Article Per Year (5 Year)
p-Index From 2020 - 2025
0.702
P-Index
This Author published in this journals
All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH Jurnal Ekonomi Kreatif dan Manajemen Bisnis Digital (JEKOMBITAL)
Yonia, Dwika Lovitasari
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Finance Loan Risk Assessment Using Machine Learning for Credit Eligibility Prediction and Model Optimization Mulyanto, Sigit; Yonia, Dwika Lovitasari; Sutejo, Bambang
IJISTECH (International Journal of Information System and Technology) Vol 8, No 5 (2025): The February edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i5.376

Abstract

Finance loans play a crucial role in the global economy, supporting individuals and businesses in accessing capital for investment and financial stability. However, more than 60% of financial institutions struggle with identifying credit risks due to the limitations of traditional assessment models, which fail to capture complex borrower behavior. Additionally, 75% of financial firms have adopted AI-driven credit risk management, yet challenges remain in model validation and decision-making transparency. This study applies machine learning techniques, including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM), to enhance loan eligibility prediction. The dataset underwent preprocessing, including handling missing values, feature engineering, and data standardization. Model evaluation used accuracy, precision, recall, and F1-score. Logistic Regression achieved the highest F1-score at 88.6% and recall at 98.6%, while Random Forest recorded the highest precision at 82.3%. Feature importance analysis identified Credit History as the most influential factor, followed by Loan Amount and Total Income. While machine learning improves loan risk assessment, challenges remain in model interpretability. Future research should integrate explainable AI (XAI) and alternative credit scoring factors to enhance model transparency and robustness in real-world applications
Finance Loan Risk Assessment Using Machine Learning for Credit Eligibility Prediction and Model Optimization Mulyanto, Sigit; Yonia, Dwika Lovitasari; Sutejo, Bambang
IJISTECH (International Journal of Information System and Technology) Vol 8, No 5 (2025): The February edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i5.376

Abstract

Finance loans play a crucial role in the global economy, supporting individuals and businesses in accessing capital for investment and financial stability. However, more than 60% of financial institutions struggle with identifying credit risks due to the limitations of traditional assessment models, which fail to capture complex borrower behavior. Additionally, 75% of financial firms have adopted AI-driven credit risk management, yet challenges remain in model validation and decision-making transparency. This study applies machine learning techniques, including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM), to enhance loan eligibility prediction. The dataset underwent preprocessing, including handling missing values, feature engineering, and data standardization. Model evaluation used accuracy, precision, recall, and F1-score. Logistic Regression achieved the highest F1-score at 88.6% and recall at 98.6%, while Random Forest recorded the highest precision at 82.3%. Feature importance analysis identified Credit History as the most influential factor, followed by Loan Amount and Total Income. While machine learning improves loan risk assessment, challenges remain in model interpretability. Future research should integrate explainable AI (XAI) and alternative credit scoring factors to enhance model transparency and robustness in real-world applications
Sentiment Classification of Livin’ by Mandiri Reviews in Indonesia Using LSTM for Digital Banking Service Improvement Mulyanto, Sigit; Yonia, Dwika Lovitasari; Situmorang, Kheylina Lidya; Sutejo, Bambang
Jurnal Ekonomi Kreatif dan Manajemen Bisnis Digital Vol 4 No 1 (2025): AGUSTUS
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/jekombital.v4i1.1011

Abstract

The rapid expansion of digital banking services in Indonesia has increased the need for continuous monitoring of user satisfaction, particularly through feedback submitted via app reviews. This study analyses user sentiment toward the Livin’ by Mandiri mobile banking application using a deep learning approach. A total of 5,000 user reviews were collected exclusively from the Google Play Store and pre-processed through text cleaning steps such as slang normalization, stemming, and tokenization. Sentiment labels (positive, neutral, negative) were assigned using an Indonesian lexicon-based method, and a Long Short-Term Memory (LSTM) model was trained and evaluated with accuracy, precision, recall, and F1-score metrics. Results indicate negative sentiment dominates (37.6%), with frequent complaints about login failures and slow performance, while the LSTM model achieved 98% accuracy. This study is limited by its single-platform data source, potential linguistic bias in Indonesian user reviews, and the model’s limitations in detecting sarcasm or complex emotions. Nonetheless, the findings demonstrate the applicability of sentiment analysis as a real-time monitoring tool to support feature enhancement and user experience improvements in Indonesian mobile banking services.
Benchmarking Machine Learning Models for Corporate Bankruptcy Prediction using Financial Ratios Mulyanto, Sigit; Yonia, Dwika Lovitasari; Arif, Muhammad; Sutejo, Bambang
Jurnal Ekonomi Kreatif dan Manajemen Bisnis Digital Vol 4 No 2 (2025): NOVEMBER
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/jekombital.v4i2.1071

Abstract

Corporate bankruptcy prediction is a critical task in financial risk management, particularly under conditions of economic uncertainty and highly imbalanced datasets. This study presents a comprehensive benchmarking framework that evaluates multiple supervised learning models and a voting ensemble approach for corporate bankruptcy prediction. Using a publicly available dataset comprising 78,682 financial records from US-listed companies on NYSE and NASDAQ (1999-2018), we compare the performance of Random Forest, XGBoost, Gradient Boosting, Support Vector Machine, Decision Tree, and a Voting Classifier. Extensive preprocessing, including outlier removal, normalization, and feature selection, and cost-sensitive learning to mitigate severe class imbalance was conducted to ensure data quality. Model performance was assessed using multiple evaluation metrics such as accuracy, F1-score, and ROC AUC to account for class imbalance. Results demonstrate that the Voting Classifier, integrating Random Forest, XGBoost, and Gradient Boosting via hard voting, achieves superior overall performance with an accuracy of 93.6%, F1-score of 96.5%, and ROC AUC of 82.6%, outperforming individual models. The findings underscore the value of ensemble approaches in improving prediction robustness while addressing class imbalance challenges in financial distress forecasting. This study contributes a reproducible experimental design that can guide future research and practical implementation of learning models in corporate bankruptcy risk assessment.
Co-Authors Bambang Sutejo, Bambang Muhammad Arif Sigit Mulyanto Situmorang, Kheylina Lidya