Artificial Neural Network Application for Aroma Monitoring on The Coffe Beans Blending Process

Susanti Roza - Politeknik Negeri Padang, Indonesia
Zas Aidha - Politeknik Negeri Padang, Indonesia
Milda Yuliza - Politeknik Negeri Padang, Indonesia
- Suryadi - Politeknik Negeri Padang, Indonesia
Surfa Yondri - Politeknik Negeri Padang, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.2.3.86

Abstract


This study aims to identify the type of coffee powder aroma from the coffee beans blending using backpropagation artificial neural network (ANN). Backpropagation is a controlled training implementing a weight adjustment pattern to achieve a minimum error value between the the predicted and the actual output. Within this study, the coffee aroma testing utilized electronic tasting sensor system consisted of 4 sensors namely TGS 2611, TGS 2620, TGS 2610 and TGS 2602. The coffee aroma monitoring and data collection in this system applied LabVIEW software as a virtual instrumentation. The testing result of this ANN was able to distinguish the coffee variety of Robusta, Arabica coffee powder and the one without any coffee aroma. The backpropagation architecture was formed by 3 layers consisting of 1 input layer with 4 input nerve cells, 1 hidden layer with 8 neural cells, and 2 output layers by applying the backpropagation training algorithm. The training data was taken from 70 data samples of each circumstance of coffee with 5 testing times. The results of the training and testing showed that the established backpropagation was capable to identify and differenciate the coffee powder in accordance with the given input with different average success rate;  91.96% for Robusta coffee, 100 % for Arabica coffee, and no 84.24% for without coffee aroma.


Keywords


Coffee powder; Aroma; Electronic Tasting; Gas Sensors; LabVIEW; Backpropagation.

Full Text:

PDF

References


Indonesia Investments. 2015. Kopi, (Online), (http://www.indonesia investments.com/id/bisnis/komoditas/kopi/item186, diakses 5 November 2016)

International Coffee Organization. 2017. Total production by exporting countries, (Online), (http://www.ico.org/trade_statistics.asp?section= Statistics, diakses 3 April 2017)

Fitra, Neni Olya. 2016. identifikasi aroma bubuk kopi menggunakan jaringan syaraf tiruan berbasis backpropagation, (Online), (https://id.scribd.com/ document/328221232/Bab-I-pendahuluan, diakses 5 November 2016)

Departement Pendidikan dan Kebudayaan. 1988. Kamus Besar Bahasa Indonesia. Jakarta: Balai Pustaka

Standar Perindustrian Indonesia. 1972. Standar Mutu Kopi Bubuk. Badan Standar Nasional, Jakarta

Najiyati, S., dan Danarti. 1997. Budidaya Kopi dan Pengolahan Pasca Panen. Penebar Swadaya, Jakarta

Hendrick, dkk. 2012. Karakterisasi Aroma Kopi Menggunakan Short Time Fourier Transform, (Online), (https://www.academia.edu/10358389/

Siang, Jong Jek. 2005. Jaringan Syaraf Tiruan dan Pemrogramannya Menggunakan Matlab, Yogyakarta: Andi Offset