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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Airlangga Journal of Innovation Management
Nazry S, Hevlie Winda
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Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Decision Trees in Predicting Loan Default Risk in Customer Relationships within the Financial Sector Syahra, Yohanni; Br. Tarigan, Yuni Franciska; Andriani, Karina; Nazry S, Hevlie Winda; Setik, Roziyani
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14672

Abstract

Loan default prediction is an important aspect of risk management in financial institutions. Accurate prediction models enable banks and lending organizations to mitigate risks, allocate resources effectively, and optimize decision-making processes. This study investigates the application of decision tree algorithms in predicting loan default risk in the financial sector. Decision trees are renowned for their interpretability, adaptability to non-linear data, and ability to handle missing values, making them a valuable tool in credit risk analysis. Using a dataset consisting of borrower profiles, credit scores, income levels, and payment history, the model identifies key predictors that influence default outcomes. The study uses the C4.5 decision tree model, which will demonstrate that decision trees achieve high prediction accuracy and offer a transparent decision-making framework, enhancing their applicability in real-world scenarios. Furthermore, the paper highlights the implications of these findings for financial institutions, emphasizing the scalability and cost-effectiveness of the model. By integrating decision tree-based models into existing risk assessment systems, lenders can proactively manage loan portfolios and reduce default rates. Future research directions are proposed to explore hybrid approaches that combine decision trees with advanced combined methods to enhance predictive capabilities. The potential of decision tree algorithms in transforming credit risk assessment and supporting more accurate data-driven financial decision-making processes
Probabilistic Markov Chain Modeling for Predicting User Behavior Patterns in Digital Systems Using Data Mining Nazry S, Hevlie Winda; Budi Antoro; Fatma sari Hutagalung
Airlangga Journal of Innovation Management Vol. 7 No. 1 (2026): Airlangga Journal of Innovation Management
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/ajim.v7i1.87129

Abstract

This study addresses the challenge of transforming sequential clickstream data into accurate yet interpretable behavioral predictions for operational decision-making in digital systems. While complex machine learning models often achieve high accuracy, their limited transparency hinders practical adoption. Therefore, this research aims to develop and evaluate a probabilistic Markov-based framework for predicting users’ next actions while maintaining interpretability. A quantitative data mining approach is applied to e-commerce clickstream data collected in January 2026. User interactions are sessionized and mapped into eight discrete behavioral states. The study compares a frequency-based baseline with first-order, second-order, and variable-order Markov models using back-off and Laplace/Dirichlet smoothing. Model evaluation employs a time-based train–test split with Accuracy@1, Mean Reciprocal Rank (MRR), and log-loss as performance metrics. Results indicate that the variable-order Markov model achieves the best performance, improving Accuracy@1 from 0.231 to 0.331, increasing MRR from 0.318 to 0.437, and reducing log-loss from 1.74 to 1.39. The findings demonstrate that Markov-based models offer an effective balance between predictive accuracy and interpretability, enabling the identification of dominant transitions, drop-off points, and conversion bottlenecks. Future research may extend this framework with time-aware or hidden-state models to capture latent user intent, while managerial implications include data-driven system optimization, recommendation enhancement, and user retention strategies.
Co-Authors Andriani, Karina Br. Tarigan, Yuni Franciska Budi Antoro Fatma Sari Hutagalung Setik, Roziyani Yohanni Syahra