SATRIO, TIO DWI (2025) COFFEE BEAN TYPE RECOGNITION SYSTEM THROUGH GREEN BEAN IMAGES FOR ANDROID AND WEB PLATFORMS. Tugas Akhir thesis, Informatics.
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Abstract
This research aims to develop an application capable of classifying coffee beans into Arabica, Robusta, and Excelsa types using the MobileNet model and Convolutional Neural Network (CNN) algorithm. CNN was chosen for its ability to extract visual features from images with high accuracy. The study utilizes hundreds of coffee bean images categorized by type and applies data augmentation techniques to enrich dataset diversity and reduce overfitting. The results indicate that MobileNet can be effectively implemented for coffee bean classification on both Android and web platforms. This application is expected to benefit the coffee industry significantly by enabling fast and accurate identification of coffee bean types, thereby assisting farmers, traders, and roasters in the coffee processing workflow. Furthermore, this research contributes to the broader application of machine learning—particularly deep learning—in the agricultural sector. Consequently, AI-based classification systems have the potential to improve the quality and productivity of the coffee industry as a whole. Keywords: Application, Coffee, Website, CNN, MobileNet, Deep Learning.
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 16 Jul 2025 09:02 |
| Last Modified: | 16 Jul 2025 09:02 |
| URI: | http://eprints.uty.ac.id/id/eprint/18183 |
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