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

Dublin Core

Title

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

Subject

data mining; SVM; perceptron; classification; fuel

Description

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, starting from the type of pertalite,
Pertamax, to diesel for various types of private vehicles to tourists who use it for vehicles and even daily staples used. 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. That way, to get everyone in
the community who deserves to receive this fuel limit to the maximum by designing subsidized fuel oil revenues. 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%.

Creator

Jaka Tirta Samudra, Rika Rosnelly, Zakarias Situmorang

Source

http://jurnal.iaii.or.id

Publisher

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

Date

June 2023

Contributor

Sri Wahyuni

Rights

ISSN Media Electronic: 2580-0760

Format

PDF

Language

English

Type

Text

Files

Collection

Tags

,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon , ,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon , ,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon , ,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon ,

Citation

Jaka Tirta Samudra, Rika Rosnelly, Zakarias Situmorang, “Comparative Analysis of Support Vector Machine and Perceptron In The Classification of Subsidized Fuel Receipts,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10004.