IMPLEMENTATION OF MACHINE LEARNING SUPPORT VECTOR MACHINE ALGORITHM IN FRAUD DETECTION SYSTEM
The rise of the fraud transaction incident requires financial organizations (such as banks) to develop ways to detect fraud transactions to avoid losses occuring from the fraud transaction itself. One of which is machine learning-based fraud detection system. In resolving these problems, the meth...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/49866 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The rise of the fraud transaction incident requires financial organizations (such as
banks) to develop ways to detect fraud transactions to avoid losses occuring from the
fraud transaction itself. One of which is machine learning-based fraud detection system.
In resolving these problems, the methodology used is CRISP-DM. Various machine
learning algorithms have been tried to classify fraud transactions. Support Vector
Machine Algorithm is one of the alternative that can be used. The radial based function
kernel method is the most appropriate for the characteristics of transaction fraud data
compared to other kernel methods. The development of a fraud transaction detection
system is carried out in two stages, the first stage is data processing and the second
stage is hyperparameter optimization. The results of testing this machine learning
model produces performance with accuracy reaching 99.9 %, precision reaching 73
%, textit recall reaching 97.9 %, f1 scores reaching 83.6 %, and the area under textit
precison-recall reaching 0.778 and false positive rate reaching 0.01 % |
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