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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Karinov Jurnal Kajian Matematika dan Aplikasinya Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi)
Denis Eka Cahyani
Universitas Negeri Malang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Performance comparison of TF-IDF and Word2Vec models for emotion text classification Denis Eka Cahyani; Irene Patasik
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3157

Abstract

Emotion is the human feeling when communicating with other humans or reaction to everyday events. Emotion classification is needed to recognize human emotions from text. This study compare the performance of the TF-IDF and Word2Vec models to represent features in the emotional text classification. We use the support vector machine (SVM) and Multinomial Naïve Bayes (MNB) methods for classification of emotional text on commuter line and transjakarta tweet data. The emotion classification in this study has two steps. The first step classifies data that contain emotion or no emotion. The second step classifies data that contain emotions into five types of emotions i.e. happy, angry, sad, scared, and surprised. This study used three scenarios, namely SVM with TF-IDF, SVM with Word2Vec, and MNB with TF-IDF. The SVM with TF-IDF method generate the highest accuracy compared to other methods in the first dan second steps classification, then followed by the MNB with TF-IDF, and the last is SVM with Word2Vec. Then, the evaluation using precision, recall, and F1-measure results that the SVM with TF-IDF provides the best overall method. This study shows TF-IDF modeling has better performance than Word2Vec modeling and this study improves classification performance results compared to previous studies.
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 Abu Bakar, Siti Juliana Abu Talib, Muhammad Abbas Achmad Safi’i Afrizal Doewes Aji Prasetya Wibawa Anjar Dwi Hariadi Bambang Harjito, Bambang Che Ani, Adi Izhar Desi Rahmadani Didik Dwi Prasetya Dito Valentino Dityo Kreshna Argeshwara Faisal Farris Setyawan Hariadi, Anjar Dwi Ira Kumalasari Irene Patasik Ito Wasito Junoh, Ahmad Kadri Langlang Gumilar Lucky Tri O M. Farrel Akbar Firzatullah Mahmuddin Yunus Nafalski, Andrew Samsul Setumin Sari Widya Sihwi, Sari Widya Setumin, Samsul Setyawan, Faisal Farris Triyoga, Khavid Wasi Winda Mustikaningtyas