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All Journal JTT (Jurnal Teknologi Terpadu)
Zidan, Umar Rahman
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Civil Engineering, Building, Construction & Architecture Control & Systems 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. 
Co-Authors Ardhillah, Onky Azra, Muhammad Azyumardi Hermanto, Muhammad Lucky Ken Ditha Tania Maulizidan, Muammar Ramadhani Meiriza, Allsella Purba, Kevin Agustin