Classification of types Roasted Coffee Beans using Convolutional Neural Network Method


  • Halifa Sekar Metha Universitas Amikom Yogyakarta
  • Kusrini Universitas Amikom Yogyakarta
  • Dhani Ariatmanto Universitas AMIKOM Yogyakarta, Indonesia




In the current digital era, the role of technology in the agricultural industry is very necessary to increase yields which can have an impact on the productivity and welfare of farmers. Coffee is a drink that has been very popular for many years. Due to the high demand for coffee beans, this research aims to develop a system that can classify types of roasted coffee beans based on images using the Convolution Neural Network (CNN) method. Coffee bean processing is the most important stage in the coffee industry, classifying coffee beans often requires more in-depth knowledge and extensive experience regarding coffee beans. Therefore, this system can be a more effective solution. The author collects a dataset containing types of roasted coffee beans, then the Convolutional Neural Network  (CNN) can analyze in the form of visual patterns each type of coffee bean. This implementation is expected to help the coffee industry identify coffee beans quickly and accurately.

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Assuncao, E., Catarina D., Pedro D. G. and Hugo P. 2020. Decision-making support System for Fruit Diseases Classification Using Deep Learning. In Proceedings of the 2020 International Conference on Decision Aid Sciences and Application. 652-656.

Bhumiratana, N.; Adhikari, K.; Chambers, E. Evolution of sensory aroma attributes from coffee beans to brewed coffee. LWT Food Sci. Technol. 2011, 44, 2185–2192. (CrossRef).

Derwing, T. M., Rossiter, M. J., & Munro, M. J. (2002). Teaching native speakers to listen to foreign-accented speech. Journal of Multilingual and Multicultural Development, 23(4), 245-259.

F. R. Moeis, T. Dartanto, J. P. Moeis, and M. Ikhsan, "A longitudinal study of agriculture households in Indonesia: The effect of land and labor mobility on welfare and poverty dynamics," World Dev Perspect, vol. 20, Dec. 2020, doi: 10.1016/j.wdp.2020.100261.

FAO Statistics 2019 Food and Agriculture Data

Giacalone, D.; Degn, T.K.; Yang, N.; Liu, C.; Fisk, I.; Münchow, M. Common roasting defects in coffee: Aroma composition, sensory characterization and consumer perception. Food Qual. Prefer. 2019, 71, 463–474. (CrossRef).

Hamid, Y., Sharyar W., Arjumand B. S., and Ali A. A. 2022. Smart Seed Classification System Based on MobileNetV2 Architecture. Proceedings of the 2022 2nd International Conference on Computing and Information Technology (ICCIT). 217-222.

Krech Thomas, H. (2004). Training strategies for improving listeners' comprehension of foreign-accented speech (Doctoral dissertation). University of Colorado, Boulder.

Maitra, I., & Subramaniyam, V. P. (2021). Coffee Bean Classification Using Deep Convolutional Neural Networks. In 2021 IEEE Calcutta Conference (CALCON) (pp. 1-6). IEEE.

Nasir, I. M., Asima B., Jamal H. S., M. Attique K., M. Sharif, Khalid I., Y. Nam, and S. Kadry. 2020. Deep Learning-Based Classification of Fruit Diseases: An Application for Precision Agriculture. Tech Science Press. 66(2). 1949-1962.

Sandler, M., Andrew H., Menglong Z., Andrey Z., and Liang-Chieh C. 2018. MobileNetV2: Inverted Residuals and Linear Bottlenecks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4510-4520.

Y. Hamid, S. Elyassami, Y. Gulzar, V. R. Balasaraswathi, T. Habuza, and S. Wani. 2022. Improvised CNN Model for Fake Image Detection. International Journal of Information Technology. 15. 5-15.


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How to Cite

Metha, H. S. ., Kusrini, K., & Ariatmanto, D. (2024). Classification of types Roasted Coffee Beans using Convolutional Neural Network Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 846-851.

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