Evaluasi Kinerja Model Klasifikasi Machine Learning untuk Deteksi Fraud pada Data Transaksi Tidak Seimbang

Kaslin Yulianty; Dodo Zaenal Abidin; Joni Devitra

doi:10.61132/prosemnasproit.v2i2.152

Authors

Kaslin Yulianty Universitas Dinamika Bangsa
Dodo Zaenal Abidin Universitas Dinamika Bangsa Jambi
Joni Devitra Universitas Dinamika Bangsa Jambi

DOI:

https://doi.org/10.61132/prosemnasproit.v2i2.152

Keywords:

Balanced Accuracy, Credit Card Transactions, Fraud Detection, ROC-AUC, SMOTE

Abstract

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

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