Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Media Journal of General Computer Science (MJGCS) Jurnal Pengabdian Masyarakat Unama
Eko Arip Winanto
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Improvement Attack Detection on Internet of Thinks Using Principal Component Analysis and Random Forest Adrian Pirtama; Yuda Prasetia; Redho Irnindo Saputra; Eko Arip Winanto
Media Journal of General Computer Science Vol. 1 No. 1 (2024): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v1i1.8

Abstract

Network security has become crucial in facing increasingly complex and sophisticated attack threats. Network intrusion detection aids in identifying suspicious activities indicating unauthorized intrusions. This research aims to enhance the performance of advanced attack detection. The Random Forest method is an algorithm that leverages an ensemble of decision trees. This ensemble comprises several independent decision trees used to classify data. One characteristic of the Random Forest method is its ability to address overfitting issues and provide good predictive quality. One approach to improving RF's performance is through Principal Component Analysis (PCA). PCA is a statistical technique used to reduce feature dimensionality. PCA eliminates feature correlations and identifies essential features that can enhance the detection of attacks and normal traffic. This research will be tested with the CIC IoT 2023 dataset, encompassing various attack types. The model testing consists of four feature dimensions, namely 5, 8, 10, and 47. The detection results are promising, significantly improving attack detection performance, reaching up to 99.2%.
Pelatihan Digital Marketing menggunakan Facebook Ads dan Marketplace Shopee sebagai strategi peningkatan Penjualan Pada UMKM Madu Mayeesha nurhadi; Pareza Alam Jusia; Ronald Naibaho; Khairuldi; Eko Arip Winanto; Dodi Sandra; Beni Irawan; Suwanto
Jurnal Pengabdian Masyarakat UNAMA Vol 2 No 1 (2023): JPMU Volume 2 Nomor 1 April 2023
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2023.2.1.745

Abstract

The community service activities carried out are community service activities funded by the Jambi Dinamika Bangsa Foundation. The implementation of this activity is carried out in the form of practicum and discussion to train training participants, namely Madu Mayeesha UMKM actors in terms of graphic design and Facebook Ads and Shopee Marketplace, to increase sales through digital marketing. This training uses the following methods: problem formulation stage, solution determination stage, settlement method, evaluation stage, and output. This training utilizes Graphic Design software such as: CorelDraw, Photoshop and Canva to create promotional designs or advertising materials for mayeesha honey products
Detection of UDP Flooding DDoS Attacks on IoT Networks Using Recurrent Neural Network Warcita; Kurniabudi; Eko Arip Winanto
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.79601

Abstract

Internet of Thing (IoT) is a concept where an object can transfer data through a network without requiring human interaction. Complex IoT networks make it vulnerable to cyber attacks such as DDoS UDP Flood attacks, UDP Flood attacks can disrupt IoT devices. Therefore, this study proposes an attack detection method using a deep learning approach with the Recurrent Neural Network (RNN) method. This study uses Principle Component Analysis (PCA) to reduce the feature dimension, before learning using RNN. The purpose of this study is to test the combined performance of the PCA and RNN methods to detect DDoS UDP Flood attacks on IoT networks. The testing in this study used 10 datasets sourced from CICIOT2023 containing UDP Flood and Benign DDoS traffic data, and the testing was carried out using three epoch parameters (iterations), namely 10, 50, and 100. The test results using RNN epoch 100 were superior, showing satisfactory performance with an accuracy value of 98%, precision of 99%, recall of 99%, and f1-score of 99%. Based on the experimental results, it can be concluded that combining PCA and RNN is able to detect UDP Flooding attacks by showing high accuracy.
Co-Authors Adrian Pirtama Beni Irawan Dodi Sandra Khairuldi Kurniabudi Kurniabudi, Kurniabudi NAIBAHO, RONALD Nurhadi Nurhadi Pareza Alam Jusia Redho Irnindo Saputra Suwanto Yuda Prasetia