Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Computer Science and Information Technologies
Rahmat Budiarto
Al-Baha University
Computer Science & IT Engineering

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

Found 3 Documents
Search

 1 
Machine learning model approach in cyber attack threat detection in security operation center Muhammad Ajran Saputra; Deris Stiawan; Rahmat Budiarto
Computer Science and Information Technologies Vol 6, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i1.p80-90

Abstract

The evolution of technology roles attracted cyber security threats not only compromise stable technology but also cause significant financial loss for organizations and individuals. As a result, organizations must create and implement a comprehensive cybersecurity strategy to minimize further loss. The founding of a cybersecurity surveillance center is one of the optimal adopted strategies, known as security operation center (SOC). The strategy has become the forefront of digital systems protection. We propose strategy optimization to prevent or mitigate cyberattacks by analyzing and detecting log anomalies using machine learning models. This study employs two machine learning models: the naïve Bayes model with Multinomial, Gaussian, and Bernoulli variants, and the support vector machine (SVM) model with radial basis function (RBF), linear, polynomial, and sigmoid kernel variants. The hyperparameters in both models are then optimized. The models with optimized hyperparameters are subsequently trained and tested. The experimental results indicate that the best performance is achieved by the RBF kernel SVM model, with an accuracy of 79.75%, precision of 80.8%, recall of 79.75%, and F1-score of 80.01%; and the Gaussian naïve Bayes model, with an accuracy of 70.0%, precision of 80.27%, recall of 70.0%, and F1-score of 70.66%. Overall, both models perform relatively well and are classified in the very good category (75%‒89%).
Clustering man in the middle attack on chain and graph-based blockchain in internet of things network using k-means Sari Nuzulastri; Deris Stiawan; Hadipurnawan Satria; Rahmat Budiarto
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p176-185

Abstract

Network security on internet of things (IoT) devices in the IoT development process may open rooms for hackers and other problems if not properly protected, particularly in the addition of internet connectivity to computing device systems that are interrelated in transferring data automatically over the network. This study implements network detection on IoT network security resembles security systems from man in the middle (MITM) attacks on blockchains. Security systems that exist on blockchains are decentralized and have peer to peer characteristics which are categorized into several parts based on the type of architecture that suits their use cases such as blockchain chain based and graph based. This study uses the principal component analysis (PCA) to extract features from the transaction data processing on the blockchain process and produces 9 features before the k-means algorithm with the elbow technique was used for classifying the types of MITM attacks on IoT networks and comparing the types of blockchain chain-based and graph-based architectures in the form of visualizations as well. Experimental results show 97.16% of normal data and 2.84% of MITM attack data were observed.
Comparative Study of Machine Learning Algorithms Using Bagging and XGBoost Techniques for Breast Cancer Classification Rully Pramudita; Dwi Ismiyana Putri; Bambang Kriswantara; Vina Zahrotun Nazah; Rahmat Budiarto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7216

Abstract

Machine learning (ML) has become an important data-driven approach for classification and prediction, including applications in medical diagnosis. Ensemble methods can improve classifier performance by combining complementary learning mechanisms. However, systematic evidence on the sequential use of Bagging and XGBoost across different classifier architectures remains limited. This study develops a staged ensemble framework in which five classifiers—Support Vector Machine (SVM), Neural Network (NN), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbours (KNN)—are first optimized through Bagging and subsequently enhanced using XGBoost. The experiments were conducted on the Breast Cancer Wisconsin (Diagnostic) dataset under a consistent 70:30 train–test protocol. Performance was assessed using accuracy, confusion matrices, ROC curves, and Area Under the Curve (AUC), while repeated experiments were used to examine statistical significance. The results show that the staged Bagging–XGBoost approach improves both predictive accuracy and class discrimination across the evaluated classifier types. Neural Network achieved the largest improvement, with mean accuracy increasing from 93.1% to 97.0% across repeated experiments. The findings demonstrate that the sequential framework can improve non-tree-based as well as tree-based classifiers, providing empirical evidence for broader use of staged ensemble integration in breast cancer classification.
Co-Authors Bambang Kriswantara Deris Stiawan Dwi Ismiyana Putri Hadipurnawan Satria Muhammad Ajran Saputra Rully Pramudita Sari Nuzulastri Vina Zahrotun Nazah