Analisis Sentimen Pengguna Twitter Terhadap Kinerja Walikota Medan Menggunakan Metode Naive Bayes Classifier
DOI:
https://doi.org/10.59581/jusiik-widyakarya.v1i2.17Keywords:
Sentiment Analysis, TextBlob, TF-IDF, Multinomial Naïve Bayes, Confusion MatrixAbstract
Technological developments today make it easy for people to use social media as a means of expressing opinions, including Twitter. The case study taken by the researcher is the sentiment towards the performance of the mayor of Medan. The case was taken because it was widely discussed by Indonesian people, especially the city of Medan on Twitter social media. One of the uses of this research is to find out the trend of Twitter user comments on the performance of the mayor of Medan by conducting a sentiment analysis. Sentiment will be classified as positive, negative and neutral. The algorithm used in sentiment analysis is Naïve Bayes. The stages in conducting sentiment analysis in this study are data preprocessing, data processing, classification, and evaluation. The results of this study are using the SMOTE method, the training and testing ratio is 80:20 because it has the highest accuracy, which is 78% compared to other ratios. The prediction results resulting from the classification turned out to be more dominant towards neutral labels. In addition to classifying for sentiment analysis, this study also measures the performance of the model created. The results showed that the Naïve Bayes algorithm has a precision value of 78%, a recall of 78%, and an f1-score of 77%.
References
APJII. (2020). "Penetrasi Internet di Indonesia Capai 73,7 Persen." Dailysocial.Id.
Paisal, P. (2020). "Analisis Sentimen Masyarakat Berdasarkan Opini dari Sosial Media Menggunakan Metode Naive Bayes Classifier (Study Kasus: Universitas Sjakhyakirti)." Jurnal Ilmiah Informatika Global, 11(1), 41–46. doi:10.36982/jig.v11i1.1071
Utsu, K., Saito, J., & Uchida, O. (2018). "Sentiment Polarity Estimation of Emoticons by Polarity Scoring of Character Components." 2018 IEEE Region 10 Symposium, Tensymp 2018, 237–242. doi:10.1109/TENCONSpring.2018.8691984
Kevin Perdana, Titania Pricillia, & Zulfachmi. (2021). "Optimasi Textblob Menggunakan Support Vector Machine Untuk Analisis Sentimen (Studi Kasus Layanan Telkomsel)." Jurnal Bangkit Indonesia, 10(1), 13–15. doi:10.52771/bangkitindonesia.v10i1.120
Vashishtha, S., & Susan, S. (2020). "Fuzzy Interpretation of Word Polarity Scores for Unsupervised Sentiment Analysis." 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, 1–6. doi:10.1109/ICCCNT49239.2020.9225646
Afdhaluzzikri, A., Mawengkang, H., & Sitompul, O. S. (2022). "Perfomance analysis of Naive Bayes method with data weighting." SinkrOn, 7(3), 817–821. doi:10.33395/sinkron.v7i3.11516
Singla, S., & Kumar, V. (2020). "Multi-Class Sentiment Classification using Machine Learning and Deep International Journal of Computer Sciences and Engineering Open Access Multi-Class Sentiment Classification using Machine Learning and Deep Learning Techniques." November. doi:10.26438/ijcse/v8i11.1420
Sulistiyowati, N., & Jajuli, M. (2020). "Integrasi Naive Bayes Dengan Teknik Sampling Smote Untuk Menangani Data Tidak Seimbang." Nuansa Informatika, 14(1), 34. doi:10.25134/nuansa.v14i1.2411
