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All Journal SMARTICS Journal Jurnal Algoritma
Fathoni
Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisis UX E-PPT Universitas XYZ dengan Metode User Experience Questionnaire Kevin Agustin Purba Purba; Umar Rahman Zidan; Muhammad Azyumardi Azra; Fathoni
SMARTICS Journal Vol 11 No 2 (2025): Journal SMARTICS (Oktober 2025)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v11i2.11916

Abstract

Digital transformation in academic administrative services demands systems that are efficient and user-experience oriented. This study aims to analyze the user experience of the e-PPT website of the Faculty of Computer Science at XYZ University, which functions as an academic administrative service platform. The research employs the User Experience Questionnaire (UEQ) method, which measures six core aspects of user experience: attractiveness, efficiency, perspicuity, dependability, stimulation, and novelty. A total of 40 active students from the Faculty of Computer Science at XYZ University participated by completing the UEQ questionnaire. The analysis results indicate that all UEQ dimensions received low scores, with the novelty dimension scoring negatively. Benchmarking against similar systems revealed that the e-PPT website falls within the lowest quartile (bottom 25%) across all scales. These findings underscore the urgent need for comprehensive improvements in the website’s design and functionality to enhance the quality of digital academic services in the future.
Penilaian Risiko Fraud Transaksi Digital menggunakan Hybrid Machine Learning dengan Clustering dan Klasifikasi Hendra Wijaya; Naek Parulian Hutagalung; Mira Afrina; Ali Ibrahim; Fathoni
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3398

Abstract

Credit card transaction fraud detection is commonly treated as a binary classification problem, whereas operational risk management requires more detailed risk-level information to support investigation prioritization. This study proposes a hybrid machine learning framework for transaction risk stratification. In the first stage, the K-Means algorithm was applied to the training set to discover latent risk structures and generate cluster-based risk labels. Subsequently, a Random Forest model was trained to predict risk levels for new transaction data. To maintain evaluation objectivity, the dataset was divided into training, validation, and testing sets, and data leakage prevention mechanisms were implemented. The testing results show that the model was able to consistently classify two levels of risk with stable precision, recall, and F1-score values. In the binary fraud detection scenario, the model achieved an accuracy of 0.8831. These findings indicate that separating latent risk exploration from predictive classification can produce a more informative risk representation compared to conventional binary approaches. However, this study is still limited to a single public dataset and one classification model. Therefore, the generalizability and potential performance improvements of the model still need to be evaluated by experimenting with other algorithms.
Co-Authors Ali Ibrahim Hendra Wijaya Kevin Agustin Purba Purba Mira Afrina Muhammad Azyumardi Azra Naek Parulian Hutagalung Umar Rahman Zidan