Mathematical Model of Drying Edamame (Glycine max (L.) Merill) Using Food Dehydrator Technology Based on Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)

Rizza, Wijaya and Aulia, brilliantina Mathematical Model of Drying Edamame (Glycine max (L.) Merill) Using Food Dehydrator Technology Based on Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). J-TEP : Jurnal Teknik Pertanian Lampung.

	Text (Hasil Turnitin) Mathematical Model of Drying Edamame (Glycine max (L.) Merill) Using Food Dehydrator Technology Based on Multiple Linear Regression (MLR) and Artificial Neural Network (ANN).pdf Restricted to Repository staff only Download (2MB)
	Text (Korespondensi) POLITEKNIK NEGERI JEMBER Mail - [JTEP-L] Editor Decision.pdf Restricted to Repository staff only Download (83kB)

Abstract

Edamame is included in perishable products or products that have a fairly short shelf life if post-harvest processing is not carried out. One of the post-harvest processing methods commonly used by the community is drying. The purpose of this study was to analyze the drying process of edamame related to the MLRL and ANN models. This study used a completely randomized design (CRD) with three variations of air velocity, namely 1 m/s, 3 m/s, and 5 m/s. Data collection was repeated three times every 30 minutes until 330 minutes. Multiple linear regression (MLR) model training and validation produce accuracy values of 88.03 and 82.23, and the value of R2 of 0.93 and 0.90. While the training and validation of the artificial neural network (ANN) model resulted in accuracy values of 88.34 and 82.15, and R2 values of 0.93 and 0.90.

Actions (login required)

View Item