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All Journal Jurnal Ilmu Komputer dan Informasi Computer Science and Information Technologies
Alfin, Muhammad Reza
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Computer Science & IT Engineering

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Transfer learning: classifying balanced and imbalanced fungus images using inceptionV3 Supriyadi, Muhamad Rodhi; Alfin, Muhammad Reza; Karisma, Aulia Haritsuddin; Maulana, Bayu Rizky; Pinem, Josua Geovani
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p112-121

Abstract

Identifying the genus of fungi is known to facilitate the discovery of new medicinal compounds. Currently, the isolation and identification process is predominantly conducted in the laboratory using molecular samples. However, mastering this process requires specific skills, making it a challenging task. Apart from that, the rapid and highly accurate identification of fungus microbes remains a persistent challenge. Here, we employ a deep learning technique to classify fungus images for both balanced and imbalanced datasets. This research used transfer learning to classify fungus from the genera Aspergillus, Cladosporium, and Fusarium using InceptionV3 model. Two experiments were run using the balanced dataset and the imbalanced dataset, respectively. Thorough experiments were conducted and model effectiveness was evaluated with standard metrics such as accuracy, precision, recall, and F1 score. Using the trendline of deviation knew the optimum result of the epoch in each experimental model. The evaluation results show that both experiments have good accuracy, precision, recall, and F1 score. A range of epochs in the accuracy and loss trendline curve can be found through the experiment with the balanced, even though the imbalanced dataset experiment could not. However, the validation results are still quite accurate even close to the balanced dataset accuracy.
Classification of Clove Leaf Blister Blight Disease Severity Using Pre-trained Model VGG16, InceptionV3, and ResNet Pramesti, Putri Ayu; Supriyadi, Muhamad Rodhi; Alfin, Muhammad Reza; Noveriza, Rita; Wahyuno, Dono; Manohara, Dyah; Melati; Miftakhurohmah; Warman, Riki; Hardiyanti, Siti; Asnawi
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i2.1237

Abstract

Clove is one of the precious plants produced in Indonesia. Clove has many benefits for humans, but clove cultivation often experiences problems due to disease attacks, including Leaf Blister Blight Disease(CDC). The handling of CDC disease is carried out based on the severity of the symptoms that can be seen on the affected leaves. This research was conducted to obtain a CDC disease classification model, so appropriate treatment can be carried out. This study used the pre-trained VGG16, InceptionV3, and ResNet models for classification. VGG16 got the highest average accuracy of 96.7%. Aside from that, k-fold cross validation improved the model's accuracy.
Co-Authors ASNAWI Dono Wahyuno Dyah Manohara Karisma, Aulia Haritsuddin Maulana, Bayu Rizky Melati Miftakhurohmah Pinem, Josua Geovani Putri Ayu Pramesti, Putri Ayu Rita Noveriza Siti Hardiyanti Supriyadi, Muhamad Rodhi Warman, Riki