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All Journal ELKHA : Jurnal Teknik Elektro Semantik Proceeding SENDI_U JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika
Fauzi Adi Rafrastara
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Balancing CICIoV2024 Dataset with RUS for Improved IoV Attack Detection Firmansyah, Muhammad David; Rizqa, Ifan; Rafrastara, Fauzi Adi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9079

Abstract

This study addresses the cybersecurity challenges within the Internet of Vehicles (IoV) by exploring the efficacy of Random Under-Sampling (RUS) in balancing the class distribution of the CICIoV2024 dataset for improved intrusion detection. IoV technology connects vehicles to digital infrastructure, fostering communication and enhancing safety but is simultaneously vulnerable to cyber threats such as Denial of Service (DoS) and spoofing attacks. This research employed RUS to mitigate data imbalance within the CICIoV2024 dataset, which often impedes effective threat detection in machine learning models. Four machine learning classifiers Random Forest, AdaBoost, Gradient Boosting, and XGBoost were evaluated on both imbalanced and balanced datasets to compare their performance. Results demonstrated that RUS significantly enhances model accuracy, precision, recall, and F1-score, reaching perfect scores across all classifiers post-balancing. Additionally, RUS contributed to substantial reductions in training and testing times, thereby boosting computational efficiency. These findings underscore the potential of RUS in addressing data imbalance in IoV cybersecurity, establishing a foundation for future research aimed at safeguarding IoV systems against evolving cyber threats.
Improving Attack Detection in IoV with Class Balancing and Feature Selection Widyatama, Thierry; Rizqa, Ifan; Rafrastara, Fauzi Adi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9080

Abstract

The Internet of Vehicles (IoV) represents a specialized application of the Internet of Things (IoT), enabling vehicles to communicate with their surrounding infrastructure to enhance transportation safety and efficiency. However, IoV systems are susceptible to various cyberattacks, including Denial of Service (DoS) and spoofing attacks, which necessitate effective and efficient detection mechanisms. This study investigates the enhancement of detection efficiency for DoS and spoofing attacks in IoV by employing Ensemble Learning methods combined with feature selection techniques. The selected feature selection methods include Information Gain Ratio, Chi-Square (X²), and Fast Correlation-Based Filter (FCBF). The CICIoV2024 dataset, utilized in this study, was balanced using the Random Under Sampling technique to address data imbalance issues. The ensemble algorithms evaluated in this research comprise Random Forest, Gradient Boosting, and XGBoost. Results indicate that all three algorithms achieved high accuracy and F1 scores, reaching 0.985. Moreover, the application of feature selection significantly reduced computational time without compromising detection performance. These findings are expected to contribute to the advancement of IoV security systems in the future.
Model Hybrid Random Forest dan Information Gain untuk meningkatkan Performa Algoritma Machine Learning pada Deteksi Malicious Software Rafrastara, Fauzi Adi; Ghozi, Wildanil; Sani, Ramadhan Rakhmat; Handoko, L. Budi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

The evolution of malware, or malicious software, has raised increasing concerns, targeting not only computers but also other devices like smartphones. Malware is no longer just monomorphic but has evolved into polymorphic, metamorphic, and oligomorphic forms. With this massive development, conventional antivirus software is becoming less effective at countering it. This is due to malware's ability to propagate itself using different fingerprint and behavioral patterns. Therefore, an intelligent machine learning-based antivirus is needed, capable of detecting malware based on behavior rather than fingerprints. This research focuses on the implementation of a machine learning model for malware detection using ensemble algorithms and feature selection to achieve optimal performance. The ensemble algorithm used is Random Forest, evaluated and compared with k-Nearest Neighbor and Decision Tree as state-of-the-art methods. To enhance classification performance in terms of processing speed, the feature selection method applied is Information Gain, with 22 features. The highest results were achieved using the Random Forest algorithm and Information Gain feature selection method, reaching a score of 99.0% for accuracy and F1-Score. By reducing the number of features, processing speed can be increased by almost fivefold.
Deteksi Serangan Denial of Service (DoS) dan Spoofing pada Internet of Vehicles menggunakan Algoritma K-Nearest Neighbor (KNN) Ghozi, Wildanil; Rafrastara, Fauzi Adi; Sani, Ramadhan Rakhmat; Abdussalam, Abdussalam
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

