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All Journal International Journal of Electrical and Computer Engineering Jurnal ULTIMATICS JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI
Overbeek, Marlinda Vasty
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Public Health

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Three layer hybrid learning to improve intrusion detection system performance Harwahyu, Ruki; Erasmus Ndolu, Fajar Henri; Overbeek, Marlinda Vasty
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1691-1699

Abstract

In imbalanced network traffic, malicious cyberattacks can be hidden in a large amount of normal traffic, making it difficult for intrusion detection systems (IDS) to detect them. Therefore, anomaly-based IDS with machine learning is the solution. However, a single machine learning cannot accurately detect all types of attacks. Therefore, a hybrid model that combines long short-term memory (LSTM) and random forest (RF) in three layers is proposed. Building the hybrid model starts with Nearmiss-2 class balancing, which reduces normal samples without increasing minority samples. Then, feature selection is performed using chi-square and RF. Next, hyperparameter tuning is performed to obtain the optimal model. In the first and second layers, LSTM and RF are used for binary classification to detect normal data and attack data. While the third layer model uses RF for multiclass classification. The hybrid model verified using the CSE-CIC-IDS2018 dataset, showed better performance compared to the single algorithm. For multiclass classification, the hybrid model achieved 99.76% accuracy, 99.76% precision, 99.76% recall, and 99.75% F1-score.
U-TAPIS Sal-Tik : Typing Error Detection Using Random Forest Algorithm Overbeek, Marlinda Vasty; Glennardy, Bryan; Mediyawati, Niknik; Nusantara, Samiaji Bintang; Sutomo, Rudi
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3563

Abstract

The development of technology in the field of journalism has grown very rapidly. However, in the field of journalism there are still frequent deviations from the language on online news portals. This can be seen from the aspect of spelling and word usage. Spelling mistakes that occur in the news can cause the information contained in the news to be unclear and ambiguous. Based on these problems, a study was conducted to create a model to detect type error in Indonesian. This model is created using the random forest algorithm. random forest is an algorithm that works by building several decision trees and then combining the decisions from each tree that has been built and taking the most votes from the predictions of each tree so that it will produce stable and accurate predictions. The results of the accuracy of the model in the research that has been done is 100%. However, it should be noted that this 100% result is that the model is able to detect words that are already contained in the dataset. Based on the evaluation results that have been carried out, because the detected word is contained in the dataset, the accuracy issued is 100%. The built model successfully detects type error in Tribunnews news articles.
Sistem Identifikasi Titik Kritis Halal Menggunakan Algoritma Forward Chaining Hin, Alexander Moya; Kusnadi, Adhi; Overbeek, Marlinda Vasty; Prawira, Oqke; Khaeruzzaman, Yaman; Prasetya, Syarief Gerald
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 8, No 1 (2023): Januari 2023
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v8i1.1285

Abstract

Halal products are obligatory to be used by people who are Muslim. When viewed in terms of the number of the Muslim population in the world and Indonesia, halal products have very potential economic opportunities. However, halal products have the risk of becoming non-halal if the accompanying process and storage do not follow halal rules. Therefore, it is necessary to identify the critical halal point, the point where the potential for such change occurs. So far, identification is made manually, of course there will be opportunities for identification errors to happen and it will take a relatively long time. To overcome these problems, identification can use a computer-based system. Forward chaining is an algorithm that is suitable for identifying halal points, because in SJH LPPOM MUI there is a decision tree for identifying halal critical points which is carried out in the same forward sequence as the forward chaining algorithm process flow. The development of a halal critical point identification system is carried out using the Software Development Life Cycle V-model method, the PHP programming language and the MySQL Database Management System. The system was successfully tested using Whitebox testing, including unit testing, integration testing, and overall system testing. Then testing using Blackbox testing techniques by comparing the results of identifying critical points using the system with the results of identifying critical points manually producing the same results. User satisfaction testing was also carried out using the End User Computing Satisfaction method and obtained an average satisfaction score of 86.53%Keywords – halal products, critical halal point, AI, forward chaining
Co-Authors Erasmus Ndolu, Fajar Henri Glennardy, Bryan Harwahyu, Ruki Hin, Alexander Moya Khaeruzzaman, Yaman kusnadi, adhi Mediyawati, Niknik Nusantara, Samiaji Bintang Prasetya, Syarief Gerald Prawira, Oqke Sutomo, Rudi