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

Jaka Tirta Samudra; Rika Rosnelly; Zakarias Situmorang

doi:10.29207/resti.v7i3.4731

Jaka Tirta Samudra Universitas Potensi Utama
Rika Rosnelly Unviersitas Potensi Utama
Zakarias Situmorang Unviersitas Potensi Utama

DOI: https://doi.org/10.29207/resti.v7i3.4731

Keywords: Data Mining, SVM, Perceptron, Klasifikasi, BBM

Abstract

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%.

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