Environmental Ergonomic Analysis in MSMEs of ‘Karak’ using Working Environment Approach, HIRARC, and Kansei Engineering - Adiatmaja, Firstama Saka; Ushada, Mirwan; Purwadi, Didik (Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia, 2025)
The working environment in Micro, Small, and Medium Enterprises (MSMEs) is considered less than ideal based on the Indonesian Minister of Health Regulation Number 70 of 2016 concerning Standards and Requirements for Industrial Working Environment, in relation to temperature, lighting, noise intensity, and humidity. Therefore, an analysis is necessary to improve MSMEs’ ability to create an ideal working environment. This study aimed to analyze and improve working environment ergonomics by focusing on the physical working environment, workers preferences and sensitivities, as well as work environment risks associated with various factors. The case study examined MSMEs in Klaten Regency, Central Java. The methods used included working environment observation, HIRARC (Hazard Identification, Risk Assessment, and Risk Control), Kansei engineering, and multiple linear regression (MLR). Specifically, HIRARC was used to assess risks, Kansei engineering to identify workers preferences and sensitivities, and MLR to determine correlations between variables. One of the risky working conditions was observed at the smoking and frying workstation, where the temperature reached 32.81˚C, exceeding the reference value of 31˚C. Lighting levels were recorded at 101.09 lux, below the recommended 200-500 lux. Noise levels measured 68.38 dB, within the acceptable limit of 85 dB, while humidity was 52.63% compared to the reference value of 51.36% ± 5.72%. HIRARC assessment classified the risk level at the smoking and frying workstation as medium to extreme. Furthermore, Kansei engineering identified key perception variables, namely dark vs. light, dirty vs. clean, unpleasant vs. pleasant, stuffy vs. cool, and hot vs. cold. The results showed working environment parameters significantly correlated with HIRARC and Kansei engineering variables, which consequently correlated with HIRARC.