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All Journal Journal of Information System Exploration and Research
Hakim, Ade Anggian
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Journal : Journal of Information System Exploration and Research

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The Optimization of Credit Scoring Model Using Stacking Ensemble Learning and Oversampling Techniques Rofik, Rofik; Aulia, Reza; Musaadah, Khalimah; Ardyani, Salma Shafira Fatya; Hakim, Ade Anggian
Journal of Information System Exploration and Research Vol. 2 No. 1 (2024): January 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i1.203

Abstract

Credit risk assessment plays an important role in efficient and safe banking decision-making. Many studies have been conducted to analyze credit scoring with a focus on achieving high accuracy. However, predicting credit scoring decisions also requires model construction that handles class imbalance and proper model implementation. This research aims to increase the accuracy of credit assessment by balancing data using Synthetic Minority Oversampling (SMOTE) and applying ensemble stacking learning techniques. The proposed model utilizes a base learner consisting of Random Forest, SVM, Extra-Tree Classifier, and XGboost as a meta-learner. Then to handle unbalanced classes using SMOTE. The research process was carried out in several stages, namely Data Collection, Preprocessing, Oversampling, Modeling, and Evaluation. The model was tested using the German Credit dataset by applying cross-validation. The evaluation results show that the stacking ensemble learning model developed has optimal performance, with an accuracy of 83.21%, precision of 79.29%, recall of 91.78%, and f1-score of 85.08%. This research shows that optimizing the stacking ensemble learning model with data balancing using SMOTE in credit scoring can improve performance in credit scoring.
The Influence of Determining the K-Value on Improving the Diabetes Classification Model using the K-NN Algorithm Korina, Nanda Putri; Prasetiyo, Budi; Hakim, Ade Anggian; Septian, M Rivaldi Ali
Journal of Information System Exploration and Research Vol. 2 No. 2 (2024): July 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i2.344

Abstract

Diabetes mellitus is still an important health problem globally, so it requires an efficient classification model to help determine a patient's diagnosis. This study aims to determine the K-value on the accuracy performance of the diabetes classification model using the K-Nearest Neighbors (K-NN) algorithm. This research utilizes a simulated dataset generated through interaction with ChatGPT, we investigate various K-values ​​in the K-NN model and assess its accuracy using a confusion matrix. Based on experiments, we found that the K-NN classification model with a K=6 obtained an optimal accuracy of 97.62%. Thus, our findings highlight the important role of selecting optimal K-values ​​in improving the performance of diabetes classification models.
Co-Authors Ardyani, Salma Shafira Fatya Aulia, Reza Budi Prasetiyo, Budi Korina, Nanda Putri Musaadah, Khalimah Rofik Rofik, Rofik Septian, M Rivaldi Ali