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Siti Marlina, Siti
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Journal : Journal of Informatics Management and Information Technology

 1 
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 Adawiyah, Robi’atul Adriadi, Andreas Septian Afrianti, Lisa Agus Mokodompit, Eliyanti Agustiani, Sarifah Aini, Lisa Nur Alidin, La Ode Asfahyadin Alifah , Wa Ode Najwa Almaida, Nisa Alwi, Fahrizal Ammanullah, Khumair Anggraeni , Sartika Putri Ardhiansyah, Maulana Ariani, Wa Ode Gusni Arianto, Adi Ariany, Lisda Arifin, Yosep Tajul Asrul, Muh Zaki Alfiansyah Aulia K , Rahma Auliany Hasan, Reski Azzahra , Zhafira Syarlianti Azzahro, Fatimah Dedy Takdir Syaifuddin Delisa, Apifah Dian Mustika Elvira Zahara, Anzu Ernawati Ernawati Fajar Agung Nugroho Fitri Julianti Fuad Rahman Ginting, Monika Nina Kurniawaty Guswendra, Rahmat Handayani, Rize Hariati Hariati Harigustian, Yayang Haris Mubarak Harly, Agustian Hartati Bahar Haryani Hasian, Muhammad Tsawaby Herawati, Augustin Rina Kismartini Kismartini Kristina, Gina Latif , Abdul Madyan Mandasari , Titin Mardaleni, Mardaleni Maria Hermita Manik Maryani Maryani Maulana, Andry Montundu, Yusuf Muhammad Faisal Mumtaz, Mumtaz Niasari, Fitri Nirwana, Putri Mira Numan Musyaffa Pasambo, Yourisna Powatu , Virji Andini Tri Ramadhani Pratama Mandala Putra, Ryan Putra, Komang Ardidhana Nugraha Putri, Anggri Putri, Diah Ayu Radhitya, Made Leo Rahayu, Karlina Rahmat Hidayat Rifansyah, Muhammad Riska Aryanti Rosaulina, Meta Sani, Nur Insan Silalahi, Rini Debora Siti Khotimatul Wildah Supriyatna Syahri, Alfi Syarif Hidayatulloh Tantri, Siti Tiara Tarigan, Beti Susanti Titik Misriati Umar Yusuf Ummu Radiyah, Ummu WITRIANA ENDAH PANGESTI Yoseph Tajul Arifin Yuliatin yuliatin Yunita, Norma Yusni Arni Zuliawati, Zuliawati