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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Journal of Information Systems Engineering and Business Intelligence Jurnal Pendidikan Agama Islam MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Tarbiyatuna : Kajian Pendidikan Islam DIMAR: Jurnal Pendidikan Islam Journal of Soft Computing Exploration Jurnal Pendidikan Islam Journal of Student Research Exploration Journal of Information System Exploration and Research Scientific Journal of Informatics Jurnal Ilmiah Riset dan Pengembangan
Rofik Rofik, Rofik
FITK UIN Sunan Kalijaga Yogyakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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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.
Breast Cancer Diagnosis Utilizing Artificial Neural Network (ANN) Algorithm for Integrating Multi-Omics Data and Clinical Features Rofik, Rofik; Artiyani, Fani; Pertiwi, Dwika Ananda Agustina
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.v2i2.249

Abstract

Breast cancer is one of the most common diseases affecting women worldwide, with a significant impact on patient's health and quality of life. Despite advances in medical technology and research, breast cancer diagnosis remains a challenge due to its complexity involving various biological and clinical factors. Several previous studies have focused on detecting this disease with optimal accuracy, but the selection of appropriate algorithms and methods is key to achieving this goal. This study aims to improve the accuracy of breast cancer diagnosis by using the ANN algorithm and data balancing method, SMOTE. This research uses Multi-Omic data and Clinical Features obtained in general from Kaggle. The research process is carried out in several stages, namely Data Collection, Preprocessing, Oversampling, Modeling, and Evaluation. This research successfully obtained an increase in accuracy, which was able to achieve an accuracy of 99.30%.  This research shows that early detection of breast cancer with ANN algorithm and data balancing using SMOTE can improve accuracy performance in early detection of breast cancer. Given the use of data in this study is not too large, it is recommended for further research to use a larger dataset to validate the strength of the model that has been built on more varied data.
Co-Authors Abdul Aziz, Alif Alamsyah - Anggraini, Tasya Fitria Ardyani, Salma Shafira Fatya Aribah, Lutfina Artiyani, Fani Aulia, Reza Budi Prasetiyo, Budi Dwi Hermawan, Guntur Dwi Jayanti, Oni Endang Sugiharti, Endang Faaza, Muhammad Hakim, Ade Anggian Hakim, Roshan Aland Herlambang, M. Jumanto Jumanto, Jumanto Jumanto Unjung KA, Cecep Bagus Suryadinata Much Aziz Muslim Muqowim, Muqowim Musaadah, Khalimah Noorhaidi Hasan Nurul Hak Pertiwi, Dwika Ananda Agustina Pratidinal Jadid, Rosyid Riza Arifudin Sabarudin Sabarudin Simanjuntak, Robert Panca R. Tanga, Yulizchia Malica Pinkan