Skripsi

PREDIKSI POLA HENTI JANTUNG BERDASARKAN DELAPAN DATA TANDA VITAL MENGGUNAKAN METODE RECURRENT NEURAL NETWORK

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Penilaian

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The ability to predict patterns of Cardiac Arrest with high accuracy can significantly contribute to more effective prevention and treatment. This research focuses on predicting patterns of Cardiac Arrest based on eight vital sign data using Recurrent Neural Network (RNN) methods with LSTM and Bi-LSTM architectures. In order to address the issue of imbalanced data, undersampling techniques and class weighting with cost-sensitive learning approach are employed. To fill in missing values in the dataset, this study utilizes Linear Interpolation as well as Deep Learning techniques such as Autoencoder and U-Net for data imputation. The best performance is achieved by the cost-sensitive Bi-LSTM (CSBi-LSTM) model without undersampling the majority class. Linear Interpolation is applied for data imputation with a total data duration and prediction range of 60 minutes. The evaluation results of the CSBi-LSTM model on accuracy, sensitivity, precision, f1-score, and specificity metrics are 95%, 95%, 5%, 10%, and 100% respectively.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002410	T113569	T1135692023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1135692023

Publisher

Indralaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xiv, 71 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.307

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Sistem Komputer
Processing Mode

Specific Detail Info

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Statement of Responsibility

FIRA

Other version/related

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