KLASIFIKASI KOMPOSISI MAKANAN UNTUK DETEKSI ALERGEN PENYAKIT ECZEMA MENGGUNAKAN ALGORITMA LSTM

MORGAN, JOVANIC and Rini, Dian Palupi and Rizqie, M. Qurhanul (2024) KLASIFIKASI KOMPOSISI MAKANAN UNTUK DETEKSI ALERGEN PENYAKIT ECZEMA MENGGUNAKAN ALGORITMA LSTM. Undergraduate thesis, Sriwijaya University.

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Abstract

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%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Eczema, makanan, deteksi, alergen, Long Short Term Memory, Word2Vec
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Jovanic Morgan
Date Deposited:	07 Jan 2025 01:51
Last Modified:	07 Jan 2025 01:51
URI:	http://repository.unsri.ac.id/id/eprint/162725

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