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All Journal Riau Journal of Computer Science INOVTEK Polbeng - Seri Informatika Pendas : Jurnah Ilmiah Pendidikan Dasar JMM (Jurnal Masyarakat Mandiri) Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL PENDIDIKAN TAMBUSAI Journal on Education JOURNAL OF SPORT EDUCATION (JOPE) Jurnal PGSD International Journal of Informatics and Computation Jurnal Pendidikan Teknologi Informasi dan Vokasional Jurnal Prakarsa Paedagogia Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) International Journal of Engineering, Science and Information Technology JURNAL MASYARAKAT NEGERI ROKANIA Decode: Jurnal Pendidikan Teknologi Informasi JOURNAL OF ICT APLICATIONS AND SYSTEM Innovative: Journal Of Social Science Research RJOCS (Riau Journal of Computer Science)
Adyanata Lubis
Universitas Rokania, Indonesia
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Journal : international journal of informatics and computation

 1 
Detection of Oil Palm Seedling Disease Based on Leaf Images Using the MobileNetV2-CNN Architecture Ego Oktafanda; Adyanata Lubis; Elyandri Prasiwiningrum
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.71

Abstract

This study aims to develop and implement a plant disease detection system for oil palm seedlings based on leaf images using the MobileNetV2 architecture, which is based on Convolutional Neural Networks (CNN). The model was trained using a dataset of oil palm leaf images to detect several types of plant diseases. In the experiments, the applied model showed excellent results, with training accuracy increasing from 79% in the first epoch to 96% in the 15 epoch, and validation accuracy also increasing from 89% to 97%. These results demonstrate that the model can effectively detect plant diseases with good generalization ability on unseen data. With stable loss reduction and continuously improving accuracy, this study proves that the MobileNetV2 architecture can be efficiently used for plant disease detection. The research also highlights the potential integration of the model into an application to provide a practical solution in oil palm plantation management and to support decision-making and improve agricultural outcomes.
Co-Authors Acep Solihin Agung Setiawan agung setiawan Agung Setiawan AGUNG SETIAWAN Agung Setiawan Ahmad Akhyar Aini, Yulfita Akhyar, Ahmad Alexius Ulan Bani Alvin, Muhammad Amelia Chandra, Detri Andri Febriansyah Angriamilleni Angriamilleni Anik Supriani Arfandi, Zirhan Arief Hidayat Afendi, Arief Hidayat Asih Ria Ningsih Asmiati B. Herawan Hayadi Basorudin Basorudin Bayu Kusuma Bela Salsabila Budi Yanto Budi Yanto, Budi Chandra, Detri Amelia Cossy Maychandra Delima, Rika Detri Amelia Candra Detri Amelia Chandra EFENDI Ego Oktafanda Ego Oktafanda Elyandri Prasiwinigrum, Elyandri Elyandri Prasiwiningrum Erliyen Nofrianda Erna Armita, NST Fadzilatul Mutmainah Fauzi Erwis Fauzi Erwis Fifto Nugroho Firman Santosa Firman Santosa Gina Sonia Amelya Gustari, Rinda Handayani, Meli Hasrijal Hasrijal Hendrisman Hera Deswita Hommy Dorthy Ellyany Sinaga Irwan Hidayat Isdaryanto Iskandar ISKANDAR Jihan Jufri Jufri Jufri Jufri Jufri Junadhi, Junadhi Karmelia, Mila Karmi Karmi Khardianti Alviani Ishak Maulana, Sabda Miftahul Jannah Miftahul Jannah Mila Karmelia Nasution, Yuli Asnita Novica Irawati Nur Aisyah Nur Azizah Nur Azizah Nurafidah, Annisa Nurhidayati Sholihah Nurliani Pariang Sonang Siregar Prasiwiningrum, Elyandri Rani Rasna, Rasna Reski, Seri Mulia Rika Delima Rina Wati Rinda Gustari Rita Arianti Sabri, Khairul Safinaz Sahira Sahira, Safinaz Saiful Anwar Salsabila, Bela Sarjon Defit Sasnita Riyani Saudah Septi Nadia Putri Seri Mulia Reski Sri Mures Walef Sriwahyudi Sulis Wulandari Suryadi, Dikky Tofikin, Tofikin Torkis Nasution Ulfi, Meitra W Panjaitan, MM Lanny Wahyudi, Sri Wulandari, Sulis Yuda Irawan Yuhasnil Yuli Asnita Nasution