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Prayudani, Santi
Politeknik Negeri Medan
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Optimization of Convolutional Neural Network for Classification of Hydroponic Vegetable Cultivation Using Machine Learning Lubis, Arif Ridho; Prayudani, Santi; Putra, Purwa Hasan; Lase, Yuyun Yusnida
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.7231

Abstract

In an effort to apply applied product innovation and support the improvement of hydroponic vegetable cultivation, it is based on several things. Among them are changes in the texture of the year, stems and vegetable quality. At this time the problems faced by hydroponic vegetable pickers, especially banyumas village youth organizations who have UMKM hydroponic vegetable cultivation. This situation will have an impact on problems and losses that result in a lack of yield and quality of harvested vegetables if not resolved quickly. The results of this study resulted in optimal accuracy performance in the classification of hydroponic vegetables with CNN, this study also successfully classified normal vegetables with vegetables affected by disease. This research produces accuracy in the first test 73% and the second test 92%.
Analisis Deteksi Penyakit Daun Pisang Menggunakan Ekstraksi Fitur CNN (MobileNetV2) dan Klasifikasi SVM Yuyun Yusnida Lase; Lampson Pindahaman Purba; Santi Prayudani; Arif Ridho Lubis; Hikmah Adwin Adam
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.6590

Abstract

Banana plants (Musa spp.) are one of the leading horticultural commodities in Indonesia that have high economic value and play an important role in national food security. However, banana productivity often decreases due to attacks by various diseases such as Sigatoka, Cordana, and Pestalotiopsis infections that can spread quickly. Early detection of these diseases is crucial to prevent greater losses. This study aims to develop a banana plant disease detection system based on digital image processing with the Support Vector Machine (SVM) algorithm. The research method includes the stages of banana leaf image acquisition, pre-processing using color segmentation, color and texture feature extraction, and disease type classification with the SVM algorithm. The test results show that the developed system is able to recognize banana leaf diseases with an accuracy of 97.8%, precision of 97%, and recall of 98%. These findings prove that the application of digital image processing and the SVM algorithm is effective in detecting banana plant diseases. This system is expected to be a fast, efficient, and accurate diagnostic tool for farmers to increase the productivity and quality of banana harvests.
Co-Authors Adam, Hikmah Adwin Adha, Lilis Tiara Ahmad Hidayat Ainul Hizriadi Ainul Hizriadi Al Khowarizmi Andam Lukcyhasnita Ariyani, Tika F. Friendly Fatmi, Yulia Friendly Friendly, Friendly Gabriel Ardi Hutagalung Ginting, Heri Sanjaya Harfita, Nur Laily Harizahahyu Harizahayu, Harizahayu Harris Aminuddin Haryadi Haryadi - Hidayah, Rizki Hikmah Adwin Adam Husna, Meryatul Ivan Jaya Jaya, Ivan Julham Julham Julham Julham Kadri Yusuf Lampson Pindahaman Purba Lubis, Arif Ridho Muhammad Fathoni Muhammad Luthfi Hamzah Muhammadiyah, Mas’ud Muharman Lubis Munthe, Joy Lousia Brigitha Na, Li Nishida, Daiki Nugroho, Muhammad Rivan Putra, Purwa Hasan Putri, Desfita Eka Radea Shiddiq Salam, Azrizal Sembiring, Zakaria Shiddiq, Radea Sibarani, Yous Sinaga, Kristin Sinuraya, Junus Situmorang, Dita Rouli Basa Suadi Zainal Syafli, Sekar Arini Syamsul Arifin Tazkiyatun Nisa Tumanggor, Orli Wiwin Sry Adinda Banjarnahor Xiang, Yang Yulia Fatmi Yusnida Lase, Yuyun Yusuf, Kadri Yuyun Yusnida Lase Zakaria Sembiring Zakaria Sembiring