KLASIFIKASI KONDISI TANAMAN SELADA HIDROPONIK BERDASARKAN CITRA DAUN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DENGAN ARSITEKTUR VGG16

Keysha Alea Maharani Azzahra Sampurno, . (2025) KLASIFIKASI KONDISI TANAMAN SELADA HIDROPONIK BERDASARKAN CITRA DAUN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DENGAN ARSITEKTUR VGG16. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

Lettuce (Lactuca sativa) is one of the most widely cultivated leafy vegetable commodities, especially in hydroponic farming systems, due to its short harvest period and high market demand. However, during the cultivation process, lettuce plants are susceptible to health issues that can affect both quality and yield. Therefore, a system is needed that can classify the condition of the plants as a basis for making decisions regarding the treatment of hydroponic lettuce. This study aims to design and develop a classification model for the condition of hydroponic lettuce plants using the Convolutional Neural Network (CNN) algorithm with the VGG16 architecture, which is then integrated into a web-based dashboard. The model is designed to classify plant conditions into two categories: healthy and unhealthy. During the training process, hyperparameter tuning was conducted by varying the values of the learning rate, optimizer, batch size, and number of epochs. The best model was obtained using a learning rate of 0.0001, Adam optimizer, batch size of 16, and 10 epochs, achieving an accuracy of 96% on the training data, 91% on the validation data, and 96% on the testing data, with corresponding loss values of 0.1050, 0.2330, and 0.1269.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 2110512057] [Pembimbing: Erly Krisnanik] [Penguji 1: Bambang Saras Yulistiawan] [Penguji 2: Ati Zaidiah]
Uncontrolled Keywords: Classification, Lettuce, Hydroponic, CNN, VGG16, Hyperparameter Tuning.
Subjects: Q Science > QK Botany
T Technology > T Technology (General)
Divisions: Fakultas Ilmu Komputer > Program Studi Sistem Informasi (S1)
Depositing User: KEYSHA ALEA MAHARANI AZZAHRA SAMPURNO
Date Deposited: 15 Aug 2025 08:03
Last Modified: 15 Aug 2025 08:03
URI: http://repository.upnvj.ac.id/id/eprint/37417

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