ISSN: 2226-6348
Open access
The purpose of this study is to analysis public sentiment towards the free school meal program organized by the Indonesian Government using data from social media. This program aims to improve students' nutritional intake and help ease the burden on low-income families. However, this policy has drawn mixed reactions on social media, both in the form of support and criticism. Data was collected from various social media platforms and processed through data cleaning and modelling stages using machine learning algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes. The results of the analysis show that the SVM and Naive Bayes methods have a higher level of accuracy in classifying positive and negative sentiments than KNN. Overall, the majority of public sentiment towards this program is positive, reflecting support for the policy that is considered beneficial for students in need. However, there are also a number of negative sentiments that highlight issues of distribution and quality of service. These findings can provide insights for the government to evaluate and improve the effectiveness of the program, as well as identify areas that need improvement.
Adak, A., Pradhan, B., & Shukla, N. (2022). Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review. Foods, 11(10). https://doi.org/10.3390/foods11101500
Ainin, S., Feizollah, A., Anuar, N. B., & Abdullah, N. A. (2020). Sentiment analyses of multilingual tweets on halal tourism. Tourism Management Perspectives, 34. https://doi.org/10.1016/j.tmp.2020.100658
Ajhari, A. A. (2023). The Comparison of Sentiment Analysis of Moon Knight Movie Reviews between Multinomial Naive Bayes and Support Vector Machine. Applied Information System and Management (AISM), 6(1), 13–20. https://doi.org/10.15408/aism.v6i1.26045
Awazon. (2022). Apa yang dimaksud dengan Analisis Sentimen? Https://Aws.Amazon.Com/Id/What-Is/Sentiment-Analysis.
Hudaefi, F. A., Caraka, R. E., & Wahid, H. (2022). Zakat administration in times of COVID-19 pandemic in Indonesia: a knowledge discovery via text mining. International Journal of Islamic and Middle Eastern Finance and Management, 15(2), 271–286. https://doi.org/10.1108/IMEFM-05-2020-0250
Klimczuk, A. (2021). Introductory Chapter: Demographic Analysis. In Demographic Analysis - Selected Concepts, Tools, and Applications. IntechOpen. https://doi.org/10.5772/intechopen.100503
Kolchyna, O., Souza, T. T. P., Treleaven, P., & Aste, T. (2015). Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination. http://arxiv.org/abs/1507.00955
Landwehr, P. M., Wei, W., Kowalchuck, M., & Carley, K. M. (2016). Using tweets to support disaster planning, warning and response. Safety Science, 90, 33–47. https://doi.org/10.1016/j.ssci.2016.04.012
Monselise, M., Chang, C. H., Ferreira, G., Yang, R., & Yang, C. C. (2021). Topics and sentiments of public concerns regarding COVID-19 vaccines: Social media trend analysis. Journal of Medical Internet Research, 23(10). https://doi.org/10.2196/30765
Nguyen, T. T., Criss, S., Allen, A. M., Glymour, M. M., Phan, L., Trevino, R., Dasari, S., & Nguyen, Q. C. (2019). Pride, love, and twitter rants: Combining machine learning and qualitative techniques to understand what our tweets reveal about race in the us. International Journal of Environmental Research and Public Health, 16(10). https://doi.org/10.3390/ijerph16101766
Program, T., Siang, M., Tundo, G., & Rachmawati, D. N. (2024). Implementasi Algoritma Naive Bayes untuk Analisis Sentimen. In Jurnal Indonesia?: Manajemen Informatika dan Komunikasi (JIMIK) (Vol. 5, Issue 3). https://journal.stmiki.ac.id
Ratna Patria. (2022). Analisis Sentimen Media Sosial: Pengertian, Manfaat, Cara. Https://Www.Domainesia.Com/Berita/Analisis-Sentimen-Media-Sosial/.
Sudo, K., Murasaki, K., Kinebuchi, T., Kimura, S., & Waki, K. (2020). Machine Learning–Based Screening of Healthy Meals From Image Analysis: System Development and Pilot Study. JMIR Formative Research, 4(10), e18507. https://doi.org/10.2196/18507
Tao, Y., Zhang, F., Shi, C., & Chen, Y. (2019). Social media data-based sentiment analysis of tourists’ air quality perceptions. Sustainability (Switzerland), 11(18). https://doi.org/10.3390/su11185070
Syaputra, R., Ajhari, A. A., Xiuya, L., Jing, L., & Othman, M. S. Bin. (2025). Sentiment Analysis of Social Media Reactions to Indonesia’s Free School Lunch Program Using Machine Learning Techniques. International Journal of Academic Research in Progressive Education and Development, 14(4), 165–185.
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