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All Journal TEPIAN
Handri Santoso
Information Technology, Pradita University
Computer Science & IT

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Comparative Evaluation of Large Language Models for Intent Classification in Indonesian Text Markus Karjadi; Handri Santoso
TEPIAN Vol. 6 No. 2 (2025): June 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i2.3355

Abstract

Large Language Models (LLMs) have shown tremendous potential in intent classification tasks, yet their practical deployment in low-resource language environments remains underexplored. This study presents an informatics-based evaluation framework to compare three LLM architectures—GPT-Neo (fine-tuned), Mistral, and Phi-2.0 (zero-shot inference)—on Indonesian intent classification. The methodology integrates classic informatics approaches such as stratified sampling, label encoding, model evaluation using Scikit-learn, and a REST API-based local inference pipeline via the Ollama framework. The study also benchmarks computational efficiency by profiling execution times on consumer-grade hardware. GPT-Neo achieved 100% accuracy after fine-tuning, while Mistral and Phi-2.0 scored approximately 55% and 18%, respectively, in zero-shot settings. The hybrid architecture designed in this work demonstrates how LLMs can be systematically evaluated and deployed using lightweight, modular informatics workflows. Results suggest that fine-tuned lightweight models are viable for high-accuracy deployment, while zero-shot models enable rapid prototyping under constrained resources.
Framework for Generating Synthetic Customer Data to Enhance Model Training in Banking Puspa Riri Agustiana; Handri Santoso
TEPIAN Vol. 6 No. 4 (2025): December 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i4.3385

Abstract

Data-driven decision-making has taken on a more central role in the banking sector. However, privacy regulations and data security concerns limit the accessibility of real customer data for model training. To address this challenge, synthetic data generation offers a promising solution. This paper presents a framework tool for generating synthetic customer data that closely mimics the statistical properties of real-world data using advanced machine learning techniques, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to enhance model training in banking applications. By leveraging advanced machine learning techniques, our framework can replicate the real Production Data to Synthetic Data customer. This synthetic data can be used to augment existing datasets, enhance model training, and improve the accuracy and robustness of predictive models. We demonstrate the effectiveness of our framework through a case study in a banking context, showcasing its potential to address challenges related to data privacy, data scarcity, and model performance.
Predicting Loan Delinquency in Installment Loans Using LightGBM for Enhanced Credit Risk Management Hanif Han; Teddy Mantoro; Handri Santoso
TEPIAN Vol. 6 No. 4 (2025): December 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i4.3423

Abstract

Credit risk assessment is essential for financial institutions to effectively manage loan portfolios, especially for installment loans. Predicting delinquency is challenging due to the complex interplay of borrower behavior, loan characteristics, and repayment pattern. Traditional models often fail to capture non-linear relationships in data and require significant preprocessing to address imbalanced datasets, feature scaling, and diverse data distributions, resulting in inefficiencies. This research predicts installment loan delinquency using LightGBM, a gradient-boosting algorithm tailored for complex, imbalanced financial datasets. Unlike previous studies focusing on general credit risk or credit card defaults, this work specifically addresses the temporal and behavioral dynamics of installment loans. The model uses a real-world dataset from financial institutions, integrating borrower demographics, loan attributes, and engineered repayment features. LightGBM's histogram-based binning and inherent handling of heterogeneous feature scales both reduce preprocessing complexity and improve performance. Evaluation results show significant improvements over traditional models, achieving an AUC-ROC of 0.91 and strong precision and recall. This approach demonstrates scalability and effectiveness for modern credit risk management. Future work could incorporate macroeconomic factors and assess real-time deployment to further expand the model’s applicability.
Co-Authors Hanif Han Markus Karjadi Puspa Riri Agustiana Teddy Mantoro