Skripsi

KLASIFIKASI GANGGUAN TIDUR BERDASARKAN KONDISI FISIOLOGIS DAN GAYA HIDUP MENGGUNAKAN MACHINE LEARNING

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Sleep disorders negatively impact rest quality and daily productivity. This study develops a classification model for sleep disorders using Machine Learning based on physiological conditions and lifestyle factors. The dataset consists of 15,000 records from Kaggle with balanced distribution across three categories: Healthy, Insomnia, and Sleep Apnea. Four algorithms were tested: Decision Tree, Random Forest, K-Nearest Neighbor (KNN), and Naïve Bayes, optimized using Grid Search Cross Validation. Feature selection with SelectKBest identified eleven key features from the dataset. Random Forest achieved the best performance with 96.07% accuracy after hyperparameter tuning. The model was integrated into a web-based system named SomniScan for sleep disorder classification. This research demonstrates that machine learning effectively supports early identification of sleep disorders.

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Availability

Detail Information

Series Title

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Call Number

T1941242026

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xi, 140 hlm.; ab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.312 07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Kecerdasan Buatan
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

TUTI

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