Klasifikasi Kematangan Buah Mangga Menggunakan Pendekatan Deep Learning Dengan Arsitektur DenseNet-121 dan Augmentasi Data
- Rizkiya Indah Permata Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Febi Yanto
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Elvia Budianita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Iwan Iskandar Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Fadhilah Syafria Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Mango is a seasonal fruit in Indonesia. In lowland areas and hot climates, this mango plant can grow abundantly. People who use mangoes generally focus more on the characteristics of the fruit which require a more precise classification to be more certain. Traditional classifications sometimes fail to properly articulate maturity criteria. This research classifies mango ripeness using a deep learning approach with densenet-121 architecture, parameters, learning rate, dropout, and data augmentation. Augmentation is the process of changing or modifying an image in such a way that the computer will detect that the image has been changed is the same picture. The original dataset was 895 data, after being augmented it became 1790 data consisting of three classes, namely ripe mango, young mango, and rotten mango. The test compares the original data and the original data added with augmentation. Accuracy using original data is 95.95%. Meanwhile, using original data combined with augmentation gets an accuracy of 99.73%
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References
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