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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Manajemen Sistem Informasi
Sharipuddin, Sharipuddin
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Computer Science & IT Control & Systems Engineering Engineering

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Analisis dan Perancangan Sistem Informasi Penilaian Kinerja Guru Yayasan Rusyda Medi Andri Medan Takhir, Said Hambali; Jasmir, Jasmir; Sharipuddin, Sharipuddin
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 4 No. 1 (2025): EDISI JANUARI 2025
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v4i1.10573

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem informasi penilaian kinerja guru berbasis web yang diterapkan di Yayasan Rusyda Medi Andri Medan. Sistem ini dirancang untuk memberikan penilaian yang lebih objektif, transparan, dan efisien. Sistem Informasi Penilaian Guru dirancang untuk menghasilkan peringkat kinerja guru secara terstruktur. Proses pengembangan sistem mengikuti metode waterfall yang melibatkan analisis kebutuhan, perancangan sistem, implementasi, dan pengujian menggunakan black-box testing. Sistem berbasis web ini dibangun menggunakan Balsamiq. Hasil implementasi menunjukkan bahwa sistem mampu memberikan rekomendasi penilaian secara lebih akurat dibandingkan metode manual sebelumnya, mengurangi risiko subjektivitas, dan meningkatkan kepercayaan para guru terhadap hasil evaluasi. Sistem ini diharapkan dapat membantu Yayasan Rusyda Medi Andri Medan dalam mengelola proses penilaian kinerja guru secara lebih efektif, mendukung kebijakan promosi dan penghargaan, serta meningkatkan kualitas pendidikan melalui pengembangan profesionalisme tenaga pengajar.
Two-steps feature selection for detection variant distributed denial of services attack in cloud environment Kurniabudi, Kurniabudi; Winanto, Eko Arip; Sharipuddin, Sharipuddin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3945-3957

Abstract

The prevalence of cloud computing among organizations poses a significant problem in ensuring security. Specifically, distributed denial of services (DDoS) attacks targeting cloud computing networks can lead to financial losses for consumers of cloud computing services. This assault has the potential to render cloud services inaccessible. The detection system serves as a remedy to prevent more substantial losses. This research aims to enhance the efficacy of the system detection model by integrating feature selection with three machine learning algorithms: decision tree (DT), random forest (RF), and naïve Bayes (NB). Therefore, our study suggests combining two phases of feature selection into the DDoS attack detection procedure. The first phase uses the information gain (IG) feature selection technique approach, and the second phase uses the principal component analysis (PCA) feature extraction approach. The technique is referred to as two-step feature selection. The test findings indicate that the implementation of two-step feature selection can enhance the performance of the DT and RF detection models by around 9%.
RNN-Based Intrusion Detection System for Internet of Vehicles with IG, PCA, and RF Feature Selection Purnama, Benni; Winanto, Eko Arip; Sharipuddin, Sharipuddin; Sandra, Dodi; Nurhadi, Nurhadi; Afuan, Lasmedi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5293

Abstract

Cyberattacks in the Internet of Vehicles (IoV) threaten road safety and data integrity, requiring intrusion detection systems (IDS) that capture temporal patterns in vehicular traffic. This study develops a Recurrent Neural Network (RNN)-based IDS and evaluates three feature-selection strategies—Information Gain (IG), Principal Component Analysis (PCA), and Random Forest (RF)—on the CICIoV2024 dataset. Features are normalized using Min–Max scaling before being fed into the RNN classifier. The models achieve perfect classification on held-out tests (accuracy/precision/recall/F1 = 1.00). However, probabilistic evaluation reveals low ROC–AUC scores (IG: 0.572, PCA: 0.429, RF: 0.415), indicating limited discriminative margins and potential overfitting or calibration issues despite flawless confusion matrices. PCA and RF further reduce computational overhead during inference compared to IG. These findings highlight that relying solely on accuracy can be misleading for IDS evaluation; temporal RNNs should be complemented with probability-aware training, calibration, or hybrid architectures. This work contributes a temporal-aware IDS framework for IoV and motivates future research on real-time deployment, hybrid RNN-CNN/LSTM models, and adversarial robustness to improve generalization and safety of connected vehicles
Transformer-Based Multi-Class Intrusion Detection Using CICIoMT2024 Dataset for Secure IoMT Networks Winanto, Eko Arip; Sharipuddin, Sharipuddin; Purnama, Benni; Nurhadi, Nurhadi; Afuan, Lasmedi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5512

