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)

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%.