The implementation of Internet of Things (IoT) technology in motor vehicles has been increasing over time and is known as the Internet of Vehicles (IoV). IoV is becoming more essential to society as it provides comfort, safety, and efficiency in driving. Unfortunately, the use of internet technology in IoV brings the potential for cyber-attacks, such as Denial of Service (DoS) and Spoofing. Intrusion Detection Systems in IoV have not yet fully matured, as this technology is still relatively new. Therefore, the potential threats and their significant impact make research on this topic urgently needed. This study aims to evaluate the performance of the k-Nearest Neighbor (kNN) classification algorithm in detecting cyber-attacks on IoV. The predicted classes in this study consist of six categories: Benign, DoS, Gas-Spoofing, Steering Wheel-Spoofing, Speed-Spoofing, and RPM-Spoofing. These two types of attacks on IoV (DoS and Spoofing) pose risks to the operational safety of vehicles, which can endanger drivers and other road users. The dataset used is a public dataset called CIC IoV2024. The performance of the kNN algorithm is also compared to three other state-of-the-art algorithms, including Naïve Bayes, Deep Neural Network, and Random Forest. The results show that k-Nearest Neighbor (kNN) achieved the best performance with a score of 98.7% for both accuracy and F1-Score metrics. kNN outperformed Naïve Bayes, which ranked second with a score of 98.1% accuracy and 98.0% F1-Score. Thus, the kNN algorithm can be recommended as a classifier in the development of an intrusion detection system for IoV
Enhancing Fraud Detection Performance in E-Commerce Platforms Using Gradient Boosting Algorithms Saputra, Ardi; Rafrastara, Fauzi Adi; Ghozi, Wildanil
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/s8q6t594

Abstract

The rapid growth of e-commerce has attracted many users. However, as transaction volumes increase, so do cases of fraud. This not only causes financial losses for sellers but also threatens the trust that is so important in the e-commerce industry. Previous studies have used the Naïve Bayes and Multilayer Perceptron algorithms to detect fraud in e-commerce with accuracy percentages of 95.00% and 94.00%, respectively, without other assessment measures, including precision, recall, and F1-score. This research seeks to create a predictive model for the likelihood of online sales fraud by comparing Gradient Boosting, Neural Network, Random Forest, and Naïve Bayes models through feature extraction and feature scaling pre-processing, with 10-fold cross-validation. The dataset used was taken from the Kaggle platform. The features included in the dataset include buyer characteristics, products sold, transaction volume, devices used, and other fraud indicators. The study's findings demonstrate that the Gradient Boosting algorithm excels in detecting fraud risk with an accuracy rate of 95.30%, precision of 94.10%, recall of 95.30%, and an F1-score of 93.80%.  These findings are anticipated to enhance the development of more efficient e-commerce security solutions.
Enhancing XGBoost Performance in Malware Detection through Chi-Squared Feature Selection Rosyada, Salma; Rafrastara, Fauzi Adi; Ramadhani, Arsabilla; Ghozi, Wildanil; Yassin, Warusia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2293

Abstract

The increasing prevalence of malware poses significant risks, including data loss and unauthorized access. These threats manifest in various forms, such as viruses, Trojans, worms, and ransomware. Each continually evolves to exploit system vulnerabilities. Ransomware has seen a particularly rapid increase, as evidenced by the devastating WannaCry attack of 2017 which crippled critical infrastructure and caused immense economic damage. Due to their heavy reliance on signature-based techniques, traditional anti-malware solutions struggle to keep pace with malware's evolving nature. However, these techniques face limitations, as even slight code modifications can allow malware to evade detection. Consequently, this highlights weaknesses in current cybersecurity defenses and underscores the need for more sophisticated detection methods. To address these challenges, this study proposes an enhanced malware detection approach utilizing Extreme Gradient Boosting (XGBoost) in conjunction with Chi-Squared Feature Selection. The research applied XGBoost to a malware dataset and implemented preprocessing steps such as class balancing and feature scaling. Furthermore, the incorporation of Chi-Squared Feature Selection improved the model's accuracy from 99.1% to 99.2% and reduced testing time by 89.28%, demonstrating its efficacy and efficiency. These results confirm that prioritizing relevant features enhances both the accuracy and computational speed of the model. Ultimately, combining feature selection with machine learning techniques proves effective in addressing modern malware detection challenges, not only enhancing accuracy but also expediting processing times.             
Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset Latifah, Ines Aulia; Rafrastara, Fauzi Adi; Bintoro, Jevan; Ghozi, Wildanil; Osman, Waleed Mahgoub
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2294

