Deteksi Anomali Pada Proses Bisnis Menggunakan Metode Multi Criteria Decision Making (MCDM) Dan Hybrid Neural Network

Ummah, Nurul Wakhidatul (2016) Deteksi Anomali Pada Proses Bisnis Menggunakan Metode Multi Criteria Decision Making (MCDM) Dan Hybrid Neural Network. Undergraduate thesis, Institut Teknologi Sepuluh Nopember Surabaya.

[img]
Preview
Text
5111100003-Undergraduate_Thesis.pdf - Published Version

Download (3MB) | Preview

Abstract

Fraud merupakan suatu kecurangan yang menyebabkan kerugian besar bagi perusahaan. Fraud dapat dideteksi dengan melihat anomali yang ada pada proses bisnis perusahaan. Eksekusi waktu yang tidak sesuai, aktivitas resource yang tidak sesuai dengan wewenangnya, aktivitas yang sengaja dilewati, dan pengambilan keputusan yang salah merupakan beberapa contoh anomali yang sering terjadi. Studi kasus tersebut dapat menggunakan ontologi untuk mendapatkan nilai atribut, kemudian dilakukan perhitugan rating menggunakan metode MCDM dan label diperoleh dari verifikasi pakar dan nilai fuzzy triangular. Label akan menjadi masukan untuk metode hibrid neural network yang berfungsi mendeteksi anomali. Metode ini merupakan pengembangan dari neural network dengan penggunaan fungsi Gaussian di hidden layer untuk mengatasi masalah aproksimasi dari value of attributes dan penambahan ridge sebagai solusi masalah multikolinearitas atribut anomali yang belum bisa diatasi oleh metode neural network sebelumnya. Uji coba metode digunakan 1200 dataset dengan cross validation dan kombinasi lima parameter. Hasil klasifikasi yang paling optimal menggunakan minimum seed 2, maximal iteration tak hingga, minimal standar deviasi 0,02