IMPLEMENTATION OF A MACHINE LEARNING ALGORITHM FOR SENTIMENT ANALYSIS OF INDONESIA'S 2019 PRESIDENTIAL ELECTION

Buntoro, Ghulam Asrofi, Arifin, Rizal, Syaifuddiin, Gus Nanang and Selamat, Ali (2021) IMPLEMENTATION OF A MACHINE LEARNING ALGORITHM FOR SENTIMENT ANALYSIS OF INDONESIA'S 2019 PRESIDENTIAL ELECTION. IIUM Engineering Journal, 22 (1). pp. 78-92.

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Abstract

In 2019,citizens of Indonesiaparticipated in the democratic process of electing a new president, vice president, and various legislative candidates for the country. The 2019 Indonesian presidential election was very tense in terms of the candidates' campaigns in cyberspace, especially on social media sites such as Facebook,Twitter,Instagram,Google+, Tumblr,LinkedIn, etc. The Indonesian people used social media platforms to express their positive,neutral, and alsonegativeopinions on the respective presidential candidates.The campaigning of respective social media users on their choice of candidates forregents,governors, and legislative positions up to presidential candidates was conducted via the Internet and online media. Therefore, the aim of this paper is to conduct sentiment analysis on the candidates in the 2019Indonesia presidential election based on Twitter datasets. The study used datasets on the opinions expressed by the Indonesian people available on Twitter with the hashtags (#) containing "Jokowi and Prabowo." We conducted data pre-processing using a selection of comments, data cleansing, text parsing, sentence normalization and tokenization based on the given text in the Indonesian language, determination of class attributes, and, finally, we classified the Twitter posts with the hashtags (#) using Naïve Bayes Classifier (NBC) and a Support Vector Machine (SVM) to achieve an optimalandmaximumoptimizationaccuracy. The study provides benefits in terms of helping the community to research opinions on Twitter that contain positive, neutral, or negative sentiments. Sentiment Analysis on the candidates in the 2019 Indonesianpresidential election onTwitterusingnon-conventionalprocesses resulted in cost, time, and effort savings. This research proved that the combination of the SVM machine learning algorithm and alphabetic tokenization produced the highest accuracy value of 79.02%. While the lowest accuracy value in this study was obtained with a combination of the NBC machine learning algorithm and N-gram tokenization with an accuracy value of 44.94%.

Item Type:	Article
Uncontrolled Keywords:	sentiment analysis; president; Indonesia; naïve Bayes classifier; support vector machine
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering > Department of Informatic Engineering
Depositing User:	Library Umpo
Date Deposited:	15 Feb 2023 04:31
Last Modified:	15 Feb 2023 04:31
URI:	http://eprints.umpo.ac.id/id/eprint/10848

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