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Metallic catalytic converter (MCC) is one of the technologies widely applied to motorcycle exhausts which aims to improve exhaust emission to be more environmentally friendly. However, even though many studies have been carried out, optimal design has not been achieved when compared to other designs. Through this research, the Taguchi method is proposed as an alternative method to find the optimum parameters of MCC. The Taguchi method was chosen because of its ability to find a robust combination of parameters. There are four MCC parameters used as inputs while each parameter consists of three levels, thus the design used is the L18 Orthogonal Array (OA) which each combination is tested on three types of vehicles, namely moped, automatic, and sports motorcycle. The signal-to-noise ratio (S/N) was adopted as one of the quality indicators of each combination. The optimization results showed that the best MCC design to reduce CO emissions is STD PGM. However, the optimum CO design can be used as an alternative because the difference in the S/N ratio is only -0.372. Meanwhile, the optimum CO design has another advantage over the STD PGM, namely the S/N value of the power ratio which tends to be higher with a difference of 5.037 compared to the STD PGM. Then, the best MCC design capable of increasing power is the optimum power design. The optimum power design has a superior S/N ratio with a difference of 5.404. In terms of emission, the optimum power design tends to be lower by a difference of -1.875 compared to the STD PGM.

Increased exhaust emissions from motor vehicles have become a major concern in efforts to reduce air pollution. One developed solution is the use of transition metallic catalytic converter (TMCC) technology in vehicle exhaust systems. This study aims to compare the fuel consumption efficiency of three types of exhaust systems, namely standard exhaust without a catalyst (STD WC), the standard exhaust with Original Equipment Manufacturer catalyst (STD OEM), and an exhaust system equipped with a Copper-Coated Chrome Metallic Catalytic Converter (TMCC CuCr). The data analysis method employed a quantitative approach by collecting fuel consumption data at each rpm and analyzing the mean and standard deviation. The research findings indicate that STD OEM has a lower average fuel consumption (0.80 liters per hour) and smaller standard deviation (0.06) compared to TMCC CuCr (0.83 liters per hour and 0.07). Although TMCC CuCr demonstrates good efficiency, STD OEM remains the best choice in terms of fuel efficiency. However, if the differences in fuel consumption and standard deviation are considered insignificant, TMCC CuCr could be a more economical alternative with its affordable price and greater material availability. Furthermore, its fuel consumption performance is not significantly different from that of STD OEM.

In this paper, Metallic Catalytic Converter (MCC) is installed in motorcycle exhausts to produce the minimum CO as well as to produce the optimum engine power. The results from previous research were collected and then used to predict the best MCC design using the Artificial Neural Network Multi-Objective Genetic Algorithm (ANN-MOGA). In addition, the ANN parameter tuning process was also carried out using the Taguchi method to find the initial weighting and bias that is able to provide the best and the most stable performance to predict the best MCC design. The best two sets of design solutions out of 70 sets of Pareto solutions were obtained by ANN-MOGA. Those two MCC designs are the optimum emission design and the optimum multi-objective design. The verification results show that the optimum multi-objective design tends to be superior in terms of CO emissions and engine power. In terms of CO emissions, the optimum multi-objective design gets a larger S/N ratio of -10.98, while the optimum emission design only gets an S/N ratio of -11.21. Meanwhile, in terms of engine power, the optimum multi-objective design gets a larger S/N ratio of 16.13, while the optimum emission design only gets an S/N ratio of 15.86 S/N. It is in line with the ANOVA test results which show that the optimum multi-objective design is proven to be better than the optimum emission design.

This research aims to develop psychomotoric instruments adapted to the current conditions of Covid-19. Development research was carried out with reference to the 4D model from Thiagarajan. The research sample used was 39 students of the Ottimmo International MasterGourmet Academy. The research instrument used a validation sheet from validators or experts. Data of the research were obtained based on the results of the validation of the experts, which were then analyzed to determine the validity and reliability of the developed psychomotoric instruments. The findings of study indicated that the results of (1) the validation got an average value of 4.61 with a very valid category; and (2) reliability got a value of 0.671 in the reliable category. The conclusion of the research shows that the developed psychomotoric instrument is suitable for use in service and beverage learning.

This study is aimed to analyze advantages of problem based learning and argumentation (PBLA) for vocational school students; and (2) analyze the implementation of PBLA to improve the critical thinking skills of vocational school students. This was library research which used secondary data sources and were collected through scientific articles, books, websites, and other relevant sources. Furthermore, the data were analyzed qualitatively with the Miles and Huberman model, which consisted of data collection, data reduction, data presentation, and concluding. From the results of the literature study, several conclusions can be drawn which include: (1) the implementation of PBLA has the potential to provide benefits both in terms of experience, motivation, innovation, and improvement of 21st Century vocational school student skills; and (2) PBLA supplementation will be very effective in increasing sudents’critical thinking skills if the teacher can direct students to be able to learn actively through various activities such as stating problems, discussing, arguing, concluding solutions, and evaluating.