How to cite (IEEE): R. Susanti, Z. Zaini, A. Hidayat, N. Alfitri, & M. Rusydi "Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network," JOIV : International Journal on Informatics Visualization, vol. 7, no. 3, , pp. 659-664, Sep. 2023. https://doi.org/10.30630/joiv.7.3.1375

How to cite (APA): Susanti, R., Zaini, Z., Hidayat, A., Alfitri, N., & Rusydi, M. (2023). Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network. JOIV : International Journal on Informatics Visualization, 7(3), 659-664. https://doi.org/10.30630/joiv.7.3.1375

How to cite (Chicago): Susanti, Roza, Zaini, Zaini, Hidayat, Anton, Alfitri, Nadia, AND Rusydi, Muhammad Ilhamdi. "Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network" JOIV : International Journal on Informatics Visualization [Online], Volume 7 Number 3 (10 September 2023) https://doi.org/10.30630/joiv.7.3.1375

How to cite (Vancouver): Susanti R, Zaini Z, Hidayat A, Alfitri N, & Rusydi M . Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network. JOIV : International Journal on Informatics Visualization [Online]. 2023 Sep; 7(3):659-664. https://doi.org/10.30630/joiv.7.3.1375

How to cite (Harvard): Susanti, R., Zaini, Z., Hidayat, A., Alfitri, N., & Rusydi, M. ,2023. Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network. JOIV : International Journal on Informatics Visualization, [Online] 7(3), pp. 659-664. https://doi.org/10.30630/joiv.7.3.1375

How to cite (MLA8): Susanti, Roza, Zaini Zaini, Anton Hidayat, Nadia Alfitri, & Muhammad Ilhamdi Rusydi. "Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network." JOIV : International Journal on Informatics Visualization [Online], 7.3 (2023): 659-664. Web. 5 Dec. 2023 , https://doi.org/10.30630/joiv.7.3.1375

BibTex Citation Data :

@article{JOIV1375,
    	author = {Roza Susanti and Zaini Zaini and Anton Hidayat and Nadia Alfitri and Muhammad Ilhamdi Rusydi},
    	title = {Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network},
    	journal = {JOIV : International Journal on Informatics Visualization},
    	volume = {7},
    	number = {3},
    	year = {2023},
    	keywords = {Coffee Ground; Aroma; Electronic Tasting; Gas Sensor;  Neural Network},
    	abstract = {Coffee is one of the famous plants’ commodities in the world. There are some coffee powders such as Arabica dan Robusta. This study aimed to identify two various coffee powders, Arabica and Robusta based on the blended aroma profiles, employing the backpropagation Artificial Neural Network (ANN). Four taste sensors were employed, namely TGS 2602, 2610, 2611, and 2620, to capture the diverse coffee aroma. These detectors were combined with the aroma sensors having transducers integrated with signal amplifiers or processors, which featured a load of 10 KΩ resistance. Three aroma types were investigated, namely Arabica coffee, Robusta coffee, and without coffee beans. The neural network architecture consisted of four inputs from all sensors, with one hidden layer housing eight neurons. Two neuron outputs were employed for classification, with 70 samples used for training ANN for each type. During the training phase, the developed neural network showed an impressive accuracy rate of 91.90%. TGS 2602 and 2611 sensors showed the most significant differences among the three aroma types. When analyzing ground Robusta coffee, TGS 2602 and 2611 sensors recorded 2.967 volts and 1.263 volts, with a gas concentration of 17.92 ppm and 2441.8 ppm. Similarly, the sensors for ground Arabica coffee displayed 3.384 volts and 1.582 volts with a gas concentration of 20.445 ppm and 3058.5 ppm in both TGS 2602 and 2611, respectively. The implemented ANN with aroma sensor as input successfully identify the coffee powders.},
        	issn = {2549-9904},    	pages = {659--664},    	doi = {10.30630/joiv.7.3.1375},
        	url = {http://joiv.org/index.php/joiv/article/view/1375}
    }
    

Refworks Citation Data :

@article{{JOIV}{1375}, author = {Susanti, R., Zaini, Z., Hidayat, A., Alfitri, N., Rusydi, M.}, title = {Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {7}, number = {3}, year = {2023}, doi = {10.30630/joiv.7.3.1375}, url = {} }