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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) Bulletin of Information Technology (BIT)
Ella Kristiantini Susan
Universitas Teknologi Yogyakarta
Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Optimizing Financial Risk Prediction for Loan Approval Decisions Khalifatur Rauf; Adi Cahyo Kuswijayanto; Ella Kristiantini Susan
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6071

Abstract

Accurate financial risk prediction is essential for effective loan approval decision-making, particularly in data-driven financial systems. This study investigates the influence of feature selection strategies on the performance of machine learning models for loan approval prediction using the publicly available Kaggle "Financial Risk for Loan Approval" synthetic dataset, which contains 20,000 applications. Experiments evaluated multiple feature selection paradigms, including filter-based, wrapper-based, embedded, and PCA-informed approaches across six classification models using stratified 10-fold cross-validation and imbalance-aware metrics. The results show that feature selection consistently improves predictive robustness and minority class recognition. Contrary to assumptions favoring complex models, Logistic Regression combined with Lasso regularization achieved the best overall predictive performance, yielding an ROC-AUC of 99.41% and an F1-score of 91.72%. Embedded feature selection methods provided the most favorable balance between accuracy and computational efficiency. These findings indicate that the effectiveness of feature selection depends heavily on its interaction with model complexity, providing empirical guidance for designing robust, interpretable financial risk prediction systems.
Klasifikasi Kinerja Konten YouTube Mobile Legends Menggunakan LightGBM dengan Pelabelan Berbasis K-Means dan Interpretasi SHAP Wayan Praka; Ella Kristiantini Susan; Lidya Nurmala Eva
Bulletin of Information Technology (BIT) Vol 7 No 3: September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i3.3138

Abstract

YouTube is a video-sharing platform widely used by content creators, including those in the gaming category such as Mobile Legends. The intense competition among Mobile Legends content creators has made content performance analysis essential for developing data-driven content strategies. However, most previous studies have relied on manual data labeling, which is prone to subjectivity and lacks efficiency. Furthermore, high-accuracy classification models are often treated as black-box models, making it difficult to explain the factors influencing prediction outcomes. This study aims to develop a YouTube Mobile Legends content performance classification model by integrating K-Means, LightGBM, and Shapley Additive exPlanations (SHAP). The dataset consists of 3,052 videos collected using the YouTube Data API v3. The K-Means algorithm was employed to generate automatic labels, while LightGBM was used as the classification model and SHAP was applied to interpret the model's predictions. The results indicate that the optimal number of clusters was K=2, producing two performance classes: High Performance (72.44%) and Low Performance (27.56%). The dataset was subsequently divided into 80% for training and 20% for testing. On the testing dataset, the LightGBM model achieved an accuracy of 93.78%, precision of 93.87%, recall of 93.78%, and an F1-score of 93.81%. Furthermore, 10-fold cross-validation yielded a mean accuracy of 92.75% with a standard deviation of 0.0148, demonstrating the model's robustness and stability. SHAP analysis revealed that Subscriber Count was the most influential feature, followed by Duration, Video Age, and Title Length. These findings demonstrate that the integration of K-Means, LightGBM, and SHAP provides an effective approach for objectively, accurately, and transparently classifying the performance of Mobile Legends content on YouTube, while offering valuable insights to support content creators in developing data-driven content strategies.
Co-Authors Adi Cahyo Kuswijayanto Khalifatur Rauf Lidya Nurmala Eva Wayan Praka