ABSTRACT
Worker’s performance in the automotive industry especially from the components assembly department are believed will be affected by the environmental factor. The intention of this study is to investigate the effect of environmental factor such as illuminance, Wet Bulb Globe Temperature (WBGT), and relative humidity to the worker productivity at automotive industry and optimize these factors by using Response Surface Method and Artificial Neural Network (ANN). The study was carried out at a room with area of 17m2 which equipped with environmental parameter control system. Subjects were performed the task of manual assembly operation. The information and data collection is done using the measurement instruments of QuestTemp°36 and Heavy Duty Light Meter. Experimental result shows that there is the relationship of these three environmental factors on the worker’s productivity. The analysis from Response Surface Method indicated that the productivity was predicted to be 1.0 under the condition of WBGT is 25.9°C and relative humidity 40% with illuminance of 441.51 lux. Meanwhile through the ANN, the data were trained to react as linear relationship. The linear relationship from ANN revealed that the optimum value of production (value≈ 1) can be achieved with the temperature (WBGT) is 22.7°C. The findings from the study can be proof nearly equal and therefore the authors believed that both methods can be used to propose the optimum environmental factors towards human productivity.
