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All Journal Indonesian Journal of Electronics and Instrumentation Systems Jurnal Ilmu Komputer JOIN (Jurnal Online Informatika) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab ILKOM Jurnal Ilmiah Journal of Applied Data Sciences
Dyah Aruming Tyas
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science

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CNN Ensemble Learning Method for Transfer learning: A Review Sulistya, Yudha Islami; Br Bangun, Elsi Titasari; Tyas, Dyah Aruming
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1541.45-63

Abstract

This  study provides a review of CNN's ensemble learning method for transfer learning by highlighting sections such as review studies, datasets, pre-trained models, transfer learning, ensemble learning, and performance. The results indicate that the trend of ensemble learning, transfer learning ensemble, and transfer learning is growing every year. In 2022, there will be 35 papers reviewed related to this topic in this study. Some datasets contain apparent information starting from the dataset name, total data points, dataset splitting, target dataset availability, and type classification. ResNet-50, VGG-16, InceptionV3, and VGG-19 are used in most papers as pre-trained models and transfer learning processes. 50 (90.1%) papers use ensemble learning, and 5 (9.1%) do without ensemble learning. The reviewed paper summarizes several performance measurements, including accuracy, precision, recall, f1-score, sensitivity, specificity, training accuracy, validation accuracy, test accuracy, training losses, validation losses, test losses, training time, and AUC, DSC. In the last section, 49 papers produce the best model performance using the proposed model, and 6 other papers use DenseNet, DeQueezeNet, Extended Yager Model, InceptionV3, and ResNet-152.
Co-Authors Agus Harjoko Ajitomo, Wahyu Ardin, Auriel Azril Br Bangun, Elsi Titasari Fadil, Nurdana Ahmad Fatrawijaya, Imam Hardjita, Pahlevi Wahyu Latifah, Husnul Maulana, Ahmad Rizki Mustaqim, Adi Zaenul Putra, Fathiyarizq Mahendra Putra, Philipus Febriano Kurnia Raden Sumiharto Suprapto Suprapto Tri Ratnaningsih Yudha Islami Sulistya