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OPTIMISASI PARAMETER CONVOLUTIONAL NEURAL NETWORK 1-DIMENSI PADA KLASIFIKASI PENYAKIT JANTUNG MENGGUNAKAN ALGORITMA GRID SEARCH

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Penilaian

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Heart disease is a condition where the heart does not work normally so that it can affect the structure and function of the heart itself. One of the medical tests that can be done to detect heart disease is an electrocardiogram (EKG). Errors during diagnosis often occur when the ECG is analyzed manually. However, in recent years the computational processing has been done a lot. In the research conducted, Convolutional Neural Network (CNN) 1-dimensional architecture is able to learn features directly so as to prevent the loss of important features and can improve accuracy. In addition, the optimization method uses a grid search algorithm to optimize the parameters to improve the performance of the proposed architecture. The ECG signal databases used are The PTB Diagnosis and BIDMC Congestive Heart Failure. In this study, classification of heart disease was carried out in 4 classes and 6 classes were tested to obtain the best combination model of the parameters of batch size, learning rate, and epoch. The best combination of parameters in 6 clases is a batch size of 16, a learning rate of 0.0001 and an epoch of 100 with a performance of 99.37% accuracy, 91.91% sensitivity, 99.18% specificity, 95.58% precision, and an F1-score of 93.67%. Then the model was tested using K-Fold with the best model on the 9th fold with an accuracy of 99.50%, sensitivity 92.28%, specificity 99.38%, precision 96.57%, and F1-score of 94.30%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002416	T49525	T495252021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T495252021

Publisher

Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Uniersitas Sriwijaya., 2021

Collation

xvii, 110 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.707

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)

Prodi Sistem Komputer
Data Sistem Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

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