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All Journal Jurnal Penelitian Saintek E-Journal Of Cultural Studies Santhet: (Jurnal Sejarah, Pendidikan Dan Humaniora) Indonesian Journal of Data and Science JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Manajemen dan Teknologi Informasi Jurnal Pengabdian Masyarakat Sains dan Teknologi Jurnal Bahasa Rupa Jurnal Bahasa Rupa
Sutarwiyasa, I Ketut
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Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Indonesian Journal of Data and Science

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Performance Comparison of MobileNetV2 and NASNetMobile Architectures in Soybean Leaf Disease Classification I Gede Rian Lanang Oka; Anak Agung Gede Bagus Ariana; Wayan Sauri Peradhayana; Ni Luh Wiwik Sri Rahayu Ginantra; I Ketut Sutarwiyasa
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.243

Abstract

Soybean is one of the essential commodities in Indonesia, commonly used as a raw material for tofu and tempeh, making it highly sought after. However, soybean production has decreased by up to 30% due to disease attacks, necessitating preventive measures. This study aims to compare two Convolutional Neural Network (CNN) architectures, MobileNetV2 and NASNetMobile, in classifying soybean leaf diseases. The models were trained using a leaf image dataset collected directly from agricultural fields and categorized into five classes. The dataset underwent augmentation to increase its size, resulting in a total of 6,000 images, which were then split with an 80:10:10 ratio. The models were trained using the Adam optimizer with a learning rate of 0.001, optimized using ReduceLROnPlateau, and a dropout rate of 0.2 to prevent overfitting. Evaluation results using a confusion matrix indicated that MobileNetV2 performed better with an accuracy of 96.67%, precision of 96.70%, recall of 96.67%, and an F1-score of 96.68%, compared to NASNetMobile, which achieved an accuracy of 86.33%, precision of 86.91%, recall of 86.33%, and an F1-score of 86.40%.
Co-Authors A.A. Ngurah Anom Kumbara Aniek Suryanti Kusuma, Aniek Suryanti Ariana, Anak Agung Gede Bagus Ariputri, Ni Putu Arliana Fitriya Desak Dwi Utami Putra Desy Amelia Eddy Hartono Elly Herliyani Gede Surya Mahendra I Gede Adi Sudi Anggara I Gede Rian Lanang Oka I Gusti Agung Indrawan I Ketut Setiawan I Nyoman Agus Suarya Putra I Nyoman Darma Putra I Nyoman Tri Anindia Putra Jajang Suryana Jama, Karolus Budiman Karus Margaretha Ketut Nala Hari Wardana Mulyawan, I Putu Ivan Wiartadi Ni Kadek Ariasih, Ni Kadek NI LUH PUTU MERY MARLINDA Ni Luh Wiwik Sri Rahayu Ginantra Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Made Wiasti Ni Wayan Wardani Wardani Putra, Putu Satria Udyana Putu Gede Surya Cipta Nugraha Putu Surya Wedra Lesmana Sandika, I Kadek Budi Semuel Hajai Nitbani Thalib, Emmy Febriani Wardani, Ni Wayan Wayan Sauri Peradhayana Widiartha, Komang Kurniawan Willdahlia, Ayu Gede Wirayoga, I Putu Ade