Increased Competitiveness and Work Readiness of Students Four Year Vocational High School (VHS)

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

ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION

Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility. In recent years, Neural networks have been very victorious in several signal processing and control applications. Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods