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All Journal INTECOMS: Journal of Information Technology and Computer Science JSAI (Journal Scientific and Applied Informatics) bit-Tech Jurnal Informatika dan Rekayasa Perangkat Lunak ILKOMNIKA: Journal of Computer Science and Applied Informatics International Journal of Business, Humanities, Education and Social Sciences (IJBHES) Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Sri Wulandari
Universitas Teknologi Yogyakarta
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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Journal : bit-Tech

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Black Hat SEO Detection Using Ensemble Learning and Multi-Dimensional Web Content Analysis Akhmad Zaqi Riyadi; Sri Wulandari
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3238

Abstract

The integrity of search engines is significantly threatened by manipulative Black Hat SEO (BSEO) tactics, particularly the hidden injection of illicit content such as online gambling. This issue is critically urgent in Indonesia, where attackers frequently compromise government domains (.go.id). By September 2023, over 9,000 such sites had been infiltrated using stealthy defacement and semantic confusion highlighting a gap in existing detection systems that rely on single-dimensional features or ignore real-world class imbalance. To address this, we propose an ensemble learning based detection system combining Random Forest (RF) and Support Vector Machine (SVM), supported by multi-dimensional feature engineering from URLs, meta-tags, hidden CSS/HTML elements, and high-risk keywords (e.g., “slot”, “judi”). Our manually annotated dataset comprises 582 .go.id URLs with a natural 4:1 class imbalance, mitigated via Random Oversampling during training. Evaluation on a balanced test set (146 samples) shows 93.8% ensemble accuracy, 99.6% AUC-ROC, and most critically 100% recall for the Black Hat class, ensuring minimal false negatives. The system also incorporates an internal “override logic” that flags evasion tactics like cloaking or hidden keyword injection, enhancing interpretability. Unlike deep learning alternatives that require large data and computational resources, our approach balances performance, efficiency, and transparency making it suitable for deployment by national cybersecurity agencies. This work advances both academic research and practical defense capabilities against sophisticated BSEO threats targeting public-sector websites.
Mobile Intelligent System for VARK-Based Student Learning Style Classification Using KNN Muhammad Saiful Anwar; Sri Wulandari
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3428

Abstract

The growing complexity of learning activities in higher education highlights the need for accurate and scalable mechanisms to identify students’ learning preferences. Conventional VARK-based assessments, which rely on manual self-report questionnaires, remain limited by subjectivity, low practicality, and the absence of real-time feedback. This study addresses these challenges by developing Bandela, a mobile intelligent system that integrates the VARK framework with the K-Nearest Neighbors (KNN) classification algorithm to provide automated learning style identification. Using a Research and Development (R&D) approach, the system was implemented through a three-tier architecture consisting of a Flutter frontend, a Python Flask backend, and a MySQL database. Questionnaire responses collected from students were used as both training and testing datasets for the KNN model, enabling real-time classification across Visual, Auditory, Read/Write, and Kinesthetic categories. Functional evaluation through Blackbox Testing demonstrated that all core features ranging from authentication and questionnaire completion to classification processing, visualization, and community interaction performed reliably and as intended. The findings indicate that Bandela offers an accessible and empirically grounded tool for identifying learning preferences, contributing to more personalized and adaptive learning strategies. This work underscores the practical value of mobile intelligent systems in advancing data-driven personalization within higher education and provides a foundation for future enhancements involving expanded datasets and exploration of additional machine learning techniques.
Co-Authors Akhmad Zaqi Riyadi Aspin William Roy Berliana Isyatin Rodhiyah Christine Dian Permata Sari M. Iqbal Hasani Muhammad Saiful Anwar Putra Afiqi Rinto Herlambang Apriliyanto Teti Indriati Kastuti