Abstract

Internet of Medical Things (IoMT) ecosystems significantly enhance healthcare services but simultaneously expand the attack surface, exposing medical networks to diverse cyber threats such as distributed denial-of-service and spoofing attacks. Existing intrusion detection systems for IoMT are often limited to binary classification and struggle to capture complex multi-class attack behaviors, particularly under highly imbalanced data distributions. This study proposes a deep Transformer-based intrusion detection model as a reproducible baseline for multi-class intrusion detection in IoMT environments. The model is evaluated on the CICIoMT2024 dataset, which comprises 19 traffic classes including benign and multiple attack categories. Data preprocessing involves stratified data splitting, feature normalization, and label encoding to ensure fair evaluation. The proposed baseline employs a six-layer Transformer encoder with eight attention heads and is trained using the AdamW optimizer. Experimental results demonstrate an overall accuracy of 98.76% and a macro F1-score of 0.92, indicating strong detection capability across most attack classes. The model achieves excellent performance on benign traffic and high-volume attacks such as DDoS and DoS, while performance degradation is observed on minority classes, including ARP spoofing, highlighting the impact of class imbalance. These findings establish the proposed Transformer model as a transparent and robust baseline for IoMT intrusion detection research. By providing reproducible performance benchmarks, this work supports future development of hybrid and imbalance-aware detection mechanisms aimed at enhancing real-time security in medical cyber-physical systems.
Information Gain-Based Feature Selection and Machine Learning Classification for DDoS Attack Variant Detection in Cloud Computing Environment Winanto, Eko Arip; Kurniabudi, Kurniabudi; Sharipuddin, Sharipuddin; Mellyati, Denia Igesti Nur
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5752

Abstract

Cloud computing environments face significant security vulnerabilities from Distributed Denial of Service (DDoS) attacks, which can cause system failures and service disruptions. Despite various existing detection methods, challenges remain regarding high computational overhead and suboptimal accuracy due to redundant features in complex datasets. This study aims to identify the optimal feature subset and evaluate its impact on detection performance across multiple machine learning algorithms for multi-class DDoS variants. The research methodology employs a two-stage approach: feature selection using Information Gain (IG) to reduce 47 original features into subsets of 8, 10, 15, and 20, followed by classification using Decision Tree (DT), Random Forest (RF), and Naïve Bayes (NB) on the CICIoT2023 dataset. Experimental results demonstrate that the Decision Tree model with an optimized subset of only 8 features, primarily Inter-Arrival Time (IAT), Header_Length, and Tot_size, achieves a superior accuracy of 99.97%. While Naïve Bayes performs well in binary classification, its accuracy drops significantly to approximately 30% in multiclass settings. This study concludes that IG-based feature selection reduces computational complexity by 30-40% while maintaining high performance across 12 DDoS variants. These findings provide a practical framework for scalable and efficient intrusion detection systems suitable for real-time deployment in resource-constrained IoT-cloud environments.
Co-Authors Dedi Saputra Dodi Sandra Farista R, Rahul Febriani ER, Riri Jasmir, Jasmir Kurniabudi, Kurniabudi Lasmedi Afuan Lola Yorita Astri, Lola Yorita Mellyati, Denia Igesti Nur Nanda Putra, Yofi NOVIANTO, YUDI Nurhadi Pareza Alam Jusia, Pareza Alam Pratama, Bimo Purnama, Benni Rizki Anggraini, Ayu Syaputra, Dikky Takhir, Said Hambali WIJAYA, IBNU SANI