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All Journal Brilliance: Research of Artificial Intelligence ITQAN: Jurnal Ilmu-ilmu Kependidikan
Alim Misbullah
Universitas Syiah Kuala, Indonesia
Religion Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Detection of DNS Spoofing Attacks on Campus Networks Using LightGBM with Hybrid Feature Selection (SelectKBest + SHAP) Arie Budiansyah; Rudi Arif Candra; Dirja Nur Ilham; Alim Misbullah
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5962

Abstract

This study investigates the detection of Domain Name System over HTTPS (DoH) spoofing attacks utilizing the CIRA-CIC-DoHBrw-2020 dataset, which encompasses over 100,000 labeled DNS records categorized as either normal or malicious. Features such as packet timing, packet size, and TLS parameters are utilized for detection purposes. A systematic feature selection process is conducted utilizing the Elbow and Kneedle methods based on F-Score values derived from a built-in model evaluation. This method ensures that the top features are selected objectively and quantitatively, thereby enhancing the robustness of the model. The model is trained using the five most significant features, yielding exceptional performance metrics: a training time of just 0.5727 seconds, an inference time of 0.0157 seconds, and an inference latency of 0.0035 milliseconds per sample. Moreover, the model delivers an outstanding accuracy of 0.9995, an F1-Score of 0.9995, and an AUC-ROC of 1.0000, reflecting near-perfect detection capabilities. The classification report reveals a balanced distribution of precision, recall, and F1-Scores of 1.00 across both normal and malicious classes, based on a test sample of 14,974 entries. The Elbow plot visually confirms the optimal number of features utilized, while the SHAP beeswarm plot provides insights into how each selected feature contributes to the model’s predictions, facilitating interpretability. Additionally, the confusion matrix corroborates the model's reliability, showcasing that nearly all samples were accurately classified. The results demonstrate that the proposed methodology significantly enhances the effectiveness of DNS spoofing detection, offering a promising avenue for securing DNS over HTTPS communications.
Data Analytics–Based Evaluation of Student Perceptions of Learning Quality in Islamic Higher Education Basrul Abdul Majid; Husni Husni; Alim Misbullah; Muhammad Aizal Hanafi Bin Fazli Hisam
Itqan: Jurnal Ilmu-ilmu Kependidikan Vol. 16 No. 2 (2025): ITQAN: Jurnal Ilmu-ilmu Kependidikan
Publisher : Fakultas Tarbiyah dan Ilmu Keguruan (FTIK) IAIN Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47766/itqan.v16i2.6305

Abstract

This study employs an integrated data analytics approach to examine student perceptions of learning quality at the Faculty of Islamic Economics and Business (FEBI), IAIN Lhokseumawe. The analytical framework combines descriptive statistics, K-Means clustering, and multiple linear regression to provide a comprehensive evaluation of instructional quality. Data were collected from 805 active undergraduate students using a structured questionnaire measuring nine learning quality indicators related to instructional delivery, lecturer competence, communication, assessment practices, and institutional compliance. The findings indicate that students generally perceive the quality of learning positively, with an overall mean score of 4.32 on a five-point Likert scale. Lecturer’s Mastery of Material and Accuracy in Answering Questions emerged as the highest-rated indicators, while Material Suitability with the Semester Learning Plan (RPS) and Transparency of Assessment Criteria received comparatively lower scores. Regression results show that instructional quality indicators significantly influence overall student satisfaction, with assessment transparency as the strongest predictor and material suitability with the RPS as the weakest.  These results highlight the importance of transparent assessment practices and consistent alignment between instructional materials and the RPS in shaping students’ learning experiences, enhancing student trust, reducing uncertainty, and sustaining educational quality in Islamic higher education.
Co-Authors Arie Budiansyah Basrul Abdul Majid Candra, Rudi Arif Husni Husni Ilham, Dirja Nur Muhammad Aizal Hanafi Bin Fazli Hisam