Design of a Coffee Blending Device with Carbon Monoxide Levels (Co) Based on Backpropagation Artificial Neural Network 2 Hidden Layers

Roza Susanti; Rieke Syochrani Zaef; Muhammad  Ilhamdi Rusydi; Zaini; Annisa Rahma Asy Syifa.k

doi:10.59890/ijatss.v2i12.96

Authors

Roza Susanti Padang State Polytechnic
Rieke Syochrani Zaef Sepuluh Nopember Institute of Technology
Muhammad Ilhamdi Rusydi Andalas University
Zaini Andalas University
Annisa Rahma Asy Syifa.k Padang State Polytechnic

DOI:

https://doi.org/10.59890/ijatss.v2i12.96

Keywords:

Robusta Coffee, Arabica Coffee, Liberica Coffee, E-Nose Sensor, Carbon Dioxide (CO2), Carbon Monoxide (CO), Artificial Neural Networks

Abstract

Coffee is categorized into three types: robusta, arabica, and liberica. After roasting, gas formation occurs within the coffee beans. Freshly roasted coffee is not palatable and is not fit for consumption due to its high carbon content. The purpose of this research is to determine the best coffee for consumption based on carbon gas levels. Carbon monoxide (CO) is measured using an E-nose sensor. The determination of carbon monoxide levels is done using MQ-135, MQ-7, TGS 2602, and TGS 2620 sensors. The highest levels of carbon monoxide (CO) were found in Arabica coffee with a light roast level, with gas levels of 389 ppm and 34.21 ppm. The lowest levels of carbon monoxide (CO) were also found in Arabica coffee with a light roast level, with gas levels of 389 ppm and 34.21 ppm. Carbon monoxide (CO) levels were found in Robusta coffee with a dark roast level, with gas levels of 327 ppm and 25 ppm. To determine coffee identification using the artificial neural network method, the success rate was 98% for liberica, 100% for arabica coffee, and 98% for robusta coffe

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