Sistem Analisa Sentiment Bakal Calon Presiden 2024 Menggunakan Metode NLP Berbasis Web

Main Article Content

Abdul Mukti
Anggun Dwi Hadiyanti
Airlangga Nurlaela
Jeremy Panjaitan


The spread of forecasts for presidential candidates in digital media, especially articles, has created public interest in the popularity, politics and performance of the presidential candidate, which has the effect of leading public opinion to positive or negative sides depending on people's assessments, arguments and information conveyed. In increasing sources of accurate information on the election of presidential candidates, an information system will be built that will assist the public in objectively selecting presidential candidates. The word processing method will be processed and managed by Natural Language Processing (NLP) using the Naïve Baiyes Classifier Algorithm by comparing the KBBI (Big Indonesian Dictionary) vocabulary so that conclusions can be drawn about people's emotions (positive, negative and even neutral) and will present information in the form of charts , trends and content.

Article Details

How to Cite
Mukti, A., Hadiyanti, A., Nurlaela, A., & Panjaitan, J. (2023). Sistem Analisa Sentiment Bakal Calon Presiden 2024 Menggunakan Metode NLP Berbasis Web. SOSCIED, 6(1), 128-140.


I. Budi and R. R. Suryono, “Application of named entity recognition method for Indonesian datasets: a review,” Bull. Electr. Eng. Informatics, vol. 12, no. 2, pp. 969–978, 2023, doi: 10.11591/eei.v12i2.4529.

S. Fu, N. Lin, G. Zhu, and S. Jiang, “Towards Indonesian Part-of-Speech Tagging: Corpus and Models,” 2018 Int. Conf. Asian Lang. Process., vol. 1, pp. 303–307, 2018, [Online]. Available:

A. A. Jalal and B. H. Ali, “Text documents clustering using data mining techniques,” Int. J. Electr. Comput. Eng., vol. 11, no. 1, pp. 664–670, 2021, doi: 10.11591/ijece.v11i1.pp664-670.

S. K.Shinde, V. Bhojane, and P. Mahajan, “NLP based Object Oriented Analysis and Design from Requirement Specification,” Int. J. Comput. Appl., vol. 47, no. 21, pp. 30–34, 2012, doi: 10.5120/7475-0574.

H. Gohil, “a Review on a Emotion Detection and Recognization From Text a Review on a Emotion Detection and Recognization From Text Using Natural,” vol. 13, no. April, pp. 6745–6750, 2018.

V. S and J. R, “Text Mining: open Source Tokenization Tools – An Analysis,” Adv. Comput. Intell. An Int. J., vol. 3, no. 1, pp. 37–47, 2016, doi: 10.5121/acii.2016.3104.

Das Deepak, “Social Media Sentiment Analysis using Machine Learning : Part — I ,” Towar. Data Sci., vol. 6, no. 6, pp. 465–472, 2019, [Online]. Available:

A. Romanov, A. Kurtukova, M. Vasilieva, and R. Meshcheryakov, “Sentiment analysis of text using machine learning techniques,” CEUR Workshop Proc., vol. 2233, no. 05, pp. 2952–2956, 2017.

D. Jurafsky and J. Martin, “Naive bayes and sentiment classification,” Speech Lang. Process., p. 1024, 2019, [Online]. Available:

M. H. S. Quadri* and D. R. K. Selvakumar, “Performance of Naïve Bayes in Sentiment Analysis of User Reviews Online,” Int. J. Innov. Technol. Explor. Eng., vol. 10, no. 2, pp. 64–68, 2020, doi: 10.35940/ijitee.a8198.1210220.
Abstract viewed = 317 times
PDF downloaded = 681 times