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All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Berliana Amelia
Institut Teknologi Telkom Surabaya
Computer Science & IT Education Engineering

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Leaf Health Identification on Melon Plants Using Convolutional Neural Network Farah Zakiyah Rahmanti; Bernadus Anggo Seno Aji; Oktavia Ayu Permata; Berliana Amelia; M. Hamim Zajuli Al Faroby
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 1 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i1.58492

Abstract

Plants require complete nutrients to grow well and produce good-quality products. Some examples of symptoms in plants that lack nutrients such as wrinkled leaves and slow ripening of fruit, so plants are less productive. Plants that lack nutrients are unhealthy plants. This research aims to identify healthy and unhealthy leaves on melon plants so that immediate action can be taken to deal with them. This research will be useful for melon farmers everywhere. The dataset used is data taken directly using a digital camera with the help of melon farmers to label each data, both healthy and unhealthy leaves. This research has two main works, they are the training process and the testing process. The proposed research uses the Convolutional Neural Network (CNN) method with 10 epochs. The test results on the 20-test data achieve 100% accuracy. We used accuracy, precision, recall, and f1-score to evaluate the classification method.
Co-Authors Bernadus Anggo Seno AjiAji Farah Zakiyah Rahmanti Mohammad Hamim Zajuli Al Faroby Oktavia Ayu Permata