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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi
Samsul Setumin
Universiti Teknologi MARA
Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Systematic literature review: application of deep learning processing technique for fig fruit detection and counting Ahmad Shukri Firdhaus Kamaruzaman; Adi Izhar Che Ani; Mohammad Afiq Hamdani Mohammad Farid; Siti Juliana Abu Bakar; Mohd Ikmal Fitri Maruzuki; Samsul Setumin; Mokh. Sholihul Hadi
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4455

Abstract

Deep learning has shown much promise in target identification in recent years, and it's becoming more popular in agriculture, where fig fruit detection and counting have become important. In this study, a systematic literature review (SLR) is utilised to evaluate a deep learning algorithm for detecting and counting fig fruits. The SLR is based on the widely used 'Reporting Standards for Systematic Evidence Synthetics' (ROSES) review process. The study starts by formulating the research questions, and the proposed SLR approach is critically discussed until the data abstraction and analysis process is completed. Following that, 33 relevant research involving the agriculture sector, fruit, were selected from many studies. IEEE, Scopus, and Web of Sciences are three databases to investigate. Due to the lack of fig fruit research, fruit and vegetable studies have been included because they use similar methods and processes. The SLR found that various deep learning algorithms can count fig fruit in the field. Furthermore, as most approaches obtained acceptable results, deep learning's performance is acceptable in F1-score and average precision (AP), higher than 80%. Moreover, improvements can be produced by enhancing the existing deep learning model with the personal dataset.
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.
Co-Authors Adi Izhar Che Ani Ahmad Shukri Firdhaus Kamaruzaman Anjar Dwi Hariadi Denis Eka Cahyani Emilia Noorsal Faisal Farris Setyawan Hendy Hendy Imam Mukhlash Iza Sazanita Isa Langlang Gumilar Mohammad Afiq Hamdani Mohammad Farid Mohammad Isa Irawan Mohd Firdaus Abdullah Mohd Hanapiah Abdullah Mohd Ikmal Fitri Maruzuki Mokh Sholihul Hadi Muhammad Khusairi Osman Noor Khairiah Abdul Karim Siti Juliana Abu Bakar Siti Noraini Sulaiman Zul Imran Azhari