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All Journal Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
Berutu, Asro Hayati
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Religion Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Analysis of Indonesian Netizen Sentiment Towards the Government's Campaign on the Use of Artificial Intelligence Using the Naive Bayes Algorithm Nasution, Salsabila; Berutu, Asro Hayati; Aulia, Fatwa
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.28

Abstract

The development of artificial intelligence (AI) has encouraged the Indonesian government to adopt this technology in various public service sectors. However, the use of AI has received mixed responses from the public, particularly on social media. This study aims to analyze Indonesian netizen sentiment towards the government's AI campaign using the Naive Bayes algorithm. Data was collected from the Twitter platform and analyzed through preprocessing, sentiment classification, and model evaluation. The results show that the majority of netizen sentiment is negative, with concerns related to unfairness for creative workers, a lack of regulation, and the use of AI for political gain. This research is expected to provide input for the government in designing more ethical and inclusive AI adoption policies.
Analysis of User Interaction Association Patterns in E-Learning Systems Using the Apriori Algorithm Rizka; Berutu, Asro Hayati; Nabawy, Putri; Pratama, Haris; Supiyandi
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.30

Abstract

The development of e-learning systems has generated a vast volume of user interaction data. Every activity—such as logging in, viewing materials, taking quizzes, and downloading assignments—contains valuable information that can be leveraged to enhance the effectiveness of online learning systems. This study aims to analyze user interaction association patterns in an e-learning system using the Apriori algorithm. A data mining approach was employed to identify relationships among features frequently accessed together, with a minimum support threshold of 0.4, minimum confidence of 0.6, and lift > 1.0. The dataset used consists of simulated (dummy) data representing seven user transactions and five main e-learning features. The analysis produced eight significant association rules with lift values above 1.0, indicating non-random relationships among features. Feature combinations such as {login} → {view_material} and {take_quiz} → {view_score} exhibited strong relationships, with confidence values reaching 0.75. These findings suggest the existence of dominant user interaction patterns that can be utilized to optimize navigation design, recommendation features, and overall user experience in e-learning systems. This research contributes to the application of the Apriori algorithm for exploring user access patterns in online education contexts, providing an analytical foundation for developing more adaptive and behavior-driven systems.
Co-Authors Aulia, Fatwa Nabawy, Putri Nasution, Salsabila Pratama, Haris Rizka Supiyandi Supiyandi