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All Journal Jurnal Teknik Informatika (JUTIF)
Joshua Muliawan
Business Information System Program, Faculty of Science and Technology, Universitas Pradita, Indonesia
Computer Science & IT

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SENTIMENT ANALYSIS OF INDONESIA'S CAPITAL CITY RELOCATION USING THREE ALGORITHMS: NAÏVE BAYES, KNN, AND RANDOM FOREST Joshua Muliawan; Erick Dazki
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.1436

Abstract

The relocation of Indonesia's capital city from Jakarta to the IKN Nusantara on the island of Borneo has become a trending topic that triggers conversations and opinions on various social media. The pros and cons of this policy are very pronounced in various media, especially on Twitter or X platform. The purpose of this research is to conduct a public sentiment analysis of public opinion related to the relocation of Indonesia's capital city. Data is taken from tweets comments collected during a certain period from June to September 2023. This research uses a Natural Language Processing approach with data pre-processing techniques to prepare the data before applying labeling and classification algorithms. This research tests the accuracy of three algorithms used in classification, namely Naïve Bayes Classifier, K-Nearest Neighbor, and Random Forest. The results of the data classification show that positive sentiment has a value of 36.8%, neutral sentiment is at 25%, and negative sentiment related to the relocation of the capital city is 38.1%. Then an accuracy test was carried out on the Naïve Bayes Classifier Algorithm method which found an accuracy value of 65.26%, the K-Nearest Neighbor Algorithm of 58.25%, and the Random Forest Algorithm of 45.05%. This shows that the Naïve Bayes Classifier Algorithm method has better accuracy than other algorithms in predicting classification in sentiment analysis. This research also identifies the frequency of key words that often appear in each sentiment which can be valuable information for monitoring public opinion on social media.
SENTIMENT ANALYSIS OF INDONESIAN ELECTION 2024 USING THE K-NEAREST NEIGHBOR METHOD Rido Dwi Kurniawan; Joshua Muliawan
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1934

Abstract

The abstract of this research discusses the analysis of Indonesian public sentiment regarding the 2024 election as observed via Twitter. Sentiment graph Research uses the Natural Language Processing method and the K-Nearest Neighbor algorithm to classify sentiment as positive, neutral, or negative. The current era of globalization influences the rapid progress of information technology circulating in society, one of the intermediaries is through the social media Twitter. Twitter can be used as a means of conveying opinions regarding suggestions, criticism and public opinion. Currently social media has a big impact on building public political sentiment and preferences. The social media I took is Twitter so that people's Tweets related to elections can be used to see a picture of public opinion. There are various opinions of Twitter users with positive, neutral and negative sentiments. However, classifying sentiment from Twitter users requires quite a lot of time and effort due to the large number of tweets found. The aim of this research is to conduct a public sentiment analysis of public opinion regarding the 2024 election. Data was collected in October and December 2023. The results show that positive sentiment dominates with 76%, followed by neutral sentiment at 16%, and negative 6%. This analysis helps understand public opinion regarding the 2024 election on social media, especially Twitter.
Co-Authors Dazki, Erick Rido Dwi Kurniawan