Decision Support System for Sentiment Analysis of Youtube Comments on Government Policies
DOI:
https://doi.org/10.47709/cnahpc.v5i1.1999Dimension:
Keywords:
Decision Support System, Sentiment Analysis, Government Policy, Text-Mining, Naïve BayesAbstract
Sentiment analysis is the process of classifying a text dataset as positive, negative or neutral. Youtube is one of the popular media used to provide responses to a problem. In the Jokowi era, infrastructure development was carried out massively and evenly, one of which was in Bali Province, namely the construction of the Mengwi-Gilimanuk Toll Road. The construction of the Mengwi-Gilimanuk Toll Road consumed a lot of people's agricultural land, which resulted in various pro and con responses from the community. From these problems, sentiment analysis is carried out to get community reviews related to the object being analyzed by utilizing algorithms to be able to classify opinions, in the construction of this system the naïve bayes algorithm is used with testing methods namely accuracy, precision, and recall. From the sentiment analysis conducted by utilizing 18 video links on YouTube with 701 comments, it produces positive sentiment as much as 50.64%, negative sentiment as much as 7.70% and neutral sentiment as much as 39.23%.
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Alam, S., Resmi, M. G., & Masripah, N. (2022). Classification of Covid-19 vaccine data screening with Naive Bayes algorithm using Knowledge Discovery in database method. Journal of Computer Networks, Architecture and High Performance Computing, 4(2), 177–185.
Bhuiyan, H., Ara, J., Bardhan, R., & Islam, M. R. (2017). Retrieving YouTube video by sentiment analysis on user comment. 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 474–478.
Cunha, A. A. L., Costa, M. C., & Pacheco, M. A. C. (2019). Sentiment analysis of youtube video comments using deep neural networks. International Conference on Artificial Intelligence and Soft Computing, 561–570.
Dar, M. H., Sihombing, V., & Utami, Y. A. D. (2021). Implementasi Algoritma Naïve Bayes Classifier ( NBC ) untuk Ana- lisis Sentimen Komentar Kebijakan Full Day School. 6(1), 61–66.
Gao, H., Zeng, X., & Yao, C. (2019). Application of improved distributed naive Bayesian algorithms in text classification. The Journal of Supercomputing, 75(9), 5831–5847.
Gunawan, B., Pratiwi, H. S., & Pratama, E. E. (2018). Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 4(2), 113. https://doi.org/10.26418/jp.v4i2.27526
Krisnananda, J., & Kartika, A. A. G. (2021). Analisis Kelayakan Ekonomi dan Finansial Pembangunan Jalan Tol Mengwi-Gilimanuk. Jurnal Teknik ITS, 10(2), E253–E258.
Laksono, R. A., Sungkono, K. R., Sarno, R., & Wahyuni, C. S. (2019). Sentiment analysis of restaurant customer reviews on tripadvisor using naïve bayes. Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019, July, 49–54. https://doi.org/10.1109/ICTS.2019.8850982
Salam, A., Zeniarja, J., & Khasanah, R. S. U. (2018). Analisis Sentimen Data Komentar Sosial Media Facebook Dengan K-Nearest Neighbor (Studi Kasus Pada Akun Jasa Ekspedisi Barang J&T Ekpress Indonesia). Prosiding SINTAK, 480–486.
Saputra, P. Y., Subhi, D. H., & Winatama, F. Z. A. (2019). Implementasi Sentimen Analisis Komentar Channel Video Pelayanan Pemerintah Di Youtube Menggunakan Algoritma Naïve Bayes. Jurnal Informatika Polinema, 5(4), 209–213. https://doi.org/10.33795/jip.v5i4.259
Syarifuddin, M. (2020). Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn. Inti Nusa Mandiri, 15(1), 23–28.
Tang, X., Li, J., Liu, M., Liu, W., & Hong, H. (2020). Flood susceptibility assessment based on a novel random Naïve Bayes method: A comparison between different factor discretization methods. Catena, 190, 104536.
Udayana, I. P. A. E. D., Kherismawati, N. P. E., & Sudipa, I. G. I. (2022). Detection of Student Drowsiness Using Ensemble Regression Trees in Online Learning During a COVID-19 Pandemic. Telematika: Jurnal Informatika Dan Teknologi Informasi, 19(2), 229–244.
Udayana, I., Sudarma, M., & Kumara, I. N. S. (2017). Balinese Latin Text Becomes Aksara Bali Using Rule Base Method. Int. J. Res. IT, Manag. Eng, 7(05), 1–7.
Wiana, K. (2018). Sad Kertih: Sastra Agama, Filosofi, dan Aktualisasinya. Bali Membangun Bali, Jurnal Bappeda Litbang, 1, 159–179.
Wongkar, M., & Angdresey, A. (2019). Sentiment analysis using Naive Bayes Algorithm of the data crawler: Twitter. 2019 Fourth International Conference on Informatics and Computing (ICIC), 1–5.
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