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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JATI (Jurnal Mahasiswa Teknik Informatika)
Muhathir
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Computer Science & IT Engineering

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

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Mobilenetv2 Analysis in Classification Diseases On Mango Leaves Simangunsong, Roy Candra; Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14430

Abstract

This study aims to analyze the performance of the MobileNetV2 model in classifying diseases on mango leaves, consisting of three classes: capmodium, collectricu, and normal leaves. The dataset used contains 1500 images, with 80% allocated for training data, 10% for testing data, and 10% for validation data. The model was trained using a deep learning approach to identify mango leaf diseases based on the visual patterns present in each class. The results show that the MobileNetV2 model achieved an accuracy of 90%, a precision of 91%, a recall of 90%, and an F1-score of 89%. These findings highlight the potential of MobileNetV2 as an effective tool for automatically detecting mango leaf diseases. Therefore, this study is expected to contribute to the development of technology-based solutions in the agricultural sector, particularly in supporting farmers in identifying diseases quickly and accurately, thereby improving mango crop productivity.
Enhancing Oil Palm Leaf Disease Classification using a Pruned SqueezeNet Architecture Nugraha Rahmadan Diyanto; Muhathir; Fadlisyah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16524

Abstract

The SqueezeNet architecture is known to be effective but possesses a considerable number of parameters, which can be optimized using pruning—a compression technique that significantly reduces model parameters without sacrificing accuracy. This research aims to apply the L2-Norm based pruning method to the SqueezeNet architecture and compare its performance (accuracy and efficiency) against the default SqueezeNet model for classifying four classes of oil palm leaf diseases. The study used a primary dataset of 4,000 images, divided into training (70%), validation (20%), and testing (10%) sets. The SqueezeNet architecture was pruned using L2-Norm structured pruning with a uniform distribution at rates from 10% to 50%, followed by fine-tuning. The results show that the default SqueezeNet achieved 97.50% accuracy with 724,548 parameters. Significantly, a 10% pruning rate actually increased the accuracy to a high of 99.25% while simultaneously reducing the parameters to 579,036. Overly aggressive pruning, such as 40%, drastically decreased accuracy to 93.25%. It is concluded that the 10% pruning rate is the most optimal, proving that this method not only makes SqueezeNet lighter but also more effective. This 10% pruned model is highly suitable for application implementation due to its enhanced efficiency. Future research is recommended to validate these findings using a more diverse dataset and to test the model on actual edge devices.
Co-Authors Fadlisyah Nugraha Rahmadan Diyanto Nurul Khairina Rizki Muliono Simangunsong, Roy Candra Sri Wahyuni