Skripsi

KLASIFIKASI KOMPOSISI MAKANAN UNTUK DETEKSI ALERGEN PENYAKIT ECZEMA MENGGUNAKAN ALGORITMA LSTM

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Penilaian

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Eczema, or Atopic Dermatitis, is a skin condition often triggered by certain allergens in food. The increasing prevalence of eczema requires a solution to help individuals prone to allergies recognize potential allergens in packaged food products. This study aims to develop a food composition classification system to detect allergens that may trigger eczema using the Long Short-Term Memory (LSTM) algorithm for text classification and Word2Vec for word representation. The dataset initially consisted of 282 food composition data collected from various sources. However, due to the imbalance in the number of labels, data augmentation was performed on the minority label, resulting in a total dataset of 499 entries. The data was then divided into 80% for training and 20% for testing. The study results showed that the developed model could identify allergens with an average accuracy of 88.95%. The model evaluation achieved the best metrics with an accuracy of 97%, precision of 97%, recall of 96%, and an F1-score of 96%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407006942	T162725	T1627252024	Central Library (REFERENS)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1627252024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xiv, 165 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)

Teknik Informatika
Pemerosesan data

Specific Detail Info

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Statement of Responsibility

MURZ

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