Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Informatika: Jurnal Pengembangan IT
Yohan Muliono
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Phishing Site Detection Classification Model Using Machine Learning Approach Yohan Muliono; Muhammad Amar Ma’ruf; Zakiyyah Mutiara Azzahra
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9951

Abstract

Phishing has been a cybercrime that has existed for a long time, and there are still many people who are victims of this attack. This research attempts to prevent phishing by extracting the attributes found on phishing websites. This study uses a hybrid method by combining allowlist and denylist as part of a classification system. This research utilizes 18 features to identify a phishing site in terms of address bar, abnormal request, and source code (HTML and JavaScript). Where in each feature the author determines the benchmark. This study validates the status code and detects 52 URL shortening service domains and then evaluates these abnormalities with a binary classification system. Algorithms that have good results are Decision Tree and K Nearest Neighbor (KNN). After evaluating the performance of the algorithm in terms of Precision, Recall, and F-Measure. As a result, the Decision Tree algorithm has the highest accuracy of 97.62% and the fastest computation time of 0.00894 seconds. So that the Decision Tree is superior in terms of accuracy and computation time in detecting phishing URLs.
A Comparison of Text Classification Methods k-NN, Naïve Bayes, and Support Vector Machine for News Classification Fanny Fanny; Yohan Muliono; Fidelson Tanzil
Jurnal Informatika: Jurnal Pengembangan IT Vol 3, No 2 (2018): JPIT, Mei 2018
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v3i2.828

Abstract

In this era, a rapid thriving Internet occasionally complicates users to retrieve news category furthermore if there are plentiful of news to be categorized. News categorization is a technique can be used to retrieve a category of news which gives easiness for users. Internet has vast amounts of information especially at news. Therefore, accurate and speedy access is becoming ever more difficult. This paper compares a news categorization using k-Nearest Neighbor, Naive Bayes and Support Vector Machine. Using vary of variables and through a several steps of preprocessing which proving k-Nearest Neighbor is producing a capable accuracy competes with Support Vector Machine whereas Naive Bayes producing just an average result, not as good as k-Nearest Neighbor and Support Vector Machine yet as bad as k-Nearest Neighbor and Support Vector Machine ever reach. As the results, k-Nearest Neighbor using correlation measurement type produces the best result of this experiment. 
Co-Authors Fanny Fanny Fidelson Tanzil Muhammad Amar Ma’ruf Zakiyyah Mutiara Azzahra