Abstract

Malware, or malicious software, continues to evolve alongside increasing cyberattacks targeting individual devices and critical infrastructure. Traditional detection methods, such as signature-based detection, are often ineffective against new or polymorphic malware. Therefore, advanced malware detection methods are increasingly needed to counter these evolving threats. This study aims to compare the performance of various feature selection methods combined with the XGBoost algorithm for malware detection using the Drebin dataset, and to identify the best feature selection method to enhance accuracy and efficiency. The experimental results show that XGBoost with the Information Gain method achieves the highest accuracy of 98.7%, with faster training times than other methods like Chi-Squared and ANOVA, which each achieved an accuracy of 98.3%. Information Gain yielded the best performance in accuracy and training time efficiency, while Chi-Squared and ANOVA offered competitive but slightly lower results. This study highlights that appropriate feature selection within machine learning algorithms can significantly improve malware detection accuracy, potentially aiding in real-world cybersecurity applications to prevent harmful cyberattacks.
Performance Improvement of Machine Learning Algorithm using PCA on IoV Attack Putra Hartanto, Octaviano Ryan Eka; Ghozi, Wildanil; Rafrastara, Fauzi Adi; Paramita, Cinantya
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8064

Abstract

In the transportation industry, the Internet of Vehicles (IoV) is an advancement of the Internet of Things (IoT), allowing automobiles to connect to networks to provide a range of features. This connectivity transforms traditional vehicles into intelligent systems, fostering innovations like autonomous driving and traffic optimization. However, this increased connectivity exposes IoV to cybersecurity threats, particularly because the networks utilized are often public and lack robust security measures. Cyberattacks targeting IoV can involve data packet modification, traffic flooding, or spoofing, potentially disabling critical vehicle components, compromising passenger safety, and increasing the risk of accidents. Consequently, accurate and efficient attack detection systems are essential to counter these threats and ensure IoV security. This study leverages the CICIoV2024 dataset and applies Principal Component Analysis (PCA) to enhance computational efficiency in detecting IoV attacks. The algorithms employed in this research include Random Forest, AdaBoost, Logistic Regression, and Deep Neural Networks. Experimental results demonstrate that implementing PCA significantly improves computational efficiency across all algorithms while maintaining consistent accuracy and F1-Score, highlighting its effectiveness in securing IoV systems. 
Pelatihan Desain Poster Promosi untuk UMKM Binaan Dinsospermasdes Kabupaten Jepara Ghozi, Wildanil; Prabowo, Dwi Puji; Rafrastara, Fauzi Adi; Pramunendar, Ricardus Anggi; Sani, Ramadhan Rakhmat
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 8, No 3 (2025): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v8i3.8379

