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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika Briliant: Jurnal Riset dan Konseptual JOURNAL OF APPLIED INFORMATICS AND COMPUTING JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Vokasi Informatika (JAVIT) J-Intech (Journal of Information and Technology) Jurnal Teknologi Informasi Cyberku Journal of Computing Theories and Applications Jurnal Informatika Polinema (JIP) Journal of Information System and Application Development Journal of Future Artificial Intelligence and Technologies
Muslikh, Ahmad Rofiqul
Faculty Of Information Technology, Universitas Merdeka, Malang, East Java, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Journal : Journal of Future Artificial Intelligence and Technologies

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Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification Firnando, Fadel Muhamad; Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-4

Abstract

This research aims to evaluate and compare the performance of several deep learning architectures, especially InceptionV3 and InceptionResNetV2, with other models, such as EfficientNetB3, ResNet50, and VGG19, in classifying rice leaf diseases. In addition, this research also evaluates the impact of using data augmentation on model performance. Three different datasets were used in this experiment, varying the number of images and class distribution. The results show that InceptionV3 and InceptionResNetV2 consistently perform excellently and accurately on most datasets. Data augmentation has varying effects, providing slight advantages on datasets with lower variation. The findings from this research are that the InceptionV3 model is the best model for classifying rice diseases based on leaf images. The InceptionV3 model produces accuracies of 99.53, 58.94, and 90.00 for datasets 1, 2, and 3, respectively. It is also necessary to be wise in carrying out data augmentation by considering the dataset's characteristics to ensure the resulting model can generalize well.
Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition Setiadi, De Rosal Ignatius Moses; Nugroho, Kristiawan; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-11

Abstract

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.
Co-Authors Achmad Nuruddin Safriandono Akbar, Ismail Amelisa, Eka Puspita Roro Arif Saivul Affandi De Rosal Ignatius Moses Setiadi Delviana Muti Dian Kristiawan Nugroho Edi Subiyantoro Elan, Melania Seindang Fachrudin, Mochamad Rizal Fandi Yulian Pamuji Firnando, Fadel Muhamad Heru Agus Santoso Imanulloh, Satrio Bagus Islam, Hussain Md Mehedul Jehadu, Yustina Vania Ghaisani Jutono Gondohanindijo, Jutono Khamdani, Ahmad Rizal Luh Putu Ratna Sundari Marcus, Ronald David Mardiana Andarwati Mustofa, Fachrul Ojugo, Arnold Adimabua Pradana, Akbar Ganang Pravisya, Raihan Ihza Rizza Muhammad Arief Swalaganata, Galandaru Syahroni Wahyu Iriananda, Syahroni Wahyu Warto - Wijaya, Nantalira Niar Yulian Pamuji, Fandi