Skripsi

IMPLEMENTATION OF DATA MINING CLASSIFICATION IN ACCURATE FRAUD TRANSCATION CLASSIFICATION (CASE STUDY: BANK BENGKULU)

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Penilaian

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Fraud is a common problem in the financial and banking, including Bank Bengkulu. Fraud is a deliberate act of deviation in order to gain profit, either directly or indirectly, and results in losses for the bank, such as lost profits and decreased corporate credibility. In overcoming these business problems, various studies have stated that data mining is one of the best recommendations in dealing with detecting existing fraud. This study aims to apply and find out how well data mining is in detecting fraudulent transactions and analyze various algorithms and approaches that can produce a model with an AUC score with the good or excellent category that can accurately detect fraud in banking transactions. Because fraud is a rare case where the number of frauds is certainly very small compared to cases of normal transactions and can cause the model's performance to be less good in detecting fraud, so it needs various approaches to the problem of returns. Models with different approaches trained with available data yield varied evaluation results and can be ranked based on AUC scores. The result of the research is that the data mining model is able to detect fraudulent transactions up to 80 percent of the total fraud transaction cases that exist. Then the approach to the case of imbalance classfication problems turned out to be very helpful in improving the performance of the data mining model in detecting fraud. And the model produced from the experiments conducted has a fairly good performance with an AUC score of 0.853 or can be categorized as good to excellent, which means that the model is quite accurate in detecting fraudulent transactions

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002692	T41190	T411902021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T411902021

Publisher

Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2021

Collation

xiv, 57 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Pemrosesan Data, Teknik Informatika
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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