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All Journal International Journal of Electrical and Computer Engineering Tekno : Jurnal Teknologi Elektro dan Kejuruan Transmisi: Jurnal Ilmiah Teknik Elektro Mechatronics, Electrical Power, and Vehicular Technology Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Informatika dan Teknik Elektro Terapan Proceeding of the Electrical Engineering Computer Science and Informatics CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan CCIT (Creative Communication and Innovative Technology) Journal Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Jurnal E-Komtek Frontier Energy System and Power Engineering Community Development Journal: Jurnal Pengabdian Masyarakat JTEIN: Jurnal Teknik Elektro Indonesia Bulletin of Community Engagement Jurnal Teknik Elektro Jurnal Teknik Informatika dan Elektro (JURTIE) Prosiding Seminar Nasional Pengabdian Kepada Masyarakat ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat Power Elektronik : Jurnal Orang Elektro Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) KOPEMAS
Langlang Gumilar
Universitas Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
COVID-19 classification using CNN-BiLSTM based on chest X-ray images Denis Eka Cahyani; Anjar Dwi Hariadi; Faisal Farris Setyawan; Langlang Gumilar; Samsul Setumin
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4848

Abstract

Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception, and AlexNet. This research classifies CXR into three groups: COVID-19, normal, and viral pneumonia. In comparison to other models, the Resnet50-BiLSTM model is the most accurate and hence the best. The accuracy of the Resnet50-BiLSTM model was 98.48%. The model that obtains the next highest accuracy i.e Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, InceptionV3, Xception-BiLSTM, Xception, AlexNet-BiLSTM, and AlexNet. In this study, precision, recall, and F1-measure are also employed to demonstrate that Resnet50-BiLSTM achieves the highest value compared to other approaches. When compared to previous studies, this study enhances classification performance results.
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network Cahyani, Denis Eka; Hariadi, Anjar Dwi; Setyawan, Faisal Farris; Gumilar, Langlang; Setumin, Samsul
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7825

Abstract

Pneumonia is a condition characterised by the sudden inflammation of lung tissue, which is triggered by microorganisms such as fungi, viruses, and bacteria. Chest X-ray imaging (CXR) can detect pneumonia, but it requires considerable time and medical expertise. Consequently, the objective of this study is to diagnose pneumonia using CXR imaging in order to effectively detect early cases of pneumonitis in children. The study employs the ensemble transfer learning convolutional neural network (ETL-CNN) transfer learning ensemble, which combines multiple CNN transfer learning models. Resnet50-VGG19 and VGG19-Xception are the ETL-CNN models used in this investigation. Comparing ETL-CNN models to CNN transfer learning models such as Resnet50, VGG19, and Xception. Pediatric CXR pneumonia, which consists of a normal and pneumonia image, is the source of these study results. The results of this analysis indicate that Resnet50-VGG19 achieved the highest level of accuracy, 99.14%. Additionally, the Resnet50-VGG19 obtained the highest levels of precision and recall when comparing to other models. Consequently, the conclusion of this study is that the Resnet50-VGG19 model can generate acceptable classification performance for pediatric pneumonia based on CXR. This study improves classification results for performance when compared to earlier studies.
Co-Authors A.N. Afandi Abdullah Iskandar Syah Abdullah Iskandar Syah Achmad Fahrul Aji Achmad Fakhri Achmad Safi’i Achmad Syahrudin Fakhri Afandi, Arif Agil Ziddan Achmad Ahmad Dhaffa' Nibrosoma Aji Prasetya Wibawa Andriansyah, Muhammad Rizal Anik Nur Handayani Anjar Dwi Hariadi Arie Muazib Arif Afandi Aripriharta - Arum Kusuma Wardhany Asfani, Khoirudin Ayu Puwatiningsih Denis Eka Cahyani Dhiyaurrahman Fakhruddin Didik Dwi Prasetya Dita Anies Munawwaroh Dito Valentino Dityo Kreshna Argeshwara Diva Ayu Lestari Dwi Mukti Asmoro Eka Mistakim Erry Asnarindra Faisal Farris Setyawan Fakhri, Achmad Syahrudin Fakhruddin, Dhiyaurrahman Falah, Moh. Zainul Farah Wardatul Afifah Farrel Candra WA Fitri Zakiyatul Azizah Gilang Indrianto Pramono Gunawan, M. Ricko Hariadi, Anjar Dwi Ihsan, Rifqi Al Inov Ivandany Ira Kumalasari Irham Fadlika Joumil Aidil Saifuddin Junoh, Ahmad Kadri Kornelius Kamargo/Irawan Dwi Wahyono Kornelius Kamargo Kusumawardana, Arya M Rodhi Faiz M. Cahyo Bagaskoro M. Farrel Akbar Firzatullah Michiko Ryuu Sakura A Mistakim, Eka Moh Zainul Falah Moh. Zainul Falah Moh. Zainul Falah Mohamad Rodhi Faiz Monika, Dezetty Muchamad Wahyu Prasetyo Muhammad Afnan Habibi Muhammad Andriansyah Muhammad Arzu Prasetyo Muhammad As’ad Sahroni Muhammad Ihsanul Rizqi Muhammad Jazuli Shubhi Muhammad Jazuli Shubhi Muhammad Rizal Andriansyah Muhammad Sadidul Itqon Mutiar, Mutiar Muttabik Fathul Lathief Nafalski, Andrew Naizatul Zainul Rofiqi Nikmah, Revalina Nazilatun Nugraha, Agil Zaidan Nur Hidayat, Wahyu Quota Sias Rafli Amirul Husain Ridho Riski Hadi Ridzki, Imron Riya Mustikasari Rodhi Faiz Rumokoy, Steven N. Sakura A, Michiko Ryuu Samat, Ahmad Asri Abd Samsul Setumin Setumin, Samsul Setyawan, Faisal Farris Sias, Quota Alief Siti Sendari Soraya Norma Mustika Sujito - Sujito Sujito Sujito Sujito Syah, Abdullah Iskandar Syamsul Arifin Tran Huy Duy Utomo, Imam Tree WA, Farrel Candra Wahyu Tri Handoko Wahyu Tri Handoko Wicaksono, Ibram Adib Yogi Dwi Mahandi Yuni Rahmawati Yunis Sulistyorini Yunis Sulistyorini, Yunis