THESIS

Performance Analysis of Deep Convolutional Neural Networks Architecture for Classification

of Severity Score for Atopic Dermatitis Skin Disease

ABSTRACT

The purpose of this research is to develop a system that iS capable of detecting and recognizing text contained in Indonesian ID Card (e-KTP) images accurately and efficiently. The first stage of this research involves selecting an ideal image from the e-KTP dataset. Furthermore, the pre-processing stage is carried out to cut the edges of the image and combine it with the background image to create a more varied dataset and obtain to generate an e-KTP image mask as training data. The total training data after selecting the ideal image 144 images. The U-Net architecture is the choice as the deep learning method used in this study as an image segmentation process. Meanwhile, for text detection and recognition, the Character-Region Awareness For Text detection (CRAFT) and TRBA framework (TPS-ResNet- BiLSTM-Attention) is used. The testing conducted is assessed based on the accuracy, Dice coefficient, and IoU score for the segmentation process with the percentage test results of the U-Net model obtaining an accuracy of 99.52%. Meanwhile for the text detection and recognition follows the confidence score.

PUBLICATION

• Evaluating Extraction Methods for Caffeine Content in Gayo Arabica Coffee Oil through Gas Chromatography-Mass Spectroscopy (link)
• Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach
• Evaluation of atopic dermatitis severity using artificial intelligence
• Digital Transformations in Vocational High School: A Case Study of Management Information System Implementation in Banda Aceh, Indonesia