Published: 2025-04-01
Analisis Sentimen Proyek Strategis Nasional Food Estate Menggunakan Algoritma Naïve Bayes, Logistic Regression dan Support Vector Machine
DOI: 10.35870/jtik.v9i2.3312
Downloads
Abstract
The National Strategic Project Food Estate is an initiative by the Indonesian government aimed at enhancing food security through the development of large-scale agricultural areas. In the vice-presidential debate ahead of the 2024 election, Food Estate re-emerged as a hot topic, sparking controversy. Therefore, this study aims to analyze public perspectives on the National Strategic Project Food Estate by comparing the performance of machine learning algorithms, including Naïve Bayes, Logistic Regression and Support Vector Machine. This research also experiments with feature extraction techniques TF-IDF and Word2Vec. The results indicate that TF-IDF feature extraction performs better in capturing relevant features to enhance classification performance compared to the Word2Vec method. The best-performing algorithm is Logistic Regression + TF-IDF, achieving an accuracy of 74%, followed by SVM + TF-IDF and Naïve Bayes + TF-IDF with accuracies of 73% and 72%, respectively.
Keywords
Food Estate ; Logistic Regression ; Naïve Bayes ; Sentiment Analysis ; Support Vector Machine
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 9 No. 2 (2025)
-
Section: Computer & Communication Science
-
Published: %750 %e, %2025
-
License: CC BY 4.0
-
Copyright: © 2025 Authors
-
DOI: 10.35870/jtik.v9i2.3312
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Yuning Rum Zattayu Mustopo
Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Mercu Buana, Kota Jakarta Barat, Daerah Khusus Ibukota Jakarta, Indonesia.
Afiyati
Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Mercu Buana, Kota Jakarta Barat, Daerah Khusus Ibukota Jakarta, Indonesia.
-
Anwarudin, M. A. (2024). Analisis sentimen publik di media sosial Twitter terhadap program Food Estate menggunakan algoritma Support Vector Machine dan Naive Bayes Classifier (Doctoral dissertation, UIN Sunan Gunung Djati Bandung).
-
Dani, A. H., Puspaningrum, E. Y., & Mumpuni, R. (2024). Studi Performa TF-IDF dan Word2Vec Pada Analisis Sentimen Cyberbullying. Router: Jurnal Teknik Informatika dan Terapan, 2(2), 94-106. https://doi.org/10.62951/router.v2i2.76.
-
Gifari, O. I., Adha, M., Hendrawan, I. R., & Durrand, F. F. S. (2022). Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine. Journal of Information Technology, 2(1), 36-40. https://doi.org/10.46229/jifotech.v2i1.330.
-
Hasri, C. F., & Alita, D. (2022). Penerapan Metode Naïve Bayes Classifier Dan Support Vector Machine Pada Analisis Sentimen Terhadap Dampak Virus Corona Di Twitter. Jurnal informatika dan rekayasa perangkat lunak, 3(2), 145-160.
-
Lasminingrat, L., & Efriza, E. (2020). The development of national food estate: The Indonesian food crisis anticipation strategy. Jurnal Pertahanan dan Bela Negara, 10(3), 229-248.
-
Muzaki, A., & Witanti, A. (2021). Sentiment analysis of the community in the twitter to the 2020 election in pandemic covid-19 by method naive bayes classifier. Jurnal Teknik Informatika (Jutif), 2(2), 101-107.
-
Naufal, H. F., & Setiawan, E. B. (2021). Ekspansi Fitur Pada Analisis Sentimen Twitter Dengan Pendekatan Metode Word2Vec. eProceedings of Engineering, 8(5).
-
Nawangsari, R. P., Kusumaningrum, R., & Wibowo, A. (2019). Word2vec for Indonesian sentiment analysis towards hotel reviews: An evaluation study. Procedia Computer Science, 157, 360-366. https://doi.org/10.1016/j.procs.2019.08.178.
-
Purba, M., Ermatita, E., Abdiansah, A., Noprisson, H., Ayumi, V., Salamah, U., ... & Yadi, Y. (2022). Effect of Random Splitting and Cross Validation for Indonesian Opinion Mining using Machine Learning Approach. Int. J. Adv. Comput. Sci. Appl, 13(9).
-
Ramadhan, I. Y. (2023). Sekuritisasi Dalam Kebijakan Food Estate di Era Pemerintahan Joko Widodo. Aliansi: Jurnal Politik, Keamanan Dan Hubungan Internasional, 2(3), 153-163.
-
Safitri, T., Umaidah, Y., & Maulana, I. (2023). Analisis Sentimen Pengguna Twitter Terhadap Grup Musik BTS Menggunakan Algoritma Support Vector Machine. Journal of Applied Informatics and Computing, 7(1), 34-41.
-
Samantri, M. (2024). Perbandingan Algoritma Support Vector Machine dan Random Forest untuk Analisis Sentimen Terhadap Kebijakan Pemerintah Indonesia Terkait Kenaikan Harga BBM Tahun 2022. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 8(1), 1-9. https://doi.org/10.35870/jti.
-
Simamora, B., Lubis, K., & Arini, H. (2021). Analisis asumsi-asumsi pada program food estate di Papua. PERSPEKTIF, 10(2), 293-300. https://doi.org/10.31289/perspektif.v10i2.4267.
-
Suryati, E., Styawati, S., & Aldino, A. A. (2023). Analisis Sentimen Transportasi Online Menggunakan Ekstraksi Fitur Model Word2vec Text Embedding Dan Algoritma Support Vector Machine (SVM). Jurnal Teknologi dan Sistem Informasi, 4(1), 96-106. 4(1), 96–106. https://doi.org/10.33365/jtsi.v4i1.2445.
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.