Artificial Neural Network Model for Affective Environmental Control System in Food SMEs

Abstract

This paper presents an affective environmental control system for Small and Medium -sized Enterprises (SMEs). The system is proposed as a technology innovation in appropriate information technology. It is defined that workplace environment set points could b e controlled using worker workload. The research ob jectives are: 1) To design an affective environmental control model for SME; 2) To develop an Artificial Neural Network (ANN) model for predicting affective environment set points. The system consisted of 4 sub -systems as measurement, assessment, control and decision. An ANN model is developed for sub -systems of control. Training and validation data are acquired from 4 (four) samples of SME in Yogyakarta Special Region, Indonesia. The model has b een developed successfully to predict temperature and light intensity set points using b ack -propagation supervised learning method. The research results indicated the satisfied performance of ANN with minimum error. ANN model indicated the closeness of R 2 value b etween training and validation data. The research results could b e applied to support the worker productivity in food SMEs b y providing a comfort workplace environment and optimum worker workload.

Management and Production Engineering Review

“…KESAN could be applied to support evidencebased policy for manager to develop an ergonomic work system based on environmental ergonomics. Subsequently, the policy could be technically implemented to several ergonomic programs as environmental ergonomics analysis [18], ergonomic risk assessment [19], affective environmental control [20] and work incentives [21]. These programs could be executed as air conditioner procurement, work sampling, time and motion study.…”