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All Journal JAM Elektron Jurnal Ilmiah JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Research in Counseling and Education JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Andalas Journal of Electrical and Electronic Engineering Technology Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat
Surfa Yondri
Jurusan Teknik Elektro Politeknik Negeri Padang
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : JOIV : International Journal on Informatics Visualization

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Artificial Neural Network Application for Aroma Monitoring on The Coffe Beans Blending Process Susanti Roza; Zas Ressy Aidha; Milda Yuliza; - Suryadi; Surfa Yondri
JOIV : International Journal on Informatics Visualization Vol 2, No 3 (2018)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (761.278 KB) | DOI: 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.
Co-Authors - Suryadi A fadly Ahmad Bahri Joni Malyan Ambiyar, Ambiyar Asmar Yulastri Dedi Kurniadi Efendi, Raimon Ganefri . Hendra Hidayat Hengki Purnama Sari Junaldi Junaldi Milda Yuliza Randy Heriyanto Revalin Herdianto Rina Pratiwi Pudja I. A Roni Putra Roza Susanti Sir Anderson Tri Oktaviandra Waskito Witrionanda Witrionanda Yuyun Irawati Zas Ressy Aidha