Sentiment Analysis of Pinterest Application User Reviews Using ANN, CNN, and RNN Methods
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Published:
Sep 30, 2025
Keywords:
Sentiment, Pinterest, Google Playstore Reviews, Deep Learning Method, Recurrent Neural Network
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Abstract
The changes occurring in the pinterest application have sparked numerous opinions expressed on google playstore, both positive and negative. The purpose of this study is to analyze the sentiment of Indonesian public towards the Pinterest application through user reviews on the platform. The research method employed in this study is qualitative, utilizing data collection techniques through scraping user reviews and interviews. The research was conducted from October 2024 to January 2025. The data used consists of 2000 reviews collected in the years 2023 and 2024. This research uses 3 deep learning methods because they can understand large amounts of data. Others prefer machine learning as their research method because it is easier and less complicated. The RNN method is an effective method for performing sentiment analysis with large amounts of data. This is supported by research results indicating that the RNN (Recurrent Neural Networks) method achieved the highest accuracy in sentiment analysis, reaching 65.17%, followed by two other deep learning methods, namely CNN (Convolutional Neural Networks) and ANN (Artificial Neural Networks). The RNN method is effective because it is supported by high precision and recall values. The author suggests that future research should explore other methods and expand data from different platforms to gain a broader perspective.
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Ramadhan, A. P., & Yulhendri. (2025). Sentiment Analysis of Pinterest Application User Reviews Using ANN, CNN, and RNN Methods. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 6(3), 227 - 237. https://doi.org/10.62527/jitsi.6.3.460
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References
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[3] F. A. Larasati, D. E. Ratnawati, and B. T. Hanggara, “Analisis Sentimen Ulasan Aplikasi Dana dengan Metode Random Forest,” … Teknol. Inf. dan …, vol. 6, no. 9, pp. 4305–4313, 2022, [Online]. Available: http://j-ptiik.ub.ac.id
[4] R. A. Pratiwinindya, A. Cahyono, and ..., “Pinterest: Optimalisasi Literasi Visual dalam Pembelajaran Ilustrasi,” Pros. Semin. …, no. 1, pp. 898–905, 2022, [Online]. Available: https://proceeding.unnes.ac.id/index.php/snpasca/article/view/1584%0Ahttps://proceeding.unnes.ac.id/index.php/snpasca/article/download/1584/1079
[5] R. S. Ainul Wildan, R. Adam Rajagede, and R. Rahmadi, “Analisis Sentimen Politik Berdasarkan Big Data dari Media Sosial Youtube: Sebuah Tinjauan Literatur,” Pros. Autom., vol. 2, no. 1, 2021.
[6] T. I. Saputra and R. Arianty, “Implementasi Algoritma K-Means Clustering Pada Analisis Sentimen Keluhan Pengguna Indosat,” J. Ilm. Inform. Komput., vol. 24, no. 3, pp. 191–198, 2019, doi: 10.35760/ik.2019.v24i3.2361.
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[9] M. F. El Firdaus, N. Nurfaizah, and S. Sarmini, “Analisis Sentimen Tokopedia Pada Ulasan di Google Playstore Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 5, p. 1329, 2022, doi: 10.30865/jurikom.v9i5.4774.
[10] R. Puspita and A. Widodo, “Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS,” J. Inform. Univ. Pamulang, vol. 5, no. 4, p. 646, 2021, doi: 10.32493/informatika.v5i4.7622.
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[15] K. C. Astuti, A. Firmansyah, and A. Riyadi, “Implementasi Text Mining Untuk Analisis Sentimen Masyarakat Terhadap Ulasan Aplikasi Digital Korlantas Polri pada Google Play Store,” REMIK Ris. dan E-Jurnal Manaj. Inform. Komput., vol. 8, no. 1, pp. 383–394, 2024.
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[17] E. Y. Hidayat, R. W. Hardiansyah, and A. Affandy, “Analisis Sentimen Twitter untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network,” J. Nas. Teknol. dan Sist. Inf., vol. 7, no. 2, pp. 108–118, 2021, doi: 10.25077/teknosi.v7i2.2021.108-118.
[18] Runimeirati, Abdul Muis, and Figur Muhammad, “Pelatihan Text Mining Menggunakan Bahasa Pemrograman Python,” Abdimas Langkanae, vol. 3, no. 1, pp. 36–46, 2023, doi: 10.53769/abdimas.3.1.2023.83.
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[20] D. P. Santoso and W. Wibowo, “Analisis Sentimen Ulasan Aplikasi Buzzbreak Menggunakan Metode Naïve Bayes Classifier pada Situs Google Play Store,” J. Sains dan Seni ITS, vol. 11, no. 2, 2022, doi: 10.12962/j23373520.v11i2.72534.
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[23] N. Yusliani, A. Yuhafiz, M. D. Marieska, and A. S. Utami, “Analisis Sentimen di Twitter Menggunakan Algoritma Artificial Neural Network,” J. Jupiter, vol. 15, no. 1, pp. 725–731, 2023.
[24] G. A. Suwito, I. Cholissodin, and P. P. Adikara, “Analisis Sentimen Citayam Fashion Week pada Komentar YouTube dengan Metode Convolutional Neural Network,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 12, pp. 5948–5956, 2022, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/12049
[25] N. H. Danial and D. Setiawati, “Convolutional Neural Network (Cnn) Based on Artificial Intelligence in Periodontal Diseases Diagnosis,” Interdental J. Kedokt. Gigi, vol. 20, no. 1, pp. 139–148, 2024, doi: 10.46862/interdental.v20i1.8641.
[26] H. Utami, “Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan Metode Recurrent Neural Network,” Indones. J. Appl. Stat., vol. 5, no. 1, p. 31, 2022, doi: 10.13057/ijas.v5i1.56825.
[27] N. Lubis, M. Z. Siambaton, and R. Aulia, “Implementasi Algoritma Deep Learning pada Aplikasi Speech to Text Online dengan Metode Recurrent Neural Network (RNN),” sudo J. Tek. Inform., vol. 3, no. 3, pp. 113–126, 2024, doi: 10.56211/sudo.v3i3.583.
[28] I. T. Batam, I. T. Batam, A. Jl, G. Mada, K. V. City, and T. A. Sekupang, “Analisis Ramalan Cuaca di Sekupang , Kota Batam Menggunakan Algoritma Decision Tree dan Confusion Matrix Fitrianingsih ( KBBI ), keadaan , udara ( tentang suhu , cahaya matahari , kelembaban , kecepatan angin , dan,” no. 4, 2024.
[29] I. M. D. P. Asana and N. P. D. T. Yanti, “Sistem Klasifikasi Pengajuan Kredit Dengan Metode Support Vector Machine (SVM) I Made Dwi Putra Asana,” J. Sist. Cerdas, vol. 6, no. 2, pp. 123–133, 2023.
[30] A. C. Aulia, M. Fatekurohman, and I. M. Tirta, “Comparison of Online and Offline Learning During The COVID-19 Pandemic using Naïve Bayes Method and C4.5,” Berk. Sainstek, vol. 11, no. 3, p. 153, 2023, doi: 10.19184/bst.v11i3.31737.