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All Journal Jurnal Fibonaci: Jurnal Pendidikan Matematika Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Chairunisah
Universitas Negeri Medan
Automotive Engineering Computer Science & IT Control & Systems Engineering Mathematics

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Meningkatkan Kemampuan Spasial: Pengalaman Implementasi Pendidikan Matematika Realistik dengan Pola Interaksi Edukatif Rewang Denny Haris; Chairunisah
Jurnal Fibonaci: Jurnal Pendidikan Matematika Vol. 5 No. 2 (2024): Jurnal Fibonaci: Jurnal Pendidikan Matematika
Publisher : Prodi Pendidikan Matematika FMIPA Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jfi.v5i2.64430

Abstract

Kemampuan spasial memiliki kontribusi yang berpengaruh terhadap pembelajaran matematika di SMP. Tersedianya pendekatan-pendekatan pembelajaran inovatif memberikan kesempatan pada guru untuk mengembangkan strategi pembelajarannya untuk mendukung kemampuan spasial ini. Penelitian ini mengkaji peningkatan kemampuan spasial dan proses jawaban siswa dalam menyelesaikan masalah spasial sebagai pengaruh implementasi pendekatan pembelajaran matematika realistik dengan pola interaksi edukatif rewang. Penelitian ini merupakan eksperimentasi yang melibatkan siswa SMPN 1 Percut Sei Tuan sebagai subjek penelitian dan tes kemampuan spasial sebagai instrumen penelitian. Penelitian ini menguraikan proses jawaban siswa yang dianalisis secara deskriptif melalui lembar jawaban siswa pada tes kemampuan spasial. Berdasarkan pendekatan pembelajaran, hasil penelitian mengindikasikan bahwa peningkatan kemampuan spasial siswa yang belajar dengan pendekatan pendidikan matematika realistik dengan pola interaksi edukatif rewang lebih baik bila dikomparasi dengan kemampuan spasial siswa yang belajar secara konvensional. Berdasarkan proses jawaban, kontribusi pembelajaran matematika realistik telah memampukan siswa dalam menyelesaikan permasalahan spasial
Comparison of Logistic Regression and XGBoost Model Performance in Predicting Credit Scores Stacyana Jesika Surianto; Chairunisah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1877

Abstract

Credit Scoring is a mathematical approach used to assess the creditworthiness of individuals or companies by classifying debtors into certain categories based on their risk profiles. This study aims to compare the performance of the Logistic Regression and XGBoost machine learning algorithms in predicting credit scores (credit scoring) to reduce the risk of Non-Performing Loan (NPL) risk at PT Graha Mazindo Mandiri. The secondary dataset used contains 1,533 car loan debtor data with 17 variables, including 1dependent variable and 16 independent variables. The research process includes data preprocessing (cleaning, handling outliers, encoding, normalization, and class balancing with SMOTE), modeling, and evaluation using the Accuracy, Precision, Recall, F1-score, and ROC-AUC metrics. The results show that XGBoost excels with 96% accuracy and ROC-AUC of 0.99 compared to Logistic Regression with an accuracy of 88% and ROC-AUC0.94, due to XGBoost ability to capture non-linear patterns and handle data imbalance. This study provides insights into credit risk factors and supports more accurate credit decision-making, with recommendations for hyperparameter optimization and model integration into operational systems.
Co-Authors Denny Haris Stacyana Jesika Surianto