Tan, P. C. Lee, and K. M. Lim, “A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research,” Applied Science, vol. 13, no. 7, pp. 1–21, 2023.
[19] F. M. Fajar and D. Maulina, “Analisis Sentimen Kurikulum Merdeka Dengan Penerapan Convolutional Neural Network,” JACIS: Journal Automation Computer Information System , vol. 4, pp. 1–11, 2024.
[20] R. Z. N. Ahmad, N. S. Harahap, S. Agustian, I. Iskandar, and S. Sanjaya, “Perbandingan Performa Random Forest dan Long Shot-Term Memory dalam Klasfikasi Teks Multilabel Terjemahan Hadis Bukhari,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 3, pp. 862–874, 2025.
[21] M. Carvalho, A. J. Pinho, and S. Bras, “Resampling Approaches to Handle Class Imbalance: A Review from A Data Perspective,” Applied Intelligence, vol. 14, no. 1, pp. 1–8, 2025.
[22] S. Diantika, “Penerapan Teknik Random Oversampling untuk Mengatasi Imbalance Class Dalam Klasifikasi Website Phising Menggunakan Algoritma Lightgbm,” JATI (Jurnal Mahasiswa Teknik Infromatika), vol. 7, pp. 19–25, 2023.
[23] R. L. Atimi and E. E. Pratama, “Implementasi Model Klasifikasi Sentimen Pada Review Produk Lazada Indonesia,” Jurnal Sains dan Informatika, vol. 8, pp. 88–96, 2022.
[24] S. Samidin and F. Akhmad, “Klasifikasi Gambar Batu-Kertas-Gunting Menggunakan Convolutional Neural Network dengan Fungsi Callback untuk Mencegah Overfitting,” Jurnal Penelitian Inovatif (JUPIN), vol. 4, no. 2, pp. 785–794, 2024.
[25] Y. Yan, “ERNIE-TextCNN: Research on Classification Methods of Chinese News Headlines in Different Situation,” Sci Rep, vol. 15, no. 1, 2025.
[26] E. Subowo, “Implementasi Pembelajaran Mendalam dalam Klasifikasi Sentimen Ulasan Aplikasi: Evaluasi Model BERT, LSTM, dan CNN,” Surya Informatika, vol. 14, no. 2, pp. 66–70, 2024.
[27] C. Miller, T. Portlock, D. M. Nyaga, and J. M. O’Sullivan, “A review of Model Evaluation Metrics for Machine Learning in Genetics and Genomics,” Front Big Data, vol. 4, pp. 1–13, 2022.
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