Abstract
This paper highlighted a new method to assess worker capacity in Indonesian small-medium food industry (SMFI). The sustainability of SMFI should be maintained based on the worker capacity. The status of worker capacity could be categorized as normal, capacity constrained worker and bottleneck. By using Kansei Engineering, worker capacity can be assessed using verbal parameter of mood and non-verbal parameter of heart rate in a given workplace environment.
Fusing various Kansei Engineering parameters of worker capacity requires a robust modeling tool. Artificial Neural Network (ANN) was used to develop a Kansei Engineering-based watchdog model. The model is defined as a black box relationship between worker capacity and workplace environmental parameters. Its function for assessing worker capacity can be defined as dynamic variation of mood and heart rate in a given workplace environment. Thus, these relationships were modeled using a three layered ANN. The model was demonstrated via a case study of Tempe Industry. The trained ANN model generated satisfied accuracy and minimum error. The research results concluded the possibility to assess the worker capacity in Indonesian SMFI by combining Kansei Engineering and ANN.
