Skripsi

KLASIFIKASI RAS MONGOLOID MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

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Penilaian

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Mongoloid is a type of human race that exists in the world. Each mongoloid race has different characteristics and physical traits including eyes, nose, and skin color. This research aims to build a system that can classify mongoloid races from input images. This system uses the Convolutional Neural Network method which then the error results from the classification using the Confusion Matrix calculation. The dataset used is image data with a total of 1600 images divided into 1280 training data and 320 testing data. Experiments were carried out by changing the learning rate and epoch parameters of the model that had been built. Based on the experiments conducted in this study, the highest accuracy classification rate of 90% was obtained from a combination of a learning rate of 0.0001 and an epoch of 15 with an accuracy value of 90%, precision 95%, recall 87%, and f1-score 91%. So the combination of a lower learning rate value and a larger number of epochs provides a higher level of accuracy for classification.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002193	T110941	T1109412023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1109412023

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xviii, 61 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Informatika
Pemrosesan Data Elektronik

Specific Detail Info

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Statement of Responsibility

MURZ

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