Development of Web Application for Classification of Variegated Banana with Machine Learning
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Abstract
This research aims to develop a web application to classify variegated bananas with machine learning to introduce deep learning machine learning techniques with convolutional neuron network algorithms and to develop a classification and association model of variegated bananas. The objectives were to 1) find a machine learning model to apply to the classification of variegated banana species, 2) develop web applications for the classification of variegated bananas with machine learning, and. 3) find the efficiency in the accuracy of the learning model. Data were collected from the database of 5 species of variegated banana leaves from the Botanical Garden Organization, namely 1) Florida variegated bananas, 2) Nam Wah variegated bananas, 3) Tanee variegated bananas, 4) Thepphanom variegated bananas and 5) Red Indo variegated bananas. The datasets were divided into 70 percent teaching and 30 percent test data. Then use a deep learning method using convolutional neuron network algorithms to process the image, create the model, and develop web applications with Python. The research found that 1) the Model of Machine Learning was applied to the classification of variegated banana species and 2) the efficiency in the accuracy of the learning model was measured by the F1 method at a maximum score accuracy of 83.00 percent.
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