One important point in carrying out the functions of the Tridharma of Higher Education by lecturers is to carry out research and publish the results of their thoughts and analyzes. The demands of publication by the academic community of Higher Education have a considerable impact on the awareness of the lecturers of the importance of conducting studies, research and writing scientific works. The development of scientific work in Indonesia has been relatively better, especially since the enactment of government regulations, which required S1, S2 and S3 students to write articles in scientific journals as one of the prerequisites for graduation. Lecturers certainly have greater demands to actively write in scientific journals both at accredited national level and reputable international journals. So the authors conducted this study aimed at analyzing the correlation of the level of lecturer workload to the increase in the number of publications. STIKOM Tunas Bangsa does not yet have a system to analyze the level of lecturer workload with an increase in the number of studies. For this reason, it is necessary to apply the Backpropagation algorithm. ANN combined with the Backpropagation algorithm can measure the level of correlation. The variables used are structural positions, number of even and odd semester credits, number of services. The target used is the amount of research. So the pattern of correlation between the two variables is formed. The output of the lecturer workload is reduced by the target which is the number of publications. So the results obtained are correlations between lecturers' workloads to the increase in the number of publications.
Online businesses are no stranger to the community where online businesses are increasingly in demand by the public because of the various supporting factors in which the public does not need to plunge into places to buy the desired items simply through online media but the lively online media in the community cannot be separated from various frauds. Other business people are still many who are reluctant to switch to online business that is inseparable from various factors or reasons they still stick with the old business method. It is very easy to play online recently in various circles of students and even housewives, can be used as additional capital. To find out what factors are the main reason people still hesitate to do business with online methods realized by the application of a decision support system determines the doubts of the online business community with the ELECTRE II method. It is hoped that research can find factors that cause public doubt in online business, so that later the output of this system can be a good evaluation material for the community in doing business. ELECTRE II method can be applied in the case of determining the failure factor of students in the main subject of community doubt in online business by considering several alternatives and criteria. Which alternatives are Cost Expenses (A1), Faced Risks (A2), and Difficult / Doubt to start the business to be run (A3). Data taken from the questionnaire respondents to STIKOM Tunas Bangsa Pematangsiantar students. From the results of this study, it was concluded that (A1) Cost Expenses with a value of 1.0238. For further research, criteria and alternatives can be added, so that the data entered is more varied. In addition, the ELECTRE II method can be compared with other methods. Research as a recommendation for business people who want to enter the world of online business.
Employee satisfaction includes the difference between the level of importance and perceived performance or results, and is an alternative evaluation that exceeds employee expectations. There are 5 dimensions to measure service quality based on expectations and perceived performance by employees, namely career development, leadership in HR, policy and law enforcement, building a work atmosphere and providing salaries and rewards. Five dimensions are very influential in the progress of STIKOM Tunas Bangsa, using data mining methods can be found important trends for campuses. Employee satisfaction assessment is based on a questionnaire filled out by the employee. The results of the questionnaire were processed using the c4.5 algorithm. The c4.5 algorithm is a classification method and produces a decision tree. C4.5 turns large facts into decision trees that represent rules. Rules are easy to understand in natural language. Based on the results of the research that has been done, the use of the C4.5 algorithm can help the campus in improving services according to the results of the questionnaire. The results of the calculation, there are two variables satisfied employee questionnaire. Meanwhile, the employee questionnaire was not satisfied with the three variables. The highest gain value is the variable to build a work atmosphere with a value of 0.20619372. The indicator of the variable of building a work atmosphere that has the highest entropy value is a fairly good indicator with a value of 1. The total of questionnaires filled in are 65 questionnaires, 44 people stated they were satisfied and only 21 people said they were not satisfied.
The research uses the Momentum Backpropagation Neural Network method to recognize characters from a letter image. But before that, the letter image will be converted into a binary image. The binary image is then segmented to isolate the characters to be recognized. Finally, the dimension of the segmented image will be reduced using Haar Wavelet. One of the weaknesses of computer systems compared to humans is recognizing character patterns if not using supporting methods. Artificial Neural Network (ANN) is a method or concept that takes the human nervous system. In ANN, there are several methods used to train computers that are made, training is used to increase the accuracy or ability of computers to recognize patterns. One of the ANN algorithms used to train and detect an image is backpropagation. With the Artificial Neural Network (ANN) method, the algorithm can produce a system that can recognize the character pattern of handwritten letters well which can make it easier for humans to recognize patterns from letters that are difficult to read due to various error factors seen by humans. The results of the testing process using the Backpropagation algorithm reached 100% with a total of 90 trained data. The test results of the test data reached 100% of the 90 test data.
This study aims to optimize the performance of the Convolutional Neural Network (CNN) in the image classification task by applying data augmentation and fine-tuning techniques to a case study of mammal classification. In this study, we took a fairly complex image classification dataset and used the CNN model as a basis for training and evaluating the performance of the model compared to Back propagation. From this study, the CNN VGG16 architecture optimized with ADAM optimization has been compared with the Back propagation optimization of SGD. We also conducted a literature review on several related studies and basic concepts in CNN, such as convolution, pooling, and fully connected layers. The research methodology involves creating datasets using data augmentation techniques, model training using fine-tuning techniques, and testing model performance using a number of evaluation metrics, including accuracy, precision, and recall. The results of this study indicate that the techniques used have succeeded in improving the performance of the CNN model in complex image classification tasks with accuracy in identifying and monitoring animal species more accurately, with an accuracy of 91.18% for the best model. Model accuracy increased by 2% after applying data augmentation and fine-tuning techniques to the CNN model. These results indicate that the techniques applied in this study can be a good alternative in improving the performance of the CNN model in the image classification task.
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