Komparasi Hasil Prediksi Dengan Menggunakan Hyperparameter Tuning Antara Random Search dan Grid Search
Abstract
Abstrak — Peningkatan yang signifikan pada transaksi keuangan mencurigakan yang berpotensi merugikan lembaga keuangan dan masyarakat semakin luas. Pencucian uang dan penipuan finansial merupakan ancaman serius yang sulit dideteksi oleh sistem tradisional, yang sering kali tidak mampu mengimbangi kompleksitas metode kriminal yang semakin canggih. Masalah utama penelitian ini adalah bagaimana meningkatkan akurasi dan efisiensi dalam mendeteksi transaksi mencurigakan menggunakan teknologi Machine Learning. Penelitian ini dilakukan dengan mengembangkan model pendeteksi transaksi mencurigakan menggunakan algoritma XGBoost, Decision Tree, dan Logistic Regression dengan membandingkan pencarian parameter terbaik untuk Hyperparameter Tuning antara Random Search dan Grid Search dalam menghasilkan prediksi yang bernilai tinggi. Kata kunci— fraud, xgboost, decision tree, logistic regression, random search, grid search, hyperparameter tuning
Downloads
References
J. Yao, J. Zhang and L. Wang, “A financial statement fraud detection model based on hybrid data mining methods,” 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 57-61, 2018, doi: 10.1109/ICAIBD.2018.8396167.
R. Frumerie, “Money Laundering Detection using Tree
Boosting and Graph Learning Algorithms,” M.S. thesis,
Dept. Mathematics., KTH., Stockholm, Sweden, 2021.
[Online]. Available : https://www.diva-portal.org/smash/record.jsf?pid=diva2
%3A1663255&dswid=6464
N. Alfa, S. Mawar, N. H. Siahaan, R. Putri, “Memahami Transaksi Keuangan Mencurigakan,” ppatk.go.id. Accessed: Oct. 10, 2023. [Online.]
Available: https://www.ppatk.go.id/siaran_pers/read/953/memaha
mi-transaksi-keuangan-mencurigakan.html
W. Nugraha and A. Sasongko, “Hyperparameter Tuning on Classification Algorithm with Grid Search,” SISTEMASI, vol. 11, no. 2, p. 391, May 2022, doi: https://doi.org/10.32520/stmsi.v11i2.1750.
Y. Özüpak, “Machine learning-based fault detection in transmission lines: A comparative study with random search optimization,” Bulletin of the Polish Academy of Sciences Technical Sciences, pp. 153229–153229, , doi: https://doi.org/10.24425/bpasts.2025.153229.
A. F. D. Putra, M. N. Azmi, H. Wijayanto, S. Utama, and I. G. P. W. Wedashwara Wirawan, “Optimizing Rain Prediction Model Using Random Forest and Grid Search Cross-Validation for Agriculture Sector”, MATRIK, vol. 23, no. 3, pp. 519–530, Jul. , doi: 10.30812/matrik.v23i3.3891.
M. Arifin and S. Adiyono, “Hyperparameter Tuning in Machine Learning to Predicting Student Academic Achievement,” International Journal of Artificial Intelligence Research, vol. 8, no. 1.1, 2024, doi: https://doi.org/10.29099/ijair.v8i1.1.1214.
D. A. Anggoro and S. S. Mukti, “Performance Comparison of Grid Search and Random Search Methods for Hyperparameter Tuning in Extreme
Gradient Boosting Algorithm to Predict Chronic
Kidney Failure,” International Journal of Intelligent
Engineering and Systems, vol. 14, no. 6, pp. 198–207,
Aug. 2021, doi: https://doi.org/10.22266/ijies2021.1231.19.
M. B. Prayoga, N. Cahyono, Subektiningsih, and Kamarudin, “PENERAPAN GRID SEARCH UNTUK OPTIMASI MODEL MACHINE LEARNING DALAM KLASIFIKASI SENTIMEN KOMENTAR
YOUTUBE,” JATI (Jurnal Mahasiswa Teknik
Informatika), vol. 9, no. 3, pp. 3817–3824, June 2025, doi: https://doi.org/10.36040/jati.v9i3.13375.
M. Gusarov, “Do I need to tune logistic regression hyperparameters?” Medium.com. Accessed: Aug. 11, [Online.] Available: https://medium.com/codex/do-i-need-to-tune-logisticregression-hyperparameters-1cb2b81fc a69
L. Owen, “Understanding the Hyperparameters of Popular Algorithms” in Hyperparameter Tuning with Python. Birmingham, UK: Packt Publishing Ltd., 2022, ch. 11, pp. 219–225.
S. F. N. Islam, A. Sholahuddin, and A. S. Abdullah,
“Extreme gradient boosting (XGBoost) method in making forecasting application and analysis of USD exchange rates against rupiah,” in Journal of Physics: Conference Series, vol. 1722, no. 1, pp. 12-16, Jan., doi: 10.1088/1742-6596/1722/1/012016.
