The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).
If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.
Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.
How to cite (IEEE):
L. Triyono, R. Gernowo, P. Prayitno, M. Rahaman, & T. Yudantoro
"Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment," JOIV : International Journal on Informatics Visualization, vol. 7, no. 3, , pp. 726-732, Sep. 2023.
https://doi.org/10.30630/joiv.7.3.1243
How to cite (APA):
Triyono, L., Gernowo, R., Prayitno, P., Rahaman, M., & Yudantoro, T.
(2023).
Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment.
JOIV : International Journal on Informatics Visualization, 7(3), 726-732.
https://doi.org/10.30630/joiv.7.3.1243
How to cite (Chicago):
Triyono, Liliek, Gernowo, Rahmat, Prayitno, Prayitno, Rahaman, Mosiur, AND Yudantoro, Tri.
"Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment" JOIV : International Journal on Informatics Visualization [Online], Volume 7 Number 3 (10 September 2023)
https://doi.org/10.30630/joiv.7.3.1243
How to cite (Vancouver):
Triyono L, Gernowo R, Prayitno P, Rahaman M, & Yudantoro T .
Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment.
JOIV : International Journal on Informatics Visualization [Online]. 2023 Sep;
7(3):726-732.
https://doi.org/10.30630/joiv.7.3.1243
How to cite (Harvard):
Triyono, L., Gernowo, R., Prayitno, P., Rahaman, M., & Yudantoro, T.
,2023.
Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment.
JOIV : International Journal on Informatics Visualization, [Online] 7(3), pp. 726-732.
https://doi.org/10.30630/joiv.7.3.1243
How to cite (MLA8):
Triyono, Liliek, Rahmat Gernowo, Prayitno Prayitno, Mosiur Rahaman, & Tri Raharjo Yudantoro.
"Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment." JOIV : International Journal on Informatics Visualization [Online], 7.3 (2023): 726-732. Web. 2 Oct. 2023
, https://doi.org/10.30630/joiv.7.3.1243
BibTex Citation Data :
@article{JOIV1243,
author = {Liliek Triyono and Rahmat Gernowo and Prayitno Prayitno and Mosiur Rahaman and Tri Yudantoro},
title = {Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment},
journal = {JOIV : International Journal on Informatics Visualization},
volume = {7},
number = {3},
year = {2023},
keywords = {Data mining; Hoax; False News; Visual Impairment},
abstract = {It has become a necessity for people to communicate with each other to complete their needs. The exchange of information conveyed in communication often cannot be directly assessed, especially online news. They just get news and are unable to filter out inappropriate stuff. The media website conveys a great deal of information. Popular news websites are one source for keeping up with the newest news. It requires a significant amount of work to deliver news on prominent websites and to choose content that is not incorrect. To crawl the web and analyse enormous data, massive computer power is required, and solutions to lower the process's space and temporal complexity must be created.Data mining is seen to be a solution to the aforementioned difficulties since it extracts particular information based on defined attributes. This research investigated a model to determine the content of false news information in Indonesian popular news. Firstly, preprocessing process from dataset that collected from keaggle. Secondly, we try use classification methods to determined which the optimal method to classify fake news. Thirdly, we use another public dataset for testing method. Furthermore, five machine learning classifiers are compared: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC), and Random Forest (RF). These classifications are utilized independently before being compared based on receiver operating characteristic curves and accuracy. The experimental result shows that DTC has the lowest accuracy of 75.33% and SVM has the highest accuracy of 83.55%. },
issn = {2549-9904}, pages = {726--732}, doi = {10.30630/joiv.7.3.1243},
url = {https://joiv.org/index.php/joiv/article/view/1243}
}
Refworks Citation Data :
@article{{JOIV}{1243}, author = {Triyono, L., Gernowo, R., Prayitno, P., Rahaman, M., Yudantoro, T.}, title = {Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {7}, number = {3}, year = {2023}, doi = {10.30630/joiv.7.3.1243}, url = {} }Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
__________________________________________________________________________
JOIV : International Journal on Informatics Visualization
ISSN 2549-9610 (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W : http://joiv.org
E : joiv@pnp.ac.id, hidra@pnp.ac.id, rahmat@pnp.ac.id
View JOIV Stats
is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.