Vocational education has an important role in the effort to create a workforce that has competencies that are in accordance with the needs of the industrial world. But the open unemployment rate in Indonesia in February 2018 was 6.87 million people and 8.92% of them were graduates of VHS. Why does that much unemployment happen? Is the learning process at VHS not yet qualified, so that the quality of VHS graduates is still low? Can the teaching factory improve the quality of VHS graduates? To answer this question, research needs to be done. This research was conducted using literature studies on related references, and a number of research reports on teaching factories conducted at VHS and continued with FGD. The results of the study concluded that the teaching factory consisted of planning, organizing, implementing, and evaluating. The teaching factory developed was integrated with the production unit that was used for the practice of students, so that VHS graduates became qualified and ready to enter the workforce.
A DC motor is applied to delicate speed and position in the industry. The stability and productivity of a system are keys for tuning of a DC motor speed. Stabilized speed is influenced by load sway and environmental factors. In this paper, a comparison study in diverse techniques to tune the speed of the DC motor with parameter uncertainties is showed. The research has discussed the application of the feed-forward neural network (FFNN) which is enhanced by a sine tree-seed algorithm (STSA). STSA is a hybrid method of the tree-seed algorithm (TSA) and Sine Cosine algorithm. The STSA method is aimed to improve TSA performance based on the sine cosine algorithm (SCA) method. A feed-forward neural network (FFNN) is popular and capable of nonlinear issues. The focus of the research is on the achievement speed of DC motor. In addition, the proposed method will be compared with proportional integral derivative (PID), FFNN, marine predator algorithm-feed-forward neural network (MPA-NN) and atom search algorithm-feed-forward neural network (ASO-NN). The performance of the speed from the proposed method has the best result. The settling time value of the proposed method is more stable than the PID method. The ITAE value of the STSA-NN method was 31.3% better than the PID method. Meanwhile, the ITSE value is 29.2% better than the PID method.
This research aims to discover students’ increase abilities, skills, and innovative behaviors after being taught using a problem-based learning model using IoT-B-HMI-LM in the form of Trainer Kit. The study was conducted using quantitative methods on 69 students and eight teachers with electrical engineering expertise at vocational high schools in East Java, Indonesia. The stratified simple sampling technique was used to determine the research sample. Data analysis used descriptive statistics and paired sample t-test.
The sample is determined by stratified random sampling. Data analysis with descriptive statistics and paired t-test. The results of the study show an increase in students' abilities, skills, and innovative behavior after being taught a problem-based learning model using the IoT-B-HMI-LM in the form of a Trainer Kit.
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