Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Computation International Journal of Informatics Engineering and Computing
Sarah Anjani
Universitas Gadjah Mada, Indonesia
Computer Science & IT

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

Found 2 Documents
Search

 1 
IDS-GAN: Stepping up Intrusion Detection Method using GAN Algorithm Fan Haoyi; Sarah Anjani
International Journal of Informatics and Computation Vol. 5 No. 1 (2023): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v5i1.55

Abstract

Many computer network threats cause the security aspect to become the most critical problem. The intrusion detection system is a widely used practical security tool to prevent malicious traffic from penetrating networks and systems. To solve the issue, we construct a novel algorithm using Generative Adversarial Networks (GAN) to address the IDS security problem. In this paper, we propose an intrusion detection model using GAN by analyzing the extracted features of the network. To build our detection model, we collect the dataset, conduct pre-processing, train our model with several hyper-parameters to get the best accuracy, then test the model using the new data. Based on experimental results, the proposed model can produce a 0.00539 error rate and indicate a more accurate model to detect anomalies in the network traffic.
Detecting Acute Liver Diseases Using CNN Algorithm Sarah Anjani; Maria Yohana Jawa Betan
International Journal of Informatics Engineering and Computing Vol. 1 No. 2 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i2.45

Abstract

This study tackles the critical challenge of detecting Acute Liver Failure (ALF) using machine learning algorithms. The main goal is to assess the effectiveness of several algorithms, including Convolutional Neural Network (CNN), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting, in accurately classifying cases of ALF. For this purpose, a comprehensive dataset with 8,785 records and 30 features from Kaggle is utilized, involving thorough preprocessing steps like feature selection, data cleaning, and normalization. The research emphasizes achieving high precision in ALF detection. Results show that CNN outperforms other algorithms, achieving a precision score of 1.00 for identifying ALF cases, demonstrating its high reliability. This study highlights the importance of algorithm selection in complex medical diagnoses, showcasing the potential of deep learning methods in healthcare and paving the way for more accurate and timely ALF detection to improve patient outcomes.
Co-Authors Fan Haoyi Maria Yohana Jawa Betan