Ahmad, I., Basheri, M., Iqbal, M. J., & Rahim, A. (2018). Performance Comparison of
Support Vector Machine, Random Forest, and Extreme Learning Machine for
Intrusion Detection. IEEE Access, 6(c), 33789–33795.
https://doi.org/10.1109/ACCESS.2018.2841987
Buntoro, G. A. (2017). Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di
Twitter. Integer Journal, 2(1), 32–41.
https://doi.org/https://doi.org/10.31284/j.integer.2017.v2i1.95
Cai, C., Li, L., & Zeng, D. (2016). New words enlightened sentiment analysis in
social media. IEEE International Conference on Intelligence and Security
Informatics: Cybersecurity and Big Data, ISI 2016, 202–204.
https://doi.org/10.1109/ISI.2016.7745470
Cheng, L. C., & Tsai, S. L. (2019). Deep learning for automated sentiment analysis of
social media. Proceedings of the 2019 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining, ASONAM 2019, 1001–1004.
https://doi.org/10.1145/3341161.3344821
Dadgar, S. M. H., Araghi, M. S., & Farahani, M. M. (2016). A novel text mining
approach based on TF-IDF and support vector machine for news classification.
Proceedings of 2nd IEEE International Conference on Engineering and
Technology, ICETECH 2016, 112–116.
https://doi.org/10.1109/ICETECH.2016.7569223
Dwivedi, S. K., & Rawat, B. (2016). A review paper on data preprocessing: A critical
phase in web usage mining process. Proceedings of the 2015 International
Conference on Green Computing and Internet of Things, ICGCIoT 2015, 506–
510. https://doi.org/10.1109/ICGCIoT.2015.7380517
Fatimah Dato Ahmad. (1995). Sistem capaian dokumen bahasa melayu: satu
pendekatan eksperimen & analisis. Tesis Dr Falsafah, Jabatan Sains Komputer,
Universiti Kebangsaan Malaysia.
Fitri, V. A., Andreswari, R., & Hasibuan, M. A. (2019). Sentiment analysis of social
media Twitter with case of Anti-LGBT campaign in Indonesia using Naïve
Bayes, decision tree, and random forest algorithm. Procedia Computer Science,
161, 765–772. https://doi.org/10.1016/j.procs.2019.11.181
Gao, Z., Feng, A. O., Song, X., & Wu, X. I. (2019). Target-Dependent Sentiment
Classification With BERT. IEEE Access, 7, 154290–154299.
https://doi.org/10.1109/ACCESS.2019.2946594
Guzman, E., & Maalej, W. (2014). How Do Users Like This Feature ? A Fine Grained
Sentiment Analysis of App Reviews. 2014 IEEE 22nd International Requirements
Engineering Conference (RE).
Hakim, A. A., Erwin, A., Eng, K. I., Galinium, M., & Muliady, W. (2014). Automated
document classification for news article in Bahasa Indonesia based on term
frequency inverse document frequency (TF-IDF) approach. Proceedings - 2014
6th International Conference on Information Technology and Electrical
Engineering: Leveraging Research and Technology Through University-Industry
Collaboration, ICITEE 2014. https://doi.org/10.1109/ICITEED.2014.7007894
Hao, W., & Castanon, J. A. (2015). Sentiment Expression via Emoticons on Social
Media- Twitter. IEEE International Conference on Big Data (Big Data), 6(6),
818–824. https://doi.org/10.22214/ijraset.2018.6125
Haque, R., Islam, N., Tasneem, M., & Das, A. K. (2023). MULTI-CLASS
SENTIMENT CLASSIFICATION ON BENGALI SOCIAL MEDIA
COMMENTS USING. International Journal of Cognitive Computing in
Engineering, 0–45. https://doi.org/10.1016/j.ijcce.2023.01.001
Hassan, A. U., Hussain, J., Hussain, M., Sadiq, M., & Lee, S. (2017). Sentiment
analysis of social networking sites (SNS) data using machine learning approach
for the measurement of depression. International Conference on Information and
Communication Technology Convergence: ICT Convergence Technologies
Leading the Fourth Industrial Revolution, ICTC 2017, 2017-Decem, 138–140.
https://doi.org/10.1109/ICTC.2017.8190959
Hollander, J. B., Graves, E., Renski, H., Foster-Karim, C., Wiley, A., & Das, D.
