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All Journal ELKHA : Jurnal Teknik Elektro Jurnas Nasional Teknologi dan Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika 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 Mantik Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika
Wildanil Ghozi
Universitas Dian Nuswantoro
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences

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IMPROVING MALWARE DETECTION USING INFORMATION GAIN AND ENSEMBLE MACHINE LEARNING Ramadhani, Arsabilla; Rafrastara, Fauzi Adi; Rosyada, Salma; Ghozi, Wildanil; Osman, Waleed Mahgoub
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3903

Abstract

Malware attacks pose a serious threat to digital systems, potentially causing data and financial losses. The increasing complexity and diversity of malware attack techniques have made traditional detection methods ineffective, thus AI-based approaches are needed to improve the accuracy and efficiency of malware detection, especially for detecting modern malware that uses obfuscation techniques. This study addresses this issue by applying ensemble-based machine learning algorithms to enhance malware detection accuracy. The methodology used involves Random Forest, Gradient Boosting, XGBoost, and AdaBoost, with feature selection using Information Gain. Datasets from VirusTotal and VxHeaven, including both goodware and malware samples. The results show that Gradient Boosting, strengthened with Information Gain, achieved the highest accuracy of 99.1%, indicating a significant improvement in malware detection effectiveness. This study demonstrates that applying Information Gain to Gradient Boosting can improve malware detection accuracy while reducing computational requirements, contributing significantly to the optimization of digital security systems.
Integrating Ensemble Learning and Information Gain for Malware Detection based on Static and Dynamic Features Sani, Ramadhan Rakhmat; Rafrastara, Fauzi Adi; Ghozi, Wildanil
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2051

Abstract

The rapid advancement of malware poses a significant threat to devices, like personal computers and mobile phones. One of the most serious threats commonly faced is malicious software, including viruses, worms, trojan horses, and ransomware. Conventional antivirus software is becoming ineffective against the ever-evolving nature of malware, which can now take on various forms like polymorphic, metamorphic, and oligomorphic variants. These advanced malware types can not only replicate and distribute themselves, but also create unique fingerprints for each offspring. To address this challenge, a new generation of antivirus software based on machine learning is needed. This intelligent approach can detect malware based on its behavior, rather than relying on outdated fingerprint-based methods. This study explored the integration of machine learning models for malware detection using various ensemble algorithms and feature selection techniques. The study compared three ensemble algorithms: Gradient Boosting, Random Forest, and AdaBoost. It used Information Gain for feature selection, analyzing 21 features. Additionally, the study employed a public dataset called ‘Malware Static and Dynamic Features VxHeaven and VirusTotal Data Set’, which encompasses both static and dynamic malware features. The results demonstrate that the Gradient Boosting algorithm combined with Information Gain feature selection achieved the highest performance, reaching an accuracy and F1-Score of 99.2%.
Impact of SMOTE for Imbalance Class in DDoS Attack Detection Using Deep Learning MLP Ilma, Zidni; Ghozi, Wildanil; Rafrastara, Fauzi Adi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6727

Abstract

DDoS attacks, which are becoming increasingly complex and frequent, pose significant challenges to network security, particularly with the rise of cyber exploitation of infrastructure. A major issue in detecting these attacks is the imbalance between normal traffic and attack data, which causes machine learning models to be biased toward the majority class. To address this, this study proposes the use of the Synthetic Minority Over-sampling Technique (SMOTE) to balance the CIC-DDoS2019 dataset, successfully enhancing the performance of a Multi-Layer Perceptron (MLP) in detecting various types of attacks. Analysis results indicate that, on the original dataset without SMOTE, the model achieved high accuracy but low F1-Score for minority classes, highlighting difficulties in recognizing underrepresented attack patterns. After applying SMOTE, the F1-Score significantly improved for minority classes, demonstrating the model's enhanced ability to identify attack patterns. All dataset subsets showed improved performance across key evaluation metrics, indicating that SMOTE effectively expanded the model's decision boundary for minority classes, enabling MLP to detect DDoS attacks more accurately in previously challenging data patterns. This approach illustrates increased model sensitivity to minority feature distributions without significantly compromising performance on majority classes.
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.
Mitigating Class Imbalance in DDoS Detection: The Impact of Random Over Sampling on Machine Learning Performance Ghozi, Wildanil; Hussein, Jasim Nadheer; Sani, Ramadhan Rakhmat; Rafrastara, Fauzi Adi; Paramita, Cinantya; Supriyanto, Catur
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.95037

Abstract

Distributed Denial of Service (DDoS) attacks are a major cybersecurity threat, involving malicious traffic generated from numerous compromised sources to overwhelm and disable targeted services. Although machine learning (ML) has shown promise in detecting DDoS attacks through network traffic analysis, a key challenge remains: the class imbalance in datasets such as UNSW-NB15, where normal traffic significantly outweighs attack instances. This imbalance leads to biased predictions and degraded detection performance for minority attack classes. To address this issue, our study investigates the impact of Random Over Sampling (ROS), a simple yet effective balancing technique on improving detection accuracy in multi-class DDoS classification tasks. While prior works have primarily focused on ensemble algorithms or feature selection, our approach is distinct in emphasizing the effect of data balancing on macro evaluation metrics such as macro precision, macro recall, and macro F1-score. ROS was selected over more complex alternatives, such as SMOTE or ADASYN, due to its computational efficiency and ability to establish a performance baseline without introducing synthetic noise. We evaluate four machine learning algorithms: Decision Tree, Naïve Bayes, Random Forest, and XGBoost, using the UNSW-NB15 dataset. The results show that Decision Tree combined with ROS yields the highest improvement in macro F1-score, increasing by 36%. However, this improvement is accompanied by a moderate reduction in accuracy for certain algorithms. These findings highlight the critical role of class balancing in enhancing the reliability of DDoS detection models, especially in imbalanced multi-class scenarios.
Co-Authors Abdus Salam, Abdus Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Agus Winarno Ajib Susanto Alomoush, Ashraf Anargya, Muhammad Alden Nayef Ardi Saputra Bagus Nurseto, Muhammad Narendra Bintoro, Jevan Catur Supriyanto Chaerul Umam Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Fahmi Bahtiar Adi Nugroho Fauzi Adi Rafrastara Gustina Alfa Trisnapradika Hamadeh, Emad Heribertus Himawan Heru Lestiawan Hussein, Jasim Nadheer Hussein, Jassim Nadheer Ilma, Zidni L. Budi Handoko Latifah, Ines Aulia Mohamed Yassin, Warusia Oki Setiono Osman, Waleed Mahgoub Paramita, Cinantya Pratama, Andriyan Yoga Putra Hartanto, Octaviano Ryan Eka Rahmawati, Firnanda Ramadhan Rakhmat Sani Ramadhani, Arsabilla Retno Astuti Setijaningsih Ricardus Anggi Pramunendar Rosyada, Salma Saputra, Ardi Solichul Huda Tyas, Wahyu Suryaning Vicola Nanda Pratama Widyanto Utomo, Arya Yani Parti Astuti Yassin, Warusia Yuminah Yuminah