Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Metode Simple Additive Weighting (SAW) (Studi Kasus di PT MNC Sky Vision Tbk)

MNC Sky Vision (formerly known as PT Matahari Lintas Cakrawala) is a company involved in operating the first Pay TV in Indonesia. The problem of evaluating employee performance is something that cannot be avoided in today's world of work. Assessment by voting with the most votes is still considered ineffective because the measurement of values ​​is still guided by interpersonal relationships and the principle of conscience. To be able to improve the quality of good employee performance and to be able to support the company's progress, especially at PT MNC Sky Vision Tbk, the agency needs a stage of improvement and system development in a good and directed management process, based on this the researcher wants to provide a solution to solving the problem through the title research “Decision Support System for Selection of the Best Employees Simple Additive Weighting (SAW) Method (Case Study at PT MNC Sky Vision Tbk)

Credit risk prediction using neural network backpropagation algorithm

Credit is a business activity that contains high risk and greatly affects the health and survival of banking businesses and financing institutions. Predicting credit risk is very beneficial for banks or financing institutions in taking decisions to establish credit. Decision makers of banks and financing institutions must have the precautionary principle to minimize credit risk when credit will be provided. The study designed credit risk prediction software with artificial neural network methods backpropagationalgorithms. Artificial neural network backpropagation with 1 hidden layer and the amount of data for training and testing as many as 20 pieces consisting of 5 models and using the logsig activation function is able to predict credit risk with a truth percentage of 70%-80%. Training and testing is used using matlab 6.1 software. Based on these results, the study recommends the development of artificial neural network algorithms as an effective method on credit risk prediction systems

Analysis of Elearning Quality Measurement With Webqual Method at Politeknik MBP Medan

The development of information technology today has also changed the lifestyle and way of life of many people. By using information technology, many people can explore the world without being limited by space and time through the internet. Information technology has become a tool that has the power as a driving force that changes business, economics, socio-politics and other fields without limits. One of the things that has experienced major changes in lifestyle and way of life in the field of education is teaching and learning activities. The Politeknik MBP Medan college, which was also affected by Pandami, had to change the teaching-learning method using the Learning Management System service which is the right teaching-learning method to provide learning materials to students, in this case called students. In order to obtain the quality of this service, first, the satisfaction level of service users is measured based on the standard of comparison (gap) between reality and the expectations of users of the service. Based on the results of quality measurements from e-learning at the Politeknik MBP Medan for the academic year 2020/2021 in the even semester using the Webqual method, it was concluded that the Webqual method along with its attributes can provide analytical results to be used in improving the performance quality of e-learning. The results of measuring the quality of the data processing of respondents' answers are obtained that the User Satisfaction Level Score is -0.18, this indicates that the quality of e-learning as a whole is not in line with the expectations of users. The results of the analysis of 22 statement attributes from the Webqual method show that all attributes get the results of the "Tingkatkan" pattern analysis and none of the statement attributes get the "Pertahankan" pattern

IMPLEMENTATION OF FUZZY LOGIC TO DETERMINE THE BEST EMPLOYEES

Employees are people who are able to work and produce goods and services for their own needs and society. Every company really yearns for employees who are able to work professionally so that the company's targets can be met. How does the company motivate employees by rewarding the best employees so that employees are motivated to work. Selection of the best employees by setting 5 (five) criteria, namely: Discipline, Loyalty, Communication, Attendance and Problem Solving. The assessment is carried out by taking attendance data and observation visually. The Fuzzy Tsukamoto method is used to obtain accurate, relevant and quickly presented results. Developed an information system to be able to produce the right decision by processing 5 (five) criteria data whose processing combines all criteria values and high rated values as the BEST value

Analysis of Elearning Quality Measurement with Webqual method using Artificial Neural Networks

Currently, artificial intelligence is a concern for the world because of its increasingly rapid and sophisticated application in helping humans to complete their work in everyday life. One of the widely used methods is artificial neural networks that are part of deep learning and a subsection of machine learning. In its network training, the data used as input is the gap score of each webqual dimension and the data used as the output is the gap score of the average webqual attributes of each respondent. The training process is expected to produce an actual output close to the predetermined target output, resulting in the best model of artificial neural networks with feedforward backpropagation algorithms. From the results of the training experiment, the best model of artificial neural network architecture was obtained with a feedforward backpropagation algorithm at the time of training from 174 data to be able to replace the Webqual method in this study using the 3-20-1 model and the algorithm used was Levenberg-Marquardt (trainln). Where there is 1 Input layer with 3 neuron units, 1 hidden layer with 20 neuron units and 1 Output layer with 1 neuron unit with a mean square error (mse) of 0.00000000000721 and regression of 1 or 100%. And after testing using 58 data using the network configuration obtained during training, the results of the comparison between the network output and the target were 100% accurate