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All Journal Jurnal Ilmiah Matematika Engineering, Mathematics and Computer Science Journal (EMACS)
Mujhid, Almuzhidul
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Cost-Sensitive Learning with LightGBM for Class Imbalance in Intrusion Detection Systems Novika, Andien Dwi; Mujhid, Almuzhidul
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13435

Abstract

Imbalanced data is a common challenge in classification problems, where standard models tend to be biased toward majority classes, leading to poor detection of minority instances. This paper presents a comparative study of Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting (XGBoost) models, enhanced with cost-sensitive learning to address class imbalance at the algorithmic level. The objective is to evaluate the impact of cost-sensitive loss adjustments on model performance using various evaluation metrics. Experimental results show that both models achieved high cross-validation and test accuracies, with LightGBM and XGBoost recording over 99.9% accuracy. However, only cost-sensitive LightGBM achieved perfect scores in precision, recall, and F1-score, indicating its ability to handle minority class identification effectively. In contrast, XGBoost exhibited lower recall and F1-score despite similar accuracy, reflecting limitations in sensitivity to minority instances. Models without cost-sensitive learning demonstrated further drops in performance across minority-related metrics. The findings suggest that cost-sensitive LightGBM is a more robust solution for imbalanced classification tasks, outperforming both its baseline and the cost-sensitive XGBoost variant. This approach is particularly beneficial for critical applications such as fraud detection, cybersecurity, and medical diagnostics, where class imbalance is prevalent and misclassification costs are high
Perbandingan 5 Jarak K-Nearest Neighbor pada Analisis Sentimen Mujhid, Almuzhidul; Thobirin, Aris; Firdausy, Salma Nadya; Surono, Sugiyarto; Rahmadani, Lanova Ade
Jurnal Ilmiah Matematika Vol 8, No 2 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/konvergensi.v0i0.23170

Abstract

K-Nearest Neighbor (KNN) merupakan algoritma yang biasa digunakan untuk klasifikasi. Penelitian ini menggunakan ulasan aplikasi Maxim di Google Play Store. Pengguna yang sudah mengunduh aplikasi Maxim berhak memberikan ulasan di Google Play Store guna berbagi informasi untuk pengguna lain. Implementasi K-Nearest Neighbor (KNN) terhadap Sentiment Analysis ulasan aplikasi Maxim dapat digunakan untuk menentukan kelas ulasan bernilai positif, neutral, atau negatif. Peneliti melakukan perbandingan 5 jarak yang berbeda untuk metode KNN yaitu jarak Euclidean, Manhattan, Minkowski, Chebyshev dan Canberra. Pengujian yang telah dilakukan memberikan hasil akurasi pada klasifikasi KNN dengan jarak yang berbeda, memberikan hasil akurasi yang berbeda-beda, yaitu jarak Euclidean  84 persen, jarak Manhattan  79 persen, jarak Minkowski 84 persen, jarak Chebyshev  7 persen dan jarak Canberra =44 persen.
Co-Authors Aris Thobirin, Aris Firdausy, Salma Nadya Novika, Andien Dwi Rahmadani, Lanova Ade Sugiyarto Surono, Sugiyarto