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All Journal SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Penelitian Sistem Informasi
Rahmat Budiarsa
Universitas Esa Unggul
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Pembuatan Model Machine Learning untuk Klasifikasi Risiko Pinjaman Perbankan Menggunakan Random Forest dan XGboost Zacki Ferdinansyah; Rahmat Budiarsa
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 3 (2026): Agustus : JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i3.4644

Abstract

Credit risk is one of the major challenges in managing loan portfolios in the banking sector, highlighting the need for approaches capable of identifying loan risk patterns and factors associated with problematic loans based on historical data. This study aims to develop and compare the performance of Random Forest and Extreme Gradient Boosting (XGBoost) algorithms in classifying loan risk patterns and to identify the features that contribute to the classification results. The study uses a historical loan dataset processed through exploratory data analysis, categorical data transformation, an 80:20 training and testing data split, and class imbalance handling using Random Under Sampling and SMOTE. Model optimization was performed through hyperparameter tuning using GridSearchCV, while model performance was evaluated based on accuracy, precision, recall, and F1-score. Model interpretation was conducted using Feature Importance, SHAP (SHapley Additive Explanations), and decision tree visualization. The results show that XGBoost with preprocessing and the second hyperparameter tuning achieved the best performance, with an accuracy of 98%, precision of 97%, recall of 85%, and F1-score of 90%, outperforming Random Forest on the dataset used. Interpretability analysis indicates that recoveries, total_rec_prncp, out_prncp, last_pymnt_amnt, and funded_amnt are among the features that contribute substantially to the classification results. These findings indicate that preprocessing, class imbalance handling, and parameter optimization can improve classification performance while providing insights into the factors contributing to loan risk patterns based on historical data.
PREDIKSI TREN PUBLIKASI BLOCKCHAIN DAN KRIPTOGRAFI DI INDONESIA MENGGUNAKAN BIBLIOMETRIK DAN LSTM Fathimah Azzahro Hasni Rifayanti; Rahmat Budiarsa
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 2 (2026): Jurnal SKANIKA Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i2.3843

Abstract

The rapid adoption of digital technologies in Indonesia has driven increasing research on Blockchain and Cryptography, yet comprehensive mapping and projections of their development remain limited. This study aims to analyze the intellectual landscape and predict publication trends in these two technologies in Indonesia using a quantitative approach integrating bibliometric analysis and Machine Learning. Data were obtained from Dimensions.ai for the 2020–2024 period, yielding 2,193 relevant documents after data cleansing. Network analysis using VOSviewer reveals a shift in Blockchain research from financial assets toward practical applications, particularly Supply Chain management and system efficiency. Meanwhile, Cryptography research shows polarization between the development of classical algorithms and applications for IoT security. A Long Short-Term Memory (LSTM) model was then developed to predict publication trends for 2025–2029 based on annual data from 2020–2024. The model achieved a Mean Absolute Percentage Error (MAPE) below 5% on the test data; however, the limited number of data points should be considered when interpreting this accuracy. LSTM projects a flattening publication trend through 2029, with Cryptography expected to surpass Blockchain by approximately 55 documents. These findings indicate that security and trust may become increasingly critical priorities compared with Blockchain infrastructure development.
Co-Authors Fathimah Azzahro Hasni Rifayanti Zacki Ferdinansyah