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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Informatika dan Komputer
Ery Permana Yudha
Department Of Informatics, Faculty Of Information Technology And Data Science, Sebelas Maret University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Design and evaluation of a secure key exchange protocol using the Kyber-Dilithium algorithm Bambang Harjito; Muhammad Defaroyan; Fajar Muslim; Ery Permana Yudha; Endra Pratama
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11157

Abstract

Over 90% of the billions of people who use the internet globally use it through the transport layer security (TLS) protocol. TLS is a security standard that performs network authentication and data encryption when accessing the internet. Authenticated key exchange (AKE) is the protocol TLS uses for network authentication and key establishment during the TLS Handshake process. The AKE protocol utilizes a public key cryptosystem (PKC) and digital signatures with algorithms commonly used, namely elliptic curve cryptography (ECC) and Rivest-Shamir-Adleman (RSA). Future advancements in quantum computing may compromise the security of the widely used ECC and RSA algorithms. This research conducts an implementation and comparative analysis of post-quantum algorithms resistant to quantum computer attacks, specifically Kyber-Dilithium, in the context of the AKE protocol. The implementation is performed at three security levels: 128-bit, 192-bit, and 256-bit. The results show that the Kyber-Dilithium is greater than those of the RSA variant and much larger than those of the ECC variant. In contrast to the ECC and RSA variants, the Kyber-Dilithium algorithm variants perform better across all security levels, even if their byte sizes are greater.
Intrusion Detection For Network Security Using Information Gain Filters On Deep Neural Networks Ery Permana Yudha; Bambang Harjito; Endra Pratama
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 3 (2026): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i3.2641

Abstract

In recent years, technologies such as big data, cloud computing, and internet networks have grown significantly. Technological advancements are also accompanied by a growth in the number of users of internet-based services, such as cloud services, which grows annually. This growth in user numbers and technological advancements increase the opportunity for cyberattacks through networks, such as theft of user data or information. Therefore, an intrusion detection system is needed as a preventive measure against cyberattacks to protect information on the network. Intrusion detection prevents cyberattacks by using machine learning, which can work effectively in heavy network traffic. Designing an optimal intrusion detection system requires various approaches, such as feature selection and the selected machine learning model. In this study, feature selection was carried out using the filter method, wrapper method, and embedded method. The filter method uses Information Gain (IG) and the wrapper method uses Recursive Feature Elimination (RFE). Then, it was tested with deep learning-based machine learning models such as Deep Neural Network (DNN), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and with traditional machine learning models such as Random Forest (RF) and Logistic Regression (LR). In this research, we contribute by proposing a comprehensive comparative study that evaluates multiple feature selection method and machine learning models to identify the most effective combination for improving intrusion detection system performance. In this study, DNN was able to produce the highest average accuracy of 87.38%. This was followed by MLP, LSTM, and Random Forest with 87.28%, 86.48%, and 86.08%, respectively. Furthermore, the Logistic Regression model had the lowest accuracy value, at 72.34%. Furthermore, the best feature selection method, on average, was the wrapper method, providing a 0.14% improvement compared to the baseline.
Optimization of User-Based Collaborative Filtering Movie Recommendation System Using Mean-Centering and Overlap Weighting on Cosine Similarity Arif Rohmadi; Ery Permana Yudha; Bambang Widoyono
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3315

Abstract

Rapid technological advancements have changed the way people enjoy entertainment. Viewers can now watch movies online without having to go to the cinema. The rapid development of online movie streaming services has increased the need for systems capable of providing content recommendations based on user preferences. This study aims to optimize a User-Based Collaborative Filtering (UBCF)-based recommendation system using the MovieLens 100K dataset containing 100,000 ratings from 943 users for 1,682 movies. The evaluation was conducted by dividing the data into 80% training data and 20% testing data. The system was optimized through two main approaches: user average normalization (mean-centering) in the KNN method to reduce bias in rating scale differences between users, and the application of overlap weighting on cosine similarity to give greater weight to user pairs with a greater number of shared item ratings. Based on experiments, a value of k = 50 was chosen as the optimal trade-off point in predicting ratings. The experimental results show that KNN with mean centering (KNNWithMeans) consistently outperforms standard KNN. The cosine + KNNWithMeans model produced an RMSE of 0.9701 and an MAE of 0.7567, lower than cosine + KNN (RMSE 1.0377; MAE 0.8226). Further overlap weighting was shown to improve prediction accuracy, with the combination of weighted cosine with α = 1 and KNNWithMeans providing the best performance with an RMSE of 0.9686 and an MAE of 0.7556.
Co-Authors Arif Rohmadi Bambang Harjito Bambang Harjito, Bambang Bambang Widoyono Endra Pratama Fajar Muslim Muhammad Defaroyan