This paper proposes a novel controller for automatic voltage regulator (AVR) system. The controller is used Focused Time Delay Neural Network (FTDNN). It does not require dynamic backpropagation to compute the network gradient. FTDNN AVR can train network faster than other dynamic networks. Simulation was performed to compare load angle (load angle) and Speed. The performance of the system with FTDNN-AVR has compared with a Conventional AVR (C-AVR) and RNN AVR. Simulations in Matlab/Simulink show the effectiveness of FTDNN-AVR design, and superior robust performance with different cases.

Vocational High School (VHS) is the initial level of vocational education whose task is to prepare students to become skilled labor in the industrial field. At this time there have been many changes, related to the era of industrial revolution 4.0, and the demands of life in the 21st century. Therefore it is necessary to increase the competence of vocational students in order to respond to these changes, both related to the era of industrial revolution 4.0, and demands for skills 4C to be able to live in existence in the 21st century. How far has the implementation of Vocational Schools been able to respond to these changes and demands? To answer this question, a research is needed entitled "Readiness of Vocational students in order to face the industrial revolution 4.0 and the demands of 21st century skills". Through literature studies, it was found that it was needed: (1) the use of learning models such as Problem Based Learning (PBL), Project Based Learning (PjBL), Cooperative Learning and the like, in order to foster 4C skills capabilities for vocational students; (2) the use of learning media such as e-larning, Flipped classroom and podcasts; (3) IoT utilization, digital literacy, and utilization of e-books that are cheap, practical, environmentally friendly, and up to date, (2) improving teacher quality related to the demands of 21st century skills. | Celebes Education Review

This study presents the application of the aquila optimizer (AO) algorithm to determine the parameters of the proportional integral derivative (PID) controller to control the speed of a dc motor. The AO method is inspired by the most popular bird of prey in the northern hemisphere named Aquila. Initially, the proposed AO algorithm is applied to unimodal and multimodal benchmark optimization problems. To get the performance of the AO method, the controller is compared with other methods, namely Seagull optimization algorithm (SOA), marine predators algorithm, giza pyramids construction (GPC), and chimp optimization algorithm (ChOA). The results represent that the AO is promising and shows the effectiveness. Determination of PID parameters using the AO method for dc motor speed control system shows superior performance.

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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.

The parasitism – predation algorithm (PPA) is an optimization method that duplicates the interaction of mutualism between predators (cats), parasites (cuckoos), and hosts (crows). The study employs a combination of the PPA methods using the cascade-forward backpropagation neural network. This hybrid method employs an automatic voltage regulator (AVR) on a single machine system, with the performance measurement focusing on speed and the rotor angle. The performance of the proposed method is compared with the feed-forward backpropagation neural network (FFBNN), cascade-forward backpropagation neural network (CFBNN), Elman recurrent neural network (E-RNN), focused time-delay neural network (FTDNN), and distributed time-delay neural network (DTDNN). The results show that the proposed method exhibits the best speed and rotor angle performance. The PPA-CFBNN method has the ability to reduce the overshoot of the speed by 1.569% and the rotor angle by 0.724%.

This study aims to determine the level of achievement of the fourth year Vocational School education program towards increasing student competitiveness and work readiness. The main objective of the fourth year vocational program is to equip students and graduates with various competencies in order to develop graduates capabilities in finding jobs, assigning work, entrepreneurship, pursuing the work faced and renewing their work skills. This research was conducted through a literature review of references originating from the theories and results of relevant research, and continued through focus group discussions. Relevant references include the policies of the Government of the Republic of Indonesia, guidance on the implementation of the fourth year vocational program, relevant research results, namely evaluations of four years vocational programs, and influencing factors in increase in competitiveness and work readiness graduates. The study found: (1) reviewed from the curriculum of fourth years vocational school graduates having more work experience in the industry in the fourth year; (2) in terms of the competency of fourth year vocational school graduates having better competence than the third year vocational program; (3) in terms of industry interest, fourth year vocational graduates have more acceptance as labor in the industry than third year graduates.