Analisis Sentimen Masyarakat terhadap Kampanye Sosial Pengurangan Food Waste di Indonesia
DOI:
https://doi.org/10.61132/uranus.v2i2.105Keywords:
sentiment analysis, food waste, campaign, social media, naive bayes classifierAbstract
The remaining food waste in Indonesia reaches around 46.35 million tons, with economic losses reaching 23 million to 48 million tons per year. This condition has led to various campaigns to reduce food waste from people concerned about the problem of food waste. However, the increase in food waste campaigns has yet to be accompanied by a decrease in the volume of food waste in Indonesia. This research aims to determine public sentiment toward food waste campaigns on Instagram social media and determine the accuracy of the methods used in data classification. The method used is the Naïve Bayes Classifier method. The results obtained were from a total of 118 data regarding the food waste campaign; 79% data showed that the public had a positive sentiment, and 21% other data had a negative sentiment. The accuracy results of using sentiment analysis were 78.94%; this shows that the performance of the Naïve Bayes method in classifying data is quite good.
References
Aqlan, A. A. Q., Manjula, B., & Naik, R. L. (2019). A study of sentiment analysis: Concepts, techniques, and challenges. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 28, Issue January). Springer Singapore. https://doi.org/10.1007/978-981-13-6459-4_16
Bappenas. (2021). Food Loss and Waste di Indonesia. Laporan Kajian Food Loss and Waste di Indonesia, 1–116.
Dalianis, H. (2018). Evaluation metrics and evaluation. In Clinical Text Mining (pp. 45-53). Springer. https://doi.org/10.1007/978-3-319-78503-5_6
DataReportal. (2023). Instagram users in Indonesia in 2023. In DataReportal (Issue 17 June 2023). https://napoleoncat.com/stats/instagram-users-in-indonesia/2023/01/
Deolika, A., Kusrini, K., & Luthfi, E. T. (2019). Analisis pembobotan kata pada klasifikasi text mining. Jurnal Teknologi Informasi, 3(2), 179. https://doi.org/10.36294/jurti.v3i2.1077
Evan, D. M., Natanael, I. N., & Aryani, D. I. (2022). Perancangan konten digital edukasi tentang anti “food waste” melalui video animasi. DEKAVE: Jurnal Desain Komunikasi Visual, 12(3), 235. https://doi.org/10.24036/dekave.v12i3.117939
Global Hunger Index. (2023). Afghanistan - Global Hunger Index 2023. www.globalhungerindex.org
Hidayat, M. T., Retnoningsih, S., & Hidayat, R. (2018). Kampanye pencegahan pembuangan makanan bagi masyarakat dengan berbagi makanan terhadap sesama. Jurnal Rekamakna ITN, 1–14.
Petiwi, M. I., Triayudi, A., & Sholihati, I. D. (2022). Analisis sentimen Gofood berdasarkan Twitter menggunakan metode Naïve Bayes dan Support Vector Machine. Jurnal Media Informatika Budidarma, 6(1), 542. https://doi.org/10.30865/mib.v6i1.3530
Putro, H. F., Vulandari, R. T., & Saptomo, W. L. Y. (2020). Penerapan metode Naive Bayes untuk klasifikasi pelanggan. Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN), 8(2). https://doi.org/10.30646/tikomsin.v8i2.500
Richter, B. (2017). Knowledge and perception of food waste among German consumers. Journal of Cleaner Production, 166, 641-648. https://doi.org/10.1016/j.jclepro.2017.08.009
Ruhyana, N. (2019). Analisis sentimen terhadap penerapan sistem plat nomor ganjil/genap pada Twitter dengan metode klasifikasi Naive Bayes. Jurnal IKRA-ITH Informatika, 3(1), 94–99. www.situs.com
Satria, R. D., Suarjaya, I. M. A. D., & Pratama, I. P. A. E. (2022). Sentiment analysis antusias masyarakat terhadap sampah plastik dengan menggunakan metode Support Vector Machine (SVM). JITTER: Jurnal Ilmiah Teknologi dan Komputer, 3(1), 880–893.
SIPSN. (2022). SIPSN - Sistem Informasi Pengelolaan Sampah Nasional. https://sipsn.menlhk.go.id/
Ting, K. M. (2017). Confusion matrix. In Encyclopedia of Machine Learning and Data Mining (pp. 260-260). Springer. https://doi.org/10.1007/978-1-4899-7687-1_50
UNEP. (2021). Food Waste Index Report 2021. United Nations Environment Programme.
We Are Social. (2023). Digital 2023 Indonesia: Platform social media yang sering digunakan di Indonesia 2023. Wearesocial, 56. https://wearesocial.com/id/blog/2023/01/digital-2023/
Yanis, R. (2018). Sentiment analysis of BPJS Kesehatan services to SMK Eklesia and Bina Insani Jailolo teachers. Jurnal Terapan Teknologi Informasi, 2(2), 113–122. https://doi.org/10.21460/jutei.2018.22.105
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