Combining Kansei Engineering and Artificial Neural Network to Assess Worker Capacity in Small-Medium Food Industry - Mirwan Ushada; Tsuyoshi Okayama; Atris Suyantohadi; Nafis Khuriyati; Haruhiko Murase (Proceeding of the Electrical Engineering Computer Science and Informatics, 2014)
This paper highlighted a new method for worker capacity assessment in Indonesian small-medium food industry. The sustainable and productivity of Indonesian food industry should be maintained based on the workers 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 response of profile of mood states, non-verbal response of heart rate in a given workplace environmental parameters. Fusing various Kansei Engineering parameters of worker capacity requires a robust modeling tool. Artificial Neural Network (ANN) is required to assess worker capacity. 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 worker capacity in Indonesian small-medium food industry by combining Kansei Engineering and ANN.