Abstract

Internet sebagai salah satu dorongan utama dalam perkembangan teknologi memungkinkan setiap manusia untuk menjangakau informasi tanpa batasan ruang dan waktu. Saat ini di Indonesia, lebih dari 196,7 juta penduduk memanfaatkan internet dalam aktivitasnya sehari-hari. Pada provinsi Jawa Tengah terdapat 25,6 juta pengguna internet aktif. Tingginya pengguna internet menjadi peluang untuk memperluas target pemasaran produk UMKM. Pemerintah Kabupaten Jepara, melalui Dinas Sosial, Pemberdayaan Masyarakat dan Desa (Dinsospermasdes) Kabupaten Jepara memiliki tanggung jawab dalam program rehabilitasi Penyandang Masalah Kesejahteraan Sosial (PMKS) dimana salah satu programnya adalah pembinaan UMKM. Saat ini, UMKM binaan Dinsospermasdes belum mampu membuat desain poster promosi yang baik dan menarik pembeli. Penulis memberikan pelatihan desain poster dengan menggunakan aplikasi canva kepada para pelaku UMKM binaan. Pelatihan tersebut telah berhasil meningkatkan pemahaman konsep desain, kemampuan pengambilan foto produk, dan kemampuan membuat desain poster promosi para pelaku UMKM. Poster-poster baru yang dihasilkan pada kegiatan pelatihan menjadi indikator keberhasilan para peserta mengikuti pelatihan. Dengan demikian, diharapkan kemampuan yang telah dimiliki dapat membantu meningkatkan penjualan produk UMKM binaan.
XGBoost-Powered Ransomware Detection: A Gradient-Based Machine Learning Approach for Robust Performance Ghozi, Wildanil; Lestiawan, Heru; Sani, Ramadhan Rakhmat; Hussein, Jassim Nadheer; Rafrastara, Fauzi Adi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2405

Abstract

Ransomware remains a rapidly evolving cyber threat, causing substantial financial and operational disruptions globally. Traditional signature-based detection systems are ineffective against sophisticated, zero-day attacks due to their static nature. Consequently, machine learning-based approaches offer a more effective and adaptive alternative. This study proposes an approach utilizing XGBoost for highly effective ransomware detection. We conducted a rigorous comparative analysis of prominent ensemble learning algorithms—XGBoost, Random Forest, Gradient Boosting, and AdaBoost—on the RISS Ransomware Dataset, comprising 1,524 instances. Our experimental results unequivocally demonstrate XGBoost as the superior ensemble model, achieving an impressive 97.60% accuracy and F1-Score. This performance surpassed Gradient Boosting (97.20%), Random Forest (96.94%), and AdaBoost (96.50%). Furthermore, this study benchmarked XGBoost against established state-of-the-art (SOTA) methods, including Support Vector Machine (SVM) and the SA-CNN-IS deep learning approach. The comprehensive results underscore the core contribution of this study: by applying XGBoost with a carefully structured machine learning pipeline, our approach consistently outperforms two state-of-the-art methods (SVM and SA-CNN-IS) as well as other ensemble algorithms. This highlights the critical role of methodological precision in maximizing detection performance against evolving ransomware threats.
Co-Authors Abdollah, Mohd. Faizal Abdussalam Abdussalam, Abdussalam Adhitya Nugraha Adhitya Nugraha Ahmed, Foez Al Fahreza, Muhammad Daffa Amalia, Syafira Rosa Amiral, Afinzaki Anargya, Muhammad Alden Nayef Astuti, Yani Parti Bintoro, Jevan Catur Supriyanto Catur Supriyanto Catur Supriyanto Cinantya Paramita Cinantya Paramita Dewi Pergiwati Dwi Eko Waluyo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Etika Kartikadarma Firmansyah, Muhammad David Foez Ahmed Ghozi, Wildani Hayu Wikan Kinasih Heru Lestiawan Hussein, Jasim Nadheer Hussein, Jassim Nadheer Ifan Rizqa Ilma, Zidni Kencana, Lisdi Inu L. Budi Handoko Latifah, Ines Aulia Lisdi Inu Kencana Mahendra, Syafrie Naufal Megantara, Rama Aria Nohan, Rajendra Osman, Waleed Mahgoub Paramita, Cinantya Pergiwati, Dewi Pulung Nurtantio Andono Putra Hartanto, Octaviano Ryan Eka Ramadhan Rakhmat Sani Ramadhani, Arsabilla Ricardus Anggi Pramunendar Rosyada, Salma Saputra, Ardi Sudibyo, Usman T. Sutojo Usman Sudibyo Widyatama, Thierry Wildanil Ghozi Yani Parti Astuti Yani Parti Astuti Yassin, Warusia