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All Journal JAAKFE UNTAN ( (Jurnal Audit Dan Akuntansi Fakultas Ekonomi Universitas Tanjungpura) JITCoS : Journal of Information Technology and Computer System
Fachrezi, Muhammad Randy
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Social Sciences

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Penerapan Algoritma Decision Tree untuk Memprediksi Risiko Gagal Bayar Nasabah Kartu Kredit Fachrezi, Muhammad Randy; Farhan, Muhammad; Aptanta, Dimas Aqila; Wallidein, Arief Denis
JAAKFE UNTAN (Jurnal Audit dan Akuntansi Fakultas Ekonomi Universitas Tanjungpura) Vol 14, No 2 (2025): Jurnal Audit dan Akuntansi Fakultas Ekonomi Universitas Tanjungpura
Publisher : Jurusan Akuntansi, Fakultas Ekonomi dan Bisnis, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jaakfe.v14i2.101100

Abstract

Penelitian ini membahas prediksi risiko gagal bayar kartu kredit dengan tujuan menghasilkan model yang akurat dan transparan. Metode yang digunakan adalah Decision Tree (CART) dengan stratified split (70:30), validasi silang, serta optimasi hyperparameter berbasis AUC‑ROC; interpretasi disediakan oleh SHAP (global dan individual) dan ekstraksi aturan “jika–maka” dari pohon. Hasil menunjukkan peningkatan kinerja dari baseline (AUC CV 0,6105; AUC test 0,6177; akurasi test 0,7387) menjadi model teroptimasi (AUC CV 0,7561; AUC test 0,7559; akurasi test 0,7812; precision 0,5052; recall 0,5404; F1 0,5222), dengan fitur perilaku pembayaran dan beban tagihan sebagai pendorong risiko utama. Kesimpulannya, model memenuhi tujuan akurasi dan keterjelasan sekaligus menghasilkan aturan operasional yang dapat diaudit untuk mendukung keputusan kredit.
Implementation of a Favorite Course Search System Based on Students’ Average Grades Using the A* Algorithm Amsyah, Dwiky Oldi; Riansyah, Rusma; Aptanta, Dimas Aqila; Fachrezi, Muhammad Randy; Firdaus, Nasywa Roudhotul
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 2 (2025): Volume 1 Number 2, December 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i2.50

Abstract

Optimal selection of elective courses plays an important role in supporting students’ academic success and ensuring alignment between learning interests and final project preparation. This study aims to develop a favorite course search system based on the A-Star (A*) algorithm by utilizing students’ average grades as the main evaluation variable. The system was implemented using the Java NetBeans platform, supported by datasets consisting of course names, credit weights (SKS), and grade distributions. The A* algorithm was adapted through the integration of heuristic components, including Standard Deviation and Relative Credit Load, to improve accuracy in identifying optimal course recommendations. Experimental results demonstrate that the system is capable of generating recommendations with an accuracy rate of 95%, verified through comparison between system outputs and manual calculations. The results also show that the Mitigation course ranked highest with a score of 6.1, indicating strong student performance in that subject. Overall, the system provides a practical and efficient solution for academic decision-making, enabling students to select elective courses more strategically based on data-driven insights. This study contributes to the development of computational methods in educational recommendation systems and opens opportunities for further enhancement through integration with real academic databases.
Co-Authors Amsyah, Dwiky Oldi Aptanta, Dimas Aqila Firdaus, Nasywa Roudhotul Muhammad Farhan Riansyah, Rusma Wallidein, Arief Denis