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All Journal Jurnal Pengabdian Teknologi, Ekonomi dan Humaniora Journal Technology Information and Data Analytic
Yahya
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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Journal : Journal Technology Information and Data Analytic

 1 
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.
Development of a Decision Support System for Motorcycle Credit Eligibility Using the TOPSIS Method Endang Ayu Susilawati; Eva Novianti; Avida Awitia; Yahya
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.v2i2.113

Abstract

The growing need for motorcycle financing in Indonesia has encouraged financial institutions to improve the accuracy and consistency of their credit evaluation processes. At PT FIFGROUP, the current assessment procedure still relies heavily on manual surveys and subjective judgments, which often leads to variations in decision outcomes and longer processing times. This study aims to design and develop a Decision Support System (DSS) that facilitates a more objective and efficient assessment of motorcycle credit eligibility by applying the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Recent advancements in decision-making research highlight TOPSIS as one of the most effective multi-criteria decision-making (MCDM) methods due to its structured approach in comparing alternatives against ideal benchmarks. Building on this body of work, the proposed system incorporates organizational criteria—such as residential status, income stability, expenditure levels, and educational background—into a standardized evaluation model.The research methodology includes system requirement analysis, conceptual and database design, and the integration of the TOPSIS algorithm into the application workflow. Through normalization, weighting, and distance calculations, the system generates a final ranking score that reflects each applicant’s eligibility. The results of the study show that the DSS significantly improves the consistency of credit evaluations, reduces subjective bias, and accelerates the decision-making process. Overall, the implementation of a TOPSIS-based DSS provides a practical and reliable solution for PT FIFGROUP to enhance the quality and efficiency of motorcycle credit assessments
Co-Authors Andi Susilo Arya Adhari Prasetyo Avida Awitia Endang Ayu Susilawati Eva Novianti Herianto Khoirul Mustaan Nur Syamsiyah Timor Setiyaningsih