Article Per Year (5 Year)
p-Index From 2021 - 2026
2.668
P-Index
This Author published in this journals
All Journal CESS (Journal of Computer Engineering, System and Science) EXPLORE International Journal of Informatics and Computation Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Teknologi Kharisma Tech EXPLORE Computing and Education Technology Journal Coreid Journal Jurnal Inovasi Teknologi Terapan JURNAL FLOBAMORATA MENGABDI International Journal of Informatics Engineering and Computing
M. Hizbul Wathan
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
Detection of DDoS Attacks Using Hybrid LSTM and SVM Algorithm Ivansius Nahak; M. Hizbul Wathan
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/vd7kk061

Abstract

Distributed Denial of Service (DDoS) attacks pose serious threats to network infrastructures by disrupting services through massive malicious traffic. This study proposes a hybrid detection model that integrates Long Short-Term Memory (LSTM) with a Support Vector Machine (SVM) classifier to improve the accuracy of DDoS detection in network traffic. The LSTM model captures temporal patterns within sequential traffic data, while the SVM performs the final classification to distinguish between normal and anomalous traffic. The experiment uses a dataset containing 104,345 records with 23 features that undergo preprocessing, encoding, scaling, and class balancing before model training. Experimental results demonstrate that the proposed hybrid model achieves stable learning performance with training accuracy reaching approximately 93% and validation accuracy around 94%. The loss curves show consistent decreases across 50 training epochs, indicating effective convergence and minimal overfitting. Confusion matrix analysis shows that the model correctly classifies the majority of normal and anomalous traffic samples, with relatively low false positive and false negative rates. Overall evaluation results show that the hybrid LSTM–SVM model achieves 95% accuracy with balanced classification performance. The model records strong precision, recall, and F1-score values for both normal and anomalous traffic classes.
Prediksi Harga Pangan di Kota Pangkalpinang Menggunakan Algoritma XGBoost Khemal Fasyah Ishaq; Riki Afriyansyah; M. Hizbul Wathan
Jurnal Inovasi Teknologi Terapan Vol. 4 No. 2 (2026): Jurnal Inovasi Teknologi Terapan
Publisher : Politeknik Manufaktur Negeri Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33504/jitt.v4i2.420

Abstract

Food price fluctuations are a strategic issue affecting food security and regional inflation stability. Pangkalpinang City experiences significant inflationary pressure in the food sector, highlighting the need for accurate predictive models. This study aims to develop a time series–based price prediction model for strategic food commodities using the Extreme Gradient Boosting (XGBoost) algorithm. The dataset consists of daily prices of 40 food commodities obtained from the Pangkalpinang City Trade Information System, covering the period from January 1, 2024, to May 28, 2025. The research methodology includes data preprocessing, feature engineering, time series–based data splitting, hyperparameter optimization using Optuna, and model evaluation. Feature engineering incorporates price lag features, statistical features, calendar features, and national holiday indicators. Model performance is evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the R-Squared (R²). The results indicate that XGBoost achieves high prediction accuracy for most commodities, particularly those with stable price patterns, while still capturing fluctuation trends in highly volatile commodities. The findings suggest that commodity-specific modeling provides better performance than a single global model and can support regional inflation control policies.
PhishTect: A Hybrid SMOTEN–FT-Transformer–PSO Framework for Enhanced Phishing Website Detection on Imbalanced Data M Hizbul Wathan; Muhammad Kalam Sabili
CoreID Journal Vol. 4 No. 2 (2026): July 2026
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v4i2.176

Abstract

Phishing websites remain a major cybersecurity threat, while conventional blacklist-based detection systems often fail to identify newly emerging and zero-day attacks. In addition, phishing datasets are frequently imbalanced, causing machine learning models to exhibit poor minority-class detection performance. To address these challenges, this study proposes PhishTect, a hybrid phishing detection framework that integrates Synthetic Minority Oversampling for Nominal Data (SMOTEN), Feature Tokenizer Transformer (FT-Transformer), and Particle Swarm Optimization (PSO). Unlike existing approaches that typically focus on balancing, deep learning, or optimization techniques separately, the proposed framework combines these components within a unified architecture. SMOTEN is employed to balance categorical phishing data, FT-Transformer learns contextual representations from tabular features, and PSO optimizes model hyperparameters to improve predictive capability. Experiments conducted on the PhishTank dataset evaluated three scenarios: baseline FT-Transformer, FT-Transformer with SMOTEN, and the proposed SMOTEN–FT-Transformer–PSO framework. The proposed model achieved the best performance, obtaining 96.62% accuracy, 0.9696 F1-score, 0.9963 ROC-AUC, and 0.997 PR-AUC. The results demonstrate that integrating oversampling, Transformer-based feature learning, and swarm intelligence optimization significantly improves phishing detection effectiveness, robustness, and generalization on imbalanced cybersecurity datasets.
EXPLAINABLE MACHINE LEARNING FOR PHISHING URL DETECTION USING SHAP INTERPRETATION M. Hizbul Wathan; Satria Agus Darma
KHARISMA Tech Vol 21 No 2 (2026): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v21i2.696

Abstract

Phishing attacks delivered through malicious URLs represent an increasingly prevalent cyber threat capable of causing significant harm to users. Although machine-learning–based phishing detection has been widely explored, most existing models still operate as black boxes, making their classification decisions difficult to interpret. This study proposes an Explainable Machine Learning framework for phishing URL detection by integrating five algorithms—XGBoost, Random Forest, Gradient Boosting, Decision Tree, and K-Nearest Neighbors—augmented with SHAP (SHapley Additive Explanations) for interpretability. The dataset includes structural URL features such as character length, special symbol counts, number of subdomains, and string entropy. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix to enable comparative assessment among algorithms. The results show that XGBoost achieves the best performance, obtaining 97.8% accuracy, an F1-score of 0.976, and stable predictions across all classes. Random Forest ranks second with 96.4% accuracy, followed by Gradient Boosting at 95.7%. Meanwhile, Decision Tree and KNN exhibit lower performance due to their higher sensitivity to data variation. SHAP analysis reveals that the most influential features in phishing prediction include URL length, special character frequency, entropy levels, and the number of subdomains. These findings demonstrate that integrating XAI not only enhances model transparency but also ensures that phishing detection systems remain accurate, interpretable, and accountable.
Co-Authors Ahmat Josi Ali, Hendrik Aulia, Rosaura Better Swengky Bradika Almandin Wisesa Cahyadi, Irsan Dede Hamdani Deswita Deswita Efendi, Muhamad Masjun Evvin Faristasari Fadhilah, Indirokan Fadli, Sofiansyah Fadli Fatir Atthariq Zami Indra Irawan Indra Irawan Irwan Ivansius Nahak Khemal Fasyah Ishaq Lesta, Lesta LINDA FUJIYANTI Moh. Aziz Muhammad Kalam Sabili Oktaviani, Ajeng Prayogi, M. Bagus Ramadani, Ardi Rani Laple Satria Rani Laple Satria Putra Riki Afriyansyah Riski Meliya Ningsih Satria Agus Darma Silvia Agustin Sirlus Andreanto Jasman Duli Sofiansyah Fadli Tegar Priyadi Wardianto, Wardianto Yang Agita Rindri Zain, M Syafrizal