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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50017779" target="_blank" >RIV/62690094:18450/21:50017779 - isvavai.cz</a>

Result on the web

<a href="https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/1532" target="_blank" >https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/1532</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.31436/iiumej.v22i1.1532" target="_blank" >10.31436/iiumej.v22i1.1532</a>

Result language

angličtina

Original language name

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

Original language description

In 2019, citizens of Indonesia participated 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&apos; 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 also negative opinions on the respective presidential candidates. The campaigning of respective social media users on their choice of candidates for regents, 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 2019 Indonesia 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 &quot;Jokowi and Prabowo.&quot; 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 Naive Bayes Classifier (NBC) and a Support Vector Machine (SVM) to achieve an optimal and maximum optimization accuracy. 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 Indonesian presidential election on Twitter using non-conventional processes 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%.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

21101 - Food and beverages

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IIUM ENGINEERING JOURNAL

ISSN

1511-788X

e-ISSN

—

Volume of the periodical

22

Issue of the periodical within the volume

1

Country of publishing house

MY - MALAYSIA

Number of pages

16

Pages from-to

78-93

UT code for WoS article

000605375600007

EID of the result in the Scopus database

2-s2.0-85099953325