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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Sistemasi: Jurnal Sistem Informasi Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Cyberspace: Jurnal Pendidikan Teknologi Informasi Rang Teknik Journal JMM (Jurnal Masyarakat Mandiri) J I M P - Jurnal Informatika Merdeka Pasuruan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Budimas : Jurnal Pengabdian Masyarakat Community Empowerment Jurnal Teknimedia: Teknologi Informasi dan Multimedia EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi Jurnal Locus Penelitian dan Pengabdian Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Penelitian Sains Teknologi
Helmi Imaduddin
Universitas Muhammadiyah Surakarta
Religion Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Comparison of transfer learning method for COVID-19 detection using convolution neural network Helmi Imaduddin; Fiddin Yusfida Ala; Azizah Fatmawati; Brian Aditya Hermansyah
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3525

Abstract

Currently, one of the most dangerous diseases is Coronavirus disease 2019 (COVID-19). COVID-19 is a threat to the whole world, and almost all countries are experiencing the COVID-19 pandemic, including Indonesia. Various ways to detect COVID-19 sufferers have been carried out, such as swab tests, rapid tests, and antigens. One way that can be done to detect COVID-19 infection is to look at X-ray images of the patient's lungs because someone infected with COVID-19 has a different lung shape from normal people. Many studies have been carried out to detect COVID-19, using either machine learning (ML) or deep learning (DL). In this study, we propose to use transfer learning as an extraction feature in the classification of the covid dataset. The study was conducted four times using four different methods, namely ResNet 50, MobileNet V2, Inception V3, and DensNet-201. After experimenting, we compared the results to find out which method has the best results in detecting COVID-19. From this research, it was found that the ResNet 50 model has the best results with 92.3% accuracy, 93% precision, 93% F1-Score, 99% sensitivity, and 90.7% specificity.
Transfer learning for detecting COVID-19 on x-ray using deep residual network Helmi Imaduddin; Brian Aditya Hermansyah
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4334

Abstract

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has been a disaster for humanity, especially in the health sector. Covid-19 is a serious disease, a large number of people lose their lives every day. This disease not only affects one country, but the whole world suffers from this viral disease. In the fight against COVID-19 immediate and accurate screening of infected patients is essential, one of the most widely used screening approaches is chest X-Ray (CXR) which is rated faster and cheaper. This study aims to detect patients suffering from COVID-19 through chest X-Ray using a transfer learning approach, the method used is with several deep residual network architectures such as ResNet50, RexNet100, SSL ResNet50, semi-weakly supervised learning (SWSL) ResNet50, Wide ResNet50, SK ResNet34, ECA ResNet50d, Inception ResNet V2, CSP ResNet50, and ResNest50d. Then the results will be compared with previous studies. The study was conducted ten times using different pre-training and got the best results on the SWSL ResNet50 architecture with an accuracy value of 99.28%, this value increased 6.98% from previous studies, 99.51% F1-Score, 99.41% Precision, 99.61% Sensitivity, and 98.33% Specificity, that means this study obtained better results than previous studies.
A robust model for early detection of chronic kidney disease leveraging machine learning and data balancing techniques Imaduddin, Helmi; Yusuf, Siti Agrippina Alodia; Adhantoro, Muhammad Syahriandi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11247

Abstract

Chronic kidney disease (CKD) requires reliable early screening, yet clinical datasets are often highly class imbalanced, which can bias machine learning models and reduce detection quality. This study presents a unified evaluation of two imbalance mitigation strategies, synthetic minority over-sampling technique (SMOTE), and cost-sensitive learning, across six classifiers: decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). Experiments were conducted on a public CKD dataset with 1,659 records and 54 features using a consistent pipeline including preprocessing, feature selection, imbalance handling, and stratified k-fold cross-validation. Models were assessed with accuracy, precision, recall, and F1-score. Results show that the imbalance strategy materially changes model behavior: cost-sensitive learning generally improves precision, while SMOTE more often increases recall and F1-score. The best overall performance was achieved by XGBoost with cost-sensitive learning, reaching 93% accuracy and 92% precision, outperforming prior reports on the same dataset. RF remained stable across both strategies, whereas KNN was sensitive to SMOTE induced distribution shifts. These findings provide practical guidance for selecting imbalance handling methods to improve healthcare machine learning for CKD detection.
Co-Authors Afiana Azizah, Synta Nur Ahwy Oktradiksa Akhmad Liana Amrul Haq Ali Zainal Abidin Anjarwati, Jesika Anni'mah, Risyma Muti' Styandri Anton Agus Setyawan Ariansyah, Sannia Putri Astriani, Anggit Azizah Fatmawati Bintang, Yayes Kasnanda Brian Aditya Hermansyah Brian Aditya Hermansyah Dedi Gunawan Devi Afriyantari Puspa Putri Dewi Sasika Rani Diah Priyawati Dias Aziz Pramudita Dimas Aryo Anggoro Fatah Yasin Al Irsyadi Fiddin Yusfida Ala Frischa Aura Salsabilla B Hayati, Luthfiana Nur Hidayat, Helmy Purnomo Ihsan Cahyo Utomo Kirana, Vintan Muhammad Arkan Setyawan Muhammad Halim Maimun Muhammad Mutawadhi’ Alfajri Muhammad Randhy Kurniawan Muhammad Syahriandi Adhantoro Muslihah , Isnawati Nabil Aziz Bima Anggita Nani Sulistianingsih Novel Idris Abas Pranowo, Carissa Aprilia Rafsanjani, Toni Ardi Rahmawati, Riana Putri Romadhon, Anita Lusi Sakina, Alivia Rahma Sania Citra Palupi Taftazani Ghazi Pratama Widi Widayat Wijanarko, Amiladito Adhyatma Yusuf, Siti Agrippina Alodia Zaki, Muhammad Ghifari