Development of a Mobile-Based Application for Classifying Caladium Plants Using the CNN Algorithm

Rudy Chandra; Tegar Arifin Prasetyo; Heni Ernita Lumbangaol; Veny Siahaan; Johan Immanuel Sianipar

doi:10.15575/join.v9i1.1296

Authors

Rudy Chandra Information Technology, Faculty of Vocational Studies, Institut Teknologi Del, Indonesia https://orcid.org/0000-0002-2267-6365
Tegar Arifin Prasetyo Information Technology, Faculty of Vocational Studies, Institut Teknologi Del, Indonesia https://orcid.org/0000-0001-8058-7961
Heni Ernita Lumbangaol Information Technology, Faculty of Vocational Studies, Institut Teknologi Del, Indonesia
Veny Siahaan Information Technology, Faculty of Vocational Studies, Institut Teknologi Del, Indonesia
Johan Immanuel Sianipar Information Technology, Faculty of Vocational Studies, Institut Teknologi Del, Indonesia

DOI:

https://doi.org/10.15575/join.v9i1.1296

Keywords:

Calladium, Classification, CNN, Confusion Matrix, Deep Learning

Abstract

Caladium is a popular ornamental plant and has business potential. However, difficulties in recognizing the type of Caladium often occur because of the similarities in shape, pattern, and color of the leaves between the different kinds of Caladium. To overcome this problem, research will use machine learning with the Convolutional Neural Network (CNN) algorithm to build a mobile application that can accurately classify four types of Caladiums. The data set used is 1200 data with four classes; each class has 300 data. The best model is found with the parameter epoch 100, learning rate 0.001, and batch size 64. The model is then implemented in a mobile application with two menus, "Take a photo" and "Choose an image," which will display the classification output and confidence values of the four types of Caladiums. Testing with 30 test data per class achieves 0.975 accuracy on both menus. On the “Take a photo” menu, precision is 0.974, recall is 0.9725, and f1-score is 0.965. Meanwhile, on the “Choose an image” menu a precision and recall value is 0.975, and f1-score value of 0.97.

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