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Titik Misriati
Universitas Bina Sarana Informatika
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Journal : Journal of Informatics Management and Information Technology

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Optimisasi Model Deep Learning untuk Deteksi Penyakit Daun Tebu dengan Fine-Tuning MobileNetV2 Aryanti, Riska; Agustiani, Sarifah; Wildah, Siti Khotimatul; Arifin, Yosep Tajul; Marlina, Siti; Misriati, Titik
Journal of Informatics Management and Information Technology Vol. 4 No. 4 (2024): October 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v4i4.411

Abstract

Sugarcane leaf diseases are a serious threat in sugarcane farming because they can significantly reduce productivity and can cause major losses in yields if not detected early. Therefore, fast and accurate disease management is needed to prevent further losses. This study aims to develop a deep learning model based on MobileNetV2 with fine-tuning techniques to effectively detect sugarcane leaf diseases. Fine-tuning is a method used to adjust the parameters of a pre-trained model on a more specific target dataset. The dataset contains images of sugarcane leaves that have been classified per class based on the type of disease. In this study, fine-tuning was performed on the MobileNetV2 architecture that had been previously trained using the sugarcane leaf dataset. The fine-tuning process was carried out by rearranging the top few layers of MobileNetV2 and adding a special classification layer to predict the class of sugarcane leaf diseases. The model was trained through two stages: initial training to obtain a baseline performance and fine-tuning by opening several layers of MobileNetV2. In the initial evaluation, the model achieved a validation accuracy of 93.12%. After fine-tuning, the accuracy increased to 95.01%, indicating that this technique was able to significantly improve disease detection capabilities. The results of this study provide important contributions in the field of agriculture, especially in supporting the sustainability of sugarcane production through artificial intelligence-based technology. The implementation of the proposed model is expected to help farmers detect diseases more quickly and take timely preventive measures, thereby reducing losses.
Co-Authors Agustiani, Sarifah Aldian Mauluda Amanda Subhi Pertiwi Arie Kurniawan Arifin, Yosep Tajul Asriyani Sagiyanto ASRIYANI SAGIYANTO, ASRIYANI Eka Dyah Setyaningsih Fachri, Muhamad Handayani Meilisa Hari Sugiarto - UBSI Haryani Ida Zuniarti Ida Zuniarti, Ida Instianti Elyana Instianti Elyana Istiqomah, Amelia Maria, Vivi Mohammad Afdhol Rizal Muhammad Arif Sulistiyo Nadhifah, Rifda Nadhifah, Rifda Norma Yunita Nurvi Oktiani Oktaviyani Oktaviyani Rahayu Ningsih Rahayu Ningsih Rahmat Hidayat Rahmat Hidayat Rahmat Hidayat Rahmawati Rahmawati Ramadhani, Arya Rina Martenia Riska Aryanti SALMAN ALFARIZI Sari Hartini, Sari Siti Khotimatul Wildah Siti Marlina Siti Marlina, Siti Siti Nurajizah, Siti Sri Ariyati Wahyudin Wahyudin Widiarina . Widiarina, Widiarina Wina Yusnaeni Yoseph Tajul Arifin