Analisis Opini Terhadap Aplikasi Riliv di Twitter Menggunakan Algoritma Naïve Bayes dan Random Forest
DOI:
https://doi.org/10.33020/saintekom.v14i1.526Keywords:
AI Project Cycle, naïve bayes, random forest, riliv application, sentiment analysisAbstract
In the current era of technological advancement and the internet, people can easily access various information. This technological advancement brings innovation in the mental health field, such as services in the form of apps. This research conducts sentiment analysis using the Naïve Bayes and Random Forest algorithms. The study aims to analyze Twitter users’ opinions regarding the Riliv apps and compare the results of classification using Naïve Bayes and Random Forest. This research methodology uses the AI Project Cycle method. The data used is tweet data from Twitter with the keyword 'aplikasi riliv’. The dataset consisted of 1035 data, which was processed to produce 273 positive, 273 neutral, and 39 negative sentiments data. The Naïve Bayes and Random Forest algorithms were applied to compare the classification results of the two. The most optimal classification results are Naïve Bayes with SMOTE with the division of 90% training data and 10% testing data, which results in an accuracy value of 82.72%, a value of precision is 82.89% and a value of recall is 82.72%. Based on the results of the distribution of sentiment data, most users gave positive reviews and were knowledgeable about the Riliv application, while only a few were disappointed
Downloads
References
Aldean, M. Y., Paradise, P., & Setya Nugraha, N. A. (2022). Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 di Twitter Menggunakan Metode Random Forest Classifier (Studi Kasus: Vaksin Sinovac). Journal of Informatics, Information System, Software Engineering and Applications (INISTA), 4(2), 64–72. https://doi.org/10.20895/inista.v4i2.575
Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2021.107134
Fadhillah, R. P. (2022). Analisis Sentimen Terhadap Pemberitaan Varian Omicron di Indonesia pada Media Sosial Instagram Menggunakan Naïve Bayes. Universitas Singaperbangsa Karawang.
Giovani, A. P., Ardiansyah, A., Haryanti, T., Kurniawati, L., & Gata, W. (2020). Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi. Jurnal Teknoinfo. https://doi.org/10.33365/jti.v14i2.679
Halim, C., Purnomo, H. D., & Wahyono, T. (2022). Analisis Pengelompokan Wilayah Penyebaran Covid-19 Di Indonesia Dengan Metode Clustering Menggunakan Algoritma K-Means dan K-Medoids. Jurnal Inovtek Polbeng, 7(2), 359–372.
Hendra, A., & Fitriyani, F. (2021). Analisis Sentimen Review Halodoc Menggunakan Nai ?ve Bayes Classifier. JISKA (Jurnal Informatika Sunan Kalijaga). https://doi.org/10.14421/jiska.2021.6.2.78-89
Nanda, S., Mualfah, D., & Fitri, D. (2022). Analisis Sentimen Kepuasan Pengguna Terhadap Layanan Streaming Mola Menggunakan Algoritma Random Forest. Jurnal Aplikasi Teknologi Informasi Dan Manajemen (JATIM), 3(2 SE-Articles). https://doi.org/10.31102/jatim.v3i2.1592
Qadrini, L., Hikmah, H., & Megasari, M. (2022). Oversampling, Undersampling, Smote SVM dan Random Forest pada Klasifikasi Penerima Bidikmisi Sejawa Timur Tahun 2017. Journal of Computer System and Informatics (JoSYC), 3(4), 386–391. https://doi.org/10.47065/josyc.v3i4.2154
Saputra, S. A., Didi Rosiyadi, Windu Gata, & Syepry Maulana Husain. (2019). Sentiment Analysis Analysis of E-Wallet Sentiments on Google Play Using the Naive Bayes Algorithm Based on Particle Swarm Optimization. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(3), 377–382. https://doi.org/10.29207/resti.v3i3.1118
Tiara, O. R. (2021). Peduli Kesehatan Mental dengan Startup Asal Indonesia Ini. My Skill. https://myskill.id/blog/dunia-kerja/kesehatan-mental-startup-indonesia/
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review. https://doi.org/10.1007/s10462-022-10144-1
Wardani, N. R., & Erfina, A. (2021). Analisis Sentimen Masyarakat Terhadap Layanan Konsultasi Dokter Menggunakan Algoritma Naive Bayes. SISMATIK (Seminar Nasional Sistem Informasi Dan Manajemen Informatika), 11–18. https://sismatik.nusaputra.ac.id/
Wijoto, R. (2019). Aplikasi Karya Anak Surabaya Ini Kantongi Penghargaan dari Google. Beritajatim.Com. https://beritajatim.com/teknologi/aplikasi-karya-anak-surabaya-ini-kantongi-penghargaan-dari-google/
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Diana Nurfitriana, Taufik Ridwan, Apriade Voutama

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright :
By submitting manuscripts to Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen, the author agrees with this policy. No specific document approval is required.
- The copyright in each article belongs to the author.
- Authors retain all their rights to the published work, not limited to the rights set forth in this page.
- Authors acknowledge that Saintekom Journal: Science, Technology, Computers and Management as the first to publish under the Creative Commons Attribution 4.0 International license (CC BY-SA).
- The author may submit the paper separately, arrange for non-exclusive distribution of the manuscript that has been published in this journal into other versions (e.g. sent to the author's institutional respository, publication into a book, etc.), by acknowledging that the manuscript has been first published Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen;
- The author warrants that the article is original, written by the named author, has not been previously published, contains no unlawful statements, does not infringe the rights of others, is subject to copyright exclusively held by the author.
- If the article is jointly prepared by more than one author, each author submitting the manuscript warrants that he or she has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to inform co-authors of the terms of this policy. Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen will not be held liable for anything that may arise due to internal author disputes.
Lisensi :
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY-SA). This license permits anyone to:.
- Share - copy and redistribute this material in any form or format;
- Adaptation - modify, alter, and create derivatives of this material for any purpose.
- Attribution - you must give appropriate credit, include a link to the license, and state that changes have been made. You may do this in any appropriate manner, but it does not imply that the licensor endorses you or your use.
- Similar Sharing - If you modify, alter, or create a derivative of this material, you must distribute your contribution under the same license as the original material.