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All Journal Jurnal Pengabdian Teknologi, Ekonomi dan Humaniora Journal Technology Information and Data Analytic
Nur Syamsiyah
Darma Persada University
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Library & Information Science Social Sciences Other

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Enhancing Teachers Proficiency with Digital Interactive Learning Resources for Differentiated Instruction Yahya Yahya; Mira Febriana Sesunan; Nur Syamsiyah; Husen Asbanu; Juan Pratama; Linda Nur Afifa; Darsono
JEPTIRA Vol 3 No 1 (2025)
Publisher : Fakultas Teknik Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/jeptira.v3i1.93

Abstract

The course seeks to enhance educators' competencies in creating customized learning experiences with digital interactive technologies, namely platforms like Wordwall and Quizizz. The event occurred on February 11, 2025, in the Bekasi region, involving high school and vocational school educators. The training materials encompassed the concept of diversified learning, an introduction to the Wordwall and Quizizz platforms, and exercises for developing interactive media that align with suitable teaching methodologies. The activity's outcomes demonstrated a notable enhancement in instructors' competencies in the creative and contextual utilization of digital media, alongside heightened understanding of the need of adaptive learning services tailored to student needs. This initiative enhances the transformation of digital education at the secondary school level.
Deteksi Serangan Brute Force SSH Menggunakan Klasifikasi Naïve Bayes pada Log Cowrie Honeypot di Lingkungan Virtual Arya Adhari Prasetyo; Herianto; Yahya; Nur Syamsiyah
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 1 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i1.88

Abstract

The increasing number of brute force cyberattacks targeting SSH services highlights the urgent need for effective early detection and mitigation systems. This study aims to analyze brute force attack patterns using the Naïve Bayes classification algorithm based on log data generated by the Cowrie Honeypot. A simulated virtual environment was developed to emulate attack scenarios and generate authentic SSH log data while preserving real server confidentiality. The system architecture follows the CRISP-DM framework, including data preprocessing, model development, evaluation, and deployment. Evaluation using confusion matrix metrics showed that the Naïve Bayes algorithm successfully distinguished brute force attempts from normal traffic with high accuracy, precision, recall, and F1-score. The findings confirm the potential of combining Cowrie honeypot data with machine learning classifiers as an early warning tool for intrusion detection in enterprise network infrastructures.
Behavioral Biometric-Driven Educational Data Mining: CNN-Based Prediction of Students’ On-Time Graduation from Handwritten Signatures Herianto; Khoirul Mustaan; Yahya; Nur Syamsiyah
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.112

Abstract

Timely graduation is a fundamental metric in higher-education accreditation and a key indicator of institutional efficiency. Conventional prediction models largely rely on longitudinal academic records, which are lagging indicators and often fail to detect risks during the early stages of study. This research proposes a paradigm shift by leveraging behavioral biometrics—specifically, the analysis of handwritten signatures using Deep Learning—to predict students’ graduation timelines and academic motivation profiles. Using a dataset from the Undergraduate Information Technology Program at Universitas Darma Persada, the study adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. A Convolutional Neural Network (CNN) model based on the ResNet-50 architecture was developed, employing transfer learning to extract complex graphological features from signature images. Through rigorous data augmentation and statistical normalization, the model addresses the limitations of a small dataset. Empirical evaluation reports a graduation-prediction accuracy of 65% (Recall: 65%, F1-Score: 64%) and an academic-personality prediction accuracy of 70% (Precision: 74%, F1-Score: 69%). Although its absolute performance remains below transcript-based models, the findings validate the potential of signatures as early leading biometric indicators capable of capturing latent discipline and intrinsic motivation. This approach offers a non-invasive decision-support tool for academic advisors within intelligent education ecosystems.
Co-Authors Arya Adhari Prasetyo Darsono Herianto Husen Asbanu Juan Pratama Khoirul Mustaan Linda Nur Afifa Mira Febriana Sesunan Yahya Yahya Yahya