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All Journal JTT (Jurnal Teknologi Terpadu) IJIE (Indonesian Journal of Informatics Education)
Maulizidan, Muammar Ramadhani
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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PREDIKSI GANGGUAN PANIK MENGGUNAKAN KNOWLEDGE DISCOVERY IN DATABASE DENGAN ALGORITMA GRADIENT BOOSTING Maulizidan, Muammar Ramadhani; Hermanto, Muhammad Lucky; Ardhillah, Onky; Azra, Muhammad Azyumardi; Purba, Kevin Agustin; Zidan, Umar Rahman; Tania, Ken Ditha; Meiriza, Allsella
Jurnal Teknologi Terpadu Vol 13, No 2 (2025): JTT (Jurnal Terpadu Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32487/jtt.v13i2.2518

Abstract

In an effort to enhance the diagnosis and intervention of panic disorder, this study develops a predictive model for determining the severity level of panic disorder using the Knowledge Discovery in Databases (KDD) approach. The dataset comprises variables such as age, gender, personal and family history, current stressors, symptom severity, impact on daily life, demographics, medical history, psychiatric history, substance use, coping mechanisms, social support, and lifestyle factors. The Gradient Boosting algorithm was employed to analyze the data and uncover complex patterns among the variables. The results indicate that the proposed model is capable of classifying the severity of panic disorder with high accuracy, aligning with findings from previous studies that utilized similar approaches. Other research also supports the effectiveness of machine learning algorithms in predicting panic attacks using data from wearable devices and mobile applications. These findings are expected to contribute to the development of decision support systems in the field of mental health. 
Knowledge Discovery Based on Sentiment Analysis of Public Perceptions About Generative AI on X Maulizidan, Muammar Ramadhani; Tania, Ken Ditha
IJIE (Indonesian Journal of Informatics Education) Vol 9, No 2 (2025): (IJIE) Indonesian Journal of Informatics Education - December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v9i2.107758

Abstract

Public discourse surrounding Generative Artificial Intelligence (GenAI) reflects diverse attitudes ranging from optimism to ethical concern, particularly as these technologies become increasingly discussed in educational contexts. This study examines public perceptions of GenAI on the social media platform X using a knowledge discovery approach that integrates multiple topic modeling techniques and Aspect-Based Sentiment Analysis (ABSA). A total of 111,675 English-language tweets collected between June 23, 2024, and June 23, 2025, were analyzed using five topic modeling methods BERTopic, Top2Vec, LDA, LSA, and NMF to identify dominant discussion themes and evaluate topic coherence. Sentiment toward specific GenAI aspects was subsequently examined using ABSA to capture fine-grained public attitudes. The results indicate that topics related to ethics and creativity are predominantly associated with negative sentiment, while innovation and cloud-related discussions show higher levels of positive sentiment. Education-related topics are largely characterized by neutral sentiment, suggesting exploratory and informational discourse. These findings highlight the importance of addressing ethical awareness, trust, and AI literacy in informatics education. By combining multi-model topic analysis with aspect-level sentiment interpretation, this study provides methodological insights and empirical evidence to support responsible GenAI integration in educational contexts.
Co-Authors Ardhillah, Onky Azra, Muhammad Azyumardi Hermanto, Muhammad Lucky Ken Ditha Tania Meiriza, Allsella Purba, Kevin Agustin Zidan, Umar Rahman