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KLASIFIKASI JENIS GANGGUAN TIDUR MENGGUNAKAN METODE LONG SHORT TERM MEMORY (LSTM)

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Sleep quality plays a critical role in maintaining physical, mental, and emotional wellbeing. Early detection of sleep disorders such as insomnia and sleep apnea is essential for enabling more effective clinical interventions. This study investigates the optimization of sleep disorder classification using Long Short-Term Memory (LSTM) networks based on time-series data incorporating 13 physiological and behavioral features including sleep duration, heart rate, and stress levels. The preprocessing pipeline included data normalization, categorical feature encoding, and decomposition of blood pressure components to ensure input consistency. We systematically evaluated the LSTM architecture through comprehensive hyperparameter tuning, examining variations in: the number of LSTM units, dropout rates, dense layer configurations, training epochs, and batch sizes. The optimal model configuration achieved 92% classification accuracy with 16 LSTM units, a dropout rate of 0.2, a dense layer of 32 units, and a batch size of 8. This configuration yielded precision of 91.99%, recall of 92%, and F1-score of 91.88%. Model convergence occurred at epoch 16 without evidence of overfitting. Notably, increasing LSTM units to 32 or 64 resulted in reduced accuracy (89.33%), suggesting increased susceptibility to overfitting with larger architectures. These findings demonstrate the effectiveness of LSTM networks in identifying complex temporal patterns associated with sleep disorders while emphasizing the importance of careful hyperparameter optimization for classification performance. The proposed approach shows promise for developing more accurate and efficient digital tools for sleep disorder detection and assessment.

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Detail Information

Series Title

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Call Number

T1840202025

Publisher

Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xxi, V-2 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.307

Content Type

Text

Media Type

-

Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Informatika, Fakultas Ilmu Komputer
software/aplikasi perangkat lunak

Specific Detail Info

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Statement of Responsibility

NO

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