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Agents: Journal of Artificial Intelligence and Data Science
Published by Universitas Islam Negeri Alauddin Makassar
ISSN : 27469204     EISSN : 27469190     DOI : https://doi.org/10.24252/jagti.v4i1.74
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
The AGENTS published the original manuscripts from researchers, practitioners, and students in the various topics of Artificial Intelligence and Data Science including but not limited to fuzzy logic, genetic algorithm, evolutionary computation, neural network, hybrid systems, adaptation and learning systems, biologically inspired evolutionary system, system life science, distributed intelligence systems, network systems, human interface, machine learning, and knowledge discovery.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 1 Documents
 1 
Search results for , issue "Vol 5 No 2 (2025): March - August" : 1 Documents clear
PREDIKSI FAKTOR RISIKO GANGGUAN TIDUR MENGGUNAKAN PENDEKATAN MACHINE LEARNING LOGISTIC REGRESSION DAN GRADIENT BOOSTING Reza, Reza Fitriansyah; Gumelar, R Tommy; Amrizal, Amrizal
AGENTS: Journal of Artificial Intelligence and Data Science Vol 5 No 2 (2025): March - August
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v5i2.85

Abstract

Sleep disorders are one of the major public health issues with broad implications for quality of life, productivity, and chronic disease risks. This study aims to predict risk factors of sleep disorders using a machine learning approach with survey data from the National Sleep Foundation. The research process involved data Cleaning, transformation, normalization, and splitting into training (80%) and testing (20%) sets. Two algorithms were applied, Logistic Regression and Gradient Boosting, and their performance was evaluated using Accuracy, Precision, Recall, and F1-score metrics. SHAP analysis was also employed to assess variable contributions to model predictions. The results indicate that Gradient Boosting outperformed Logistic Regression, achieving perfect Accuracy and F1-score (1.00), while Logistic Regression only reached 0.70. SHAP analysis revealed that sleep duration and quality are the most influential factors, followed by caffeine consumption and age. Therefore, Gradient Boosting not only provides accurate classification but also comprehensive insights into key determinants of sleep disorders, serving as a foundation for more effective health interventions.

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