Analisis Sentimen Ulasan Aplikasi Smart Campus Unisbank di Google Playstore Menggunakan Algoritma Naive Bayes
DOI:
https://doi.org/10.35870/jtik.v8i2.1882Keywords:
Naive Bayes, Python, Smartcampus, Preprocessing, TF-IDFAbstract
This study explores sentiment analysis on SmartCampus Unisbank application reviews on Google Play Store using the Naive Bayes classification method. Through Python programming language and web scraping techniques employing the google-play-scraper library, review data was automatically obtained and organized in CSV format. Text preprocessing techniques such as case folding, stopwords removal, tokenization, and stemming were applied to ensure accurate analysis. The data was divided into training and testing sets, and TF-IDF (Term Frequency-Inverse Document Frequency) was used for feature extraction. A Naive Bayes model was constructed and evaluated, achieving an accuracy of 84.6%. While the model demonstrated proficiency in identifying negative sentiments with 100% precision, it requires refinement for recognizing positive sentiments. These findings provide valuable insights for SmartCampus Unisbank developers to understand user perspectives and improve application quality
Downloads
References
Agustian, N., & Salsabila, U. H. (2021). Peran teknologi pendidikan dalam pembelajaran. Islamika, 3(1), 123-133. DOI: 10.36088/islamika.v3i1.1047.
Areni, I. S., Palantei, E., Suyuti, A., Baharuddin, M., Samman, F. A., & Umraeni, A. E. (2019). Pengembangan dan implementasi smart campus berbasis smart card di Institut Agama Islam Negeri Bone. JURNAL TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat, 2(1), 1-7. DOI: https://doi.org/10.25042/jurnal_tepat.v2i1.51.
Flores, V. A., Jasa, L., & Linawati, L. (2020). Analisis Sentimen untuk Mengetahui Kelemahan dan Kelebihan Pesaing Bisnis Rumah Makan Berdasarkan Komentar Positif dan Negatif di Instagram. Majalah Ilmiah Teknologi Elektro, 19(1), 49.
Wisnu, G. R. G., Muttaqi, A. R., Santoso, A. B., Putra, P. K., & Budi, I. (2020, October). Sentiment analysis and topic modelling of 2018 central java gubernatorial election using twitter data. In 2020 International Workshop on Big Data and Information Security (IWBIS) (pp. 35-40). IEEE. DOI: 10.1109/IWBIS50925.2020.9255583.
Fitriyyah, S. N. J., Safriadi, N., & Pratama, E. E. (2019). Analisis Sentimen Calon Presiden Indonesia 2019 dari Media Sosial Twitter Menggunakan Metode Naive Bayes. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 5(3), 279-285. DOI: http://dx.doi.org/10.26418/jp.v5i3.34368.
Wahyudi, R., & Kusumawardhana, G. (2021). Analisis Sentimen pada review Aplikasi Grab di Google Play Store Menggunakan Support Vector Machine. Jurnal Informatika, 8(2), 200-207.
Hendriyanto, M. D., Ridha, A. A., & Enri, U. (2022). Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine. INTECOMS: Journal of Information Technology and Computer Science, 5(1), 1-7. DOI: https://doi.org/10.31539/intecoms.v5i1.3708
Tanggraeni, A. I., & Sitokdana, M. N. (2022). Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 9(2), 785-795. DOI: https://doi.org/10.35957/jatisi.v9i2.1835.
Saini, S., Punhani, R., Bathla, R., & Shukla, V. K. (2019, April). Sentiment analysis on twitter data using R. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM) (pp. 68-72). IEEE.
Apriani, R., & Gustian, D. (2019). Analisis Sentimen dengan Naïve Bayes Terhadap Komentar Aplikasi Tokopedia. Jurnal Rekayasa Teknologi Nusa Putra, 6(1), 54-62. DOI: https://doi.org/10.52005/rekayasa.v6i1.86.
Pratama, M. O., Satyawan, W., Jannati, R., Pamungkas, B., Syahputra, M. E., & Neforawati, I. (2019, April). The sentiment analysis of Indonesia commuter line using machine learning based on twitter data. In Journal of Physics: Conference Series (Vol. 1193, No. 1, p. 012029). IOP Publishing.
Syafa'at, M. H., Setyaningsih, E. R., & Kristian, Y. (2021). Svm Untuk Sentiment Analysis Calon Kepala Daerah Berdasar Data Komentar Video Debat Pilkada Di Youtube. Antivirus: Jurnal Ilmiah Teknik Informatika, 15(2), 262-276.
Yusnitasari, T., Ikasari, D., Pratiwi, E. E. S., & Ramdani, N. S. (2017, November). Analisis Sentimen Terhadap Review Restoran Fish Streat pada Aplikasi Zomato Menggunakan Stemming Nazief Adriani dan Naive Bayes Classifier. In Prosiding Sentrinov (Seminar Nasional Terapan Riset Inovatif) (Vol. 3, No. 1, pp. EB163-174).
Kosasih, R., & Alberto, A. (2021). Analisis Sentimen Produk Permainan Menggunakan Metode TF-IDF Dan Algoritma K-Nearest Neighbor. InfoTekJar: Jurnal Nasional Informatika dan Teknologi Jaringan, 6(1), 134-139. DOI: https://doi.org/10.30743/infotekjar.v6i1.3893.
Muin, A. A. (2016). Metode Naive Bayes Untuk Prediksi Kelulusan (Studi Kasus: Data Mahasiswa Baru Perguruan Tinggi). Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar, 2(1), 22-26.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Dwi Rahma Firmansyah, Endang Lestariningsih
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
