This study aimed to model group preference decision-making to implement Industry 4.0 on food and beverage small- and medium-sized enterprises (SMEs). An ant colony optimisation was adopted to model the decision-making process based on the travelling salesman problem. The model was demonstrated on three preferences of Industry 4.0, namely, ergonomic work methods, machineries and tools, and e-commerce and promotion. The Likert-scale Kansei words data were obtained from 120 SMEs’ manager in Indonesia. The data were then plotted to Cartesian coordinates with the x and y-axes showing the mode and average values, respectively, and were simulated in a sequence using ACO. The results indicated that machinery and tools were the most preferred to implement Industry 4.0. Adaptiveness was the most preferred attribute in making the first decision. The benchmarking demonstrated ACO performed better than genetic algorithm in modelling group preference. The group preference was also compared to trust level and the finding showed a high correlation between both constructs. The high trust in the group preferences is expected to contribute for the sustainability of Industry 4.0. The method enriches various existing theoretical approaches for group preference decision-making and applicable to assist the SME’s management for implementation of Industry 4.0.

This study aimed to model a trust decision-making of Indonesian small and medium-sized enterprise (SME) groups in the adoption of Industry 4.0 namely, ergonomic, machinery, and e-commerce technology. The data on trust and its constraints were collected through a questionnaire, and formulated in a Kansei fitness function. The trust was modeled by Swarm Modeling (SM) to extract critical constraints. Traveling salesman problem-based Ant Colony Optimization (ACO) was used to determine the optimum decision-making path. The simulation indicated that the perception of technology benefit limited the adoption of Industry 4.0. The three optimal trust decision-making paths were generated on Java, Sumatera and Nusa Tenggara groups.

This paper highlights modeling affective temperature control in food small and medium-sized enterprises (SMEs). Modeling defined that workstation temperature set point could be controlled based on worker heart rate and workstation environment using Artificial Neural Network (ANN). The research objectives were: 1) to propose modeling affective temperature control in food SMEs based on heart rate and workstation environment; and 2) to develop an ANN model for predicting workstation temperature set point. Training and validation data were collected from six food SMEs in Yogyakarta Special Region, Indonesia. The data of temperature set points were verified using a simulated confined room. The inputs of the ANN model were worker heart rate, workstation temperature, relative humidity distribution and light intensity. The output was temperature set point. Research results concluded satisfactory performance of ANN. The model could be used to provide environmental ergonomics in food SMEs.

Merapi is categorized as the most active volcano in Indonesia and most important place of open green space in Yogyakarta, Indonesia. Merapi had erupted from September to October 2010 and destroyed many open green spaces. One of the potential solutions to increase percentage of open green space is the application of greening material in eco-agro-tourism program in Merapi Disaster Prone Area. Greening material is a plant panel consisting of plant and hydroponics media; suitable to be attached in the surroundings of a building. Sphagnum Moss (Sphagnum sp) which is a local biodiversity was used and evaluated as a greening material. The design was developed using Kansei Engineering. Four prototypes of the Sphagnum Moss as greening material were generated as rooftop, canopy, pot and portable garden.

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.

Efficient biogas production significantly impacts greenhouse gas (GHG) emissions and carbon sequestration by reducing emissions and enhancing carbon storage. Nonetheless, the consistency and optimization of biogas production are hindered by fluctuations in key input variables, namely, pH, moisture content, organic loading rate (OLR), and temperature, which significantly impact the quality of agricultural waste biomass and biogas production. Any fluctuations in these variables can affect biogas productivity. This study aims to provide valuable optimization parameters for maximum biogas production using rice straw and cow dung as agricultural waste biomass for biogas input materials. Therefore, machine learning techniques such as multilayer perceptron feedforward neural networks with a particle swarm optimization (PSO) combination generate optimal values for each variable for maximum biogas production. This study uses three variants of the training function for neural networks, namely gradient descent with momentum and adaptive learning rate, gradient descent with momentum, and gradient descent with adaptive learning rate. The findings reveal that, under an optimum pH value of 6.0000, a humidity of 62.3176%, an OLR of 67.6823 kg.m³/day, and a temperature of 37.0482 °C, biogas production has the potential to increase to 2.91 m³/day with a high accuracy testing value of R² = 0.90. These methods in use accurately predict the optimal parameters, with a maximum deviation of 8.48% from experimentally derived values and mean square error (MSE) of 0.0051243. This study emphasizes the benefits of using multilayer perceptron feedforward neural networks and particle swarm optimization to optimize operational parameters and accurately predict biogas production.