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All Journal Journal of Information Systems and Informatics Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Agam, Padel Mohammad
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Computer Science & IT Education Engineering Industrial & Manufacturing Engineering

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SENTIMENT ANALYSIS OF PUBLIC OPINION ON PRESIDENTIAL ADVISORY APPOINTMENTS USING NAIVE BAYES CLASSIFICATION Negara, Edi Surya; Syaputra, Rezki; Erlansyah, Deni; Andryani, Ria; Saksono, Prihambodo Hendro; Aditya, Ferdi; Agam, Padel Mohammad
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 8 No. 2 (2024): Volume 8, Nomor 2, December 2024
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v8i2.35254

Abstract

Social media platforms such as Twitter, Facebook, and YouTube have become significant channels for public discourse, where users freely express opinions, including negative sentiments and hate speech. To better understand public opinion, particularly in politically charged contexts, sentiment analysis can classify user comments as either positive or negative. This study aims to analyze public sentiment regarding the formation of a special advisory team for President Jokowi, using a sentiment classification approach. The study employed a Naïve Bayes classifier to analyze sentiment from 3,000 comments gathered from Twitter, Facebook, and YouTube. The dataset was divided into 80% training data (used to train the model with known sentiment) and 20% test data. The Naïve Bayes algorithm was chosen for its simplicity and effectiveness in handling large datasets in text classification tasks. The Naive Bayes classification on sentiment analysis of public opinion regarding the appointment of presidential advisors achieved an overall accuracy of 71% in classifying the test data. Negative sentiment was classified with an accuracy of 71%, while positive sentiment was classified with an accuracy of 70%. The results demonstrate that the Naïve Bayes classifier is a viable method for sentiment analysis in political discourse, although the model's performance indicates room for improvement. The novelty of this research lies in its focus on sentiment analysis of public opinion specifically related to presidential advisory appointments, an area not yet extensively explored in sentiment analysis studies. This study contributes to the field by providing insights into the public’s perception of political decisions using machine learning techniques. The implications for future research include refining classification methods for better accuracy and applying the model to other political or governmental topics.
User Satisfaction and Application Usage of PA'KEPO: A UTAUT 2 Model Analysis Agam, Padel Mohammad; Negara, Edi Surya; Andryani, Ria
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.888

Abstract

The PA'KEPO (Payo jadi Keluargo Polisi) application is a mobile Android application owned by Polda Sumsel for the recruitment process of police members. This study aims to evaluate user satisfaction and the use of the PA'KEPO application by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) 2 model with the addition of the Perceived Trust variable, which represents users' trust in the security and reliability of the application, enhancing users' intention to use the application and impacting actual usage behavior. The analysis of the relationships between variables employs the Structural Equation Modeling (SEM) approach to test the complex relationships between latent variables, allowing for the analysis of data with diverse scales. The research results indicate that the majority of respondents experienced a high level of satisfaction with the PA'KEPO application. The most influential variables—Effort Expectancy, Perceived Trust, Hedonic Motivation, Habit, and Behavioral Intention—significantly affect Use Behavior. Based on these findings, it is recommended that Polda Sumsel continue to encourage the use of the PA'KEPO application by optimizing the variables that have not yet shown significant effects. Improvement recommendations include evaluating and enhancing the application's functionality in line with the daily needs of its users.
Co-Authors Aditya, Ferdi Andryani, Ria Deni Erlansyah Edi Surya Negara Saksono, Prihambodo Hendro Syaputra, Rezki