The Use of Artificial Neural Networks (ANN) in the Chayote Chips Dough Mixer

Roza Susanti; Zas Ressy Aidha; Surfa Yondri; Sir Anderson; Tri Oktaviandra

doi:10.25077/ajeeet.v2i2.27

The Use of Artificial Neural Networks (ANN) in the Chayote Chips Dough Mixer

Authors

Roza Susanti Politeknik Negeri Padang
Zas Ressy Aidha Politeknik Negeri Padang
Surfa Yondri Politeknik Negeri Padang
Sir Anderson Politeknik Negeri Padang
Tri Oktaviandra Politeknik Negeri Padang

DOI:

https://doi.org/10.25077/ajeeet.v2i2.27

Keywords:

Chayote Chips, TCS3200, RGB Color Sensor, Artificial Neural Network

Abstract

This study uses a backpropagation neural network to determine the evenness of the chayote chip dough. The Tcs3200 Color Sensor mounted on the stirrer alt is used as a sensor to determine the color of the chayote emping dough. A regression score of 1 indicates that the input and target data match in the test results of the artificial neural network, which has an objective error (MSE) value of 0.0096306 achieved in the 313th epoch. Changes in RGB color readings on the TCS sensor from min values ??<40 and max values>52 in mixing dough are influenced by distance and light intensity which will be converted in the form of frequency.

Downloads

Published

2022-12-30

How to Cite

Susanti, R., Aidha, Z. R., Yondri, S., Anderson, S., & Oktaviandra, T. (2022). The Use of Artificial Neural Networks (ANN) in the Chayote Chips Dough Mixer. Andalas Journal of Electrical and Electronic Engineering Technology, 2(2), 50–54. https://doi.org/10.25077/ajeeet.v2i2.27