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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Novalina, Nadya
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Computer Science & IT Electrical & Electronics Engineering Materials Science & Nanotechnology Medicine & Pharmacology

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Implementation of Xception and EfficientNetB3 for COVID-19 Detection on Chest X-Ray Image via Transfer Learning Novalina, Nadya; Rizkinia, Mia
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.14

Abstract

COVID-19 is a highly contagious infectious disease caused by the SARS-CoV-2 virus that can cause respiratory issues. The utilization of X-ray imaging has the potential to serve as an alternative means of detecting COVID-19 by offering insights into the condition of the lungs. Rapid and automated analysis of medical images and patterns can be achieved through deep learning techniques. In this study, we propose methods for the automatic classification of COVID-19 from Chest X-Ray images using CNN with transfer learning techniques, namely Xception and EfficientNetB3 architectures, as well as an ensemble of both architectures working in parallel. Additionally, we use Grad-CAM to visualize the regions of the X-ray image that are most important for the classification decision. The classification of COVID-19 is carried out for four types of classes: COVID-19, normal, bacterial pneumonia, and viral pneumonia. The proposed classifier models achieve an overall accuracy of 94.44% for the Xception classifier, 95.28% for the EfficientNetB3 classifier, and 94.44% for the parallel classifier. The accuracy value is higher than the other comparison classifiers accuracy values. Overall, the proposed classifiers can be recommended as tools to assist radiologists and clinical practitioners in diagnosing and following up with COVID-19 cases.
Benchmarking machine learning algorithm for stunting risk prediction in Indonesia Novalina, Nadya; Aksar Tarigan, Ibrahim Amyas; Kayla Kameela, Fatimah; Rizkinia, Mia
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8997

Abstract

Stunting is a condition caused by poor nutrition that results in below-average height development, potentially leading to long-term effects such as intellectual disability, low learning abilities, and an increased risk of developing chronic diseases. One effort to reduce stunting is to apply a machine learning algorithm with a data science approach to develop risk prediction models based on factors in stunting. The study used the current cross industry standard process for data mining (CRISP-DM) framework to gain insight and analyzed 1561 records of data collected from the Indonesia family life survey (IFLS) for the prediction models. Two sampling methods, random undersampling, and oversampling synthetic minority oversampling technique (SMOTE), were employed and compared to overcome the data imbalance problem. Four machine learning classifier algorithms were trained and tested to determine the best-performing model. The experiment results showed that the algorithms yielded an average accuracy of more than 75%. Using the undersampling technique, the accuracy obtained by logistic regression, k-nearest neighbor (KNN), support vector classifier (SVC), and decision tree classifier were 95.21%, 78.91%, 92.97%, and 86.26% respectively. Meanwhile, the oversampling technique reached 96.17%, 88.50%, 93.29%, and 95.21%, respectively. Logistic regression emerges as the best classification, with oversampling yielding superior performance.
Co-Authors Aksar Tarigan, Ibrahim Amyas Kayla Kameela, Fatimah Mia Rizkinia