(2016). A (Short) History of Social Media Sentiment Analysis. Urban Social
Listening: Potential and Pitfalls for Using Microblogging Data in Studying
Cities, 1–97. https://doi.org/10.1057/978-1-137-59491-4
Imamah, Husni, Rachman, E. M., Suzanti, I. O., & Mufarroha, F. A. (2019). Text
Mining and Support Vector Machine for Sentiment Analysis of Tourist Reviews
in Bangkalan Regency. Journal of Physics: Conference Series, 1477(2).
https://doi.org/10.1088/1742-6596/1477/2/022023
Imran, A. S., Daudpota, S. M., Kastrati, Z., & Bhatra, R. (2020). Cross-Cultural
Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on
COVID-19 Related Tweets. IEEE Access, 8, 181074–181090.
https://doi.org/10.1109/ACCESS.2020.3027350
Jony, R. I., Mohammed, N., Habib, A., Momen, S., & Rony, R. I. (2015). An
Evaluation of Data Processing Solutions Considering Preprocessing and
“special” Features. Proceedings - 11th International Conference on Signal-Image
Technology and Internet-Based Systems, SITIS 2015, 224–231.
https://doi.org/10.1109/SITIS.2015.125
Li, J., & Qiu, L. (2017). A sentiment analysis method of short texts in microblog.
Proceedings - 2017 IEEE International Conference on Computational Science
and Engineering and IEEE/IFIP International Conference on Embedded and
Ubiquitous Computing, CSE and EUC 2017, 1, 776–779.
https://doi.org/10.1109/CSE-EUC.2017.153
Li, S., & Wan, P. (2009). Study on the data preprocessing of the questionnaire based
on the combined classification data mining model. 2009 International
Conference on E-Learning, E-Business, Enterprise Information Systems, and E-
Government, EEEE 2009, 217–220. https://doi.org/10.1109/EEEE.2009.49
Luo, F., Li, C., & Cao, Z. (2016). Affective-feature-based sentiment analysis using
SVM classifier. Proceedings of the 2016 IEEE 20th International Conference on
Computer Supported Cooperative Work in Design, CSCWD 2016, 276–281.
https://doi.org/10.1109/CSCWD.2016.7566001
Mao, J., & Liu, W. (2019). A BERT-based approach for automatic humor detection
and scoring. CEUR Workshop Proceedings, 2421(September 2019), 197–202.
Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing
images with support vector machines. IEEE Transactions on Geoscience and
Remote Sensing, 42(8), 1778–1790. https://doi.org/10.1109/TGRS.2004.831865
Mishra, A., & Vishwakarma, S. (2015). Analysis of TF-IDF Model and its Variant for
Document Retrieval. Proceedings - 2015 International Conference on
Computational Intelligence and Communication Networks, CICN 2015, 772–776.
https://doi.org/10.1109/CICN.2015.157
Ren, L., Xu, B., Lin, H., Liu, X., & Yang, L. (2020). Sarcasm Detection with
Sentiment Semantics Enhanced Multi-level Memory Network. Neurocomputing,
401, 320–326. https://doi.org/10.1016/j.neucom.2020.03.081
Seki, Y. (2016). Use of Twitter for Analysis of Public Sentiment for Improvement of
Local Government Service. 2016 IEEE International Conference on Smart
Computing, SMARTCOMP 2016, 1–3.
https://doi.org/10.1109/SMARTCOMP.2016.7501726
Shi, H. X., & Li, X. J. (2011). A sentiment analysis model for hotel reviews based on
supervised learning. Proceedings - International Conference on Machine
Learning and Cybernetics, 950–954.
https://doi.org/10.1109/ICMLC.2011.6016866
Su, Y., Li, S., Ju, S., Zhou, G., & Li, X. (2012). Multi-view Learning for Semi-
supervised Sentiment Classification. 2012 International Conference on Asian
Language Processing Multi-View, 13–16. https://doi.org/10.1109/IALP.2012.53
Yahav, I., Shehory, O., & Schwartz, D. (2019). Comments Mining With TF-IDF: The
Inherent Bias and Its Removal. IEEE Transactions on Knowledge and Data
Engineering, 31(3), 437–450. https://doi.org/10.1109/TKDE.2018.2840127
Yunitasari, Y., Musdholifah, A., & Sari, A. K. (2019). Sarcasm Detection For
Sentiment Analysis in Indonesian Tweets. IJCCS (Indonesian Journal of
Computing and Cybernetics Systems), 13(1), 53.
https://doi.org/10.22146/ijccs.41136
Zhai, G., Yang, Y., Wang, H., & Du, S. (2020). Multi-attention fusion modeling for
sentiment analysis of educational big data. Big Data Mining and Analytics, 3(4),
311–319. https://doi.org/10.26599/BDMA.2020.9020024