Skripsi

KLASIFIKASI PENYAKIT PADA SALURAN PENCERNAAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN INCEPTIONV3

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Penilaian

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Diseases of the digestive tract are significant health issues that require quick and accurate diagnosis to enable effective treatment. This study aims to develop a classification model for digestive tract diseases using the Convolutional Neural Network (CNN) method with the InceptionV3 architecture. The model is evaluated using the 5-Fold Cross-Validation approach to enhance generalization and prediction reliability. The dataset utilized consists of various types of medical images of the digestive tract, which have undergone augmentation and normalization processes to boost model performance. In this experiment, the model was trained with a batch size of 16, image resolution of 299×299 pixels, over the course of 10 epochs. The evaluation results demonstrate that the developed model achieves high accuracy with an accuracy rate of 99%, proving that the InceptionV3 architecture is effective for the task of classifying digestive tract diseases. With these findings, this study is expected to contribute to the development of artificial intelligence-based systems to support medical professionals in the diagnostic process.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003669	T176743	T1767432025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1767432025

Publisher

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

Collation

xiii, 83 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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