Comparative Analysis of SVM and Perceptron Algorithms in Classification of Work Programs

Government agencies are required to mobilize every aspect of publication which is carried out every year which must be accounted for and also carried out for each device that receives it such as assisted villages by utilizing available apbd funds in maximizing work programs designed so that they can be implemented optimally and effectively. by getting the best from all aspects of the work program implementation, of course there are important points in designing an annual work program without exception. data mining itself can help the department of population, family planning, women's empowerment and child protection in analyzing each work program design from before it is implemented onwards to look at various aspects of past data whose grouping is in the form of classification. The purpose of this study is to build a classification model with the addition of a sigmoid activation function that uses svm and perceptron to obtain a comparison value for the accuracy of the algorithm used to obtain the best working program design. The classification results are used to get the best value for classifying the best P2KBP3A work program dataset where it can be seen that the average accuracy value is 87.5%, the f1 value is 82.2%, the precision value is 80.2%, and the recall value is 87.5% so that the final result of the research results obtained a good accuracy value

Comparative Analysis of Support Vector Machine and Perceptron In The Classification of Subsidized Fuel Receipts

Currently, fuel oil is one of the important factors for the community and even a country on this earth to utilize this natural gas fuel for daily use as the main use and also by increasing the community's need for fuel oil. But there are several factors that cause this fuel problem, there is a factor of time and usage time, which is certain that one day it will expire and its capacity in a country, even if the country runs out of fuel, will make requests to other countries and also obstacles to supplying this fuel oil to the public. which is the main fuel from the Pertamina government agency which has begun to limit purchases for this fuel oil to certain circles by marking the types of subsidies or not subsidies that must be controlled by the government in limiting purchases for the public. In dealing with solving problems from the perspective of ownership or even utilization, there are limits to owning fuel, and not everyone has to have a lot or even too much.  In solving the problem of dividing fuel revenue, which is good for filling revenue, it can be solved by using machine learning, namely data mining itself can help in completing subsidized fuel receipts without being excessive for the community so that they can be controlled and managed for their purchases. In building a fuel oil reception design, it can be grouped into a classification model that uses SVM and perceptron which uses the activation function of the sigmoid to get the final result of accuracy where getting the average value of 5-fold, 10-fold, 20-fold is accuracy. is 90.0%, the F1 value is 85.6%, the precision value is 87.6%, and the recall value is 90.0%.Currently, fuel oil is one of the important factors for the community and even a country on this earth to utilize this natural gas fuel for daily use as the main use and also by increasing the community's need for fuel oil. But there are several factors that cause this fuel problem, there is a factor of time and usage time, which is certain that one day it will expire and its capacity in a country, even if the country runs out of fuel, will make requests to other countries and also obstacles to supplying this fuel oil to the public. which is the main fuel from the Pertamina government agency which has begun to limit purchases for this fuel oil to certain circles by marking the types of subsidies or not subsidies that must be controlled by the government in limiting purchases for the public. In dealing with solving problems from the perspective of ownership or even utilization, there are limits to owning fuel, and not everyone has to have a lot or even too much.  In solving the problem of dividing fuel revenue, which is good for filling revenue, it can be solved by using machine learning, namely data mining itself can help in completing subsidized fuel receipts without being excessive for the community so that they can be controlled and managed for their purchases. In building a fuel oil reception design, it can be grouped into a classification model that uses SVM and perceptron which uses the activation function of the sigmoid to get the final result of accuracy where getting the average value of 5-fold, 10-fold, 20-fold is accuracy. is 90.0%, the F1 value is 85.6%, the precision value is 87.6%, and the recall value is 90.0%