The biometric-based module of smart grid system E Engel, I V Kovalev and A Ermoshkina Abstract. Face identification systems are developing rapidly, and these developments drive the advancement of biometric-based identification systems that have high accuracy. However, to develop a good face recognition system and to have high accuracy is something that's hard to find. Human faces have diverse expressions and attribute changes such as eyeglasses, mustache, beard and others. Fisher Linear Discriminant (FLD) is a class-specific method that distinguishes facial image images into classes and also creates distance between classes and intra classes so as to produce better classification.
This research focused on the development a new learning model in Vocational Education to answer the challenges of this Industrial Revolution 4.0 era. The problem identified was the lack of learning outcomes, especially subjects oriented to software engineering for information systems students in particular and other computer science seen in the phenomenon of the inability of students to produce intelligent systems. From a series of validity, practicality, and effectiveness test results, use content validity with Aiken'V and construct validity with CFA (Confirmatory Factor Analysis) states that the model resulting from this study is stated, valid, practical and effective. This study also produced a new learning model with five syntaxes, namely (1) Define Problem and Design a Plan Project, (2) Interaction with Support System, (3) Create a Project, (4) Keep control and Monitoring Project, (4) Yield and Assessment of Project. And based on the test of the validity of the syntax of this model stated goodness-of-fit or valid.
Non-Cash Food Assistance (BPNT) is social food assistance that is paid in non-cash form every month by the government. However, the problem of identifying BPNT recipients has not been identified properly and it is not certain that the poor really deserve to receive BPNT. So far, the existing system has not been optimal for selecting BPNT recipients with the existing criteria. Data management still uses a manual system and is not effective in determining who is entitled to receive BPNT and who is not. A creative solution to overcome this problem is the use of a web-based decision support system (DSS) using the Additive Ratio Assessment (ARAS) method. A system based on human and computer intelligence that creates options to improve decision making. The purpose of this research is to develop a system that helps decision making in deciding the acceptance of BPNT in poor families. This makes it easy to determine who is eligible and who is not eligible for assistance. This method is easy to apply for ranking by comparing it with other methods so that the results are more precise and accurate. The calculation process for the ARAS method using a very complex web-based system greatly facilitates and speeds up the determination of BPNT receipts. To facilitate implementation, 10 data with 5 criteria were used as sample data. Based on the calculation results of the potential beneficiaries on behalf of Selamat and Prayogi, the priority is getting assistance with a final score of < 0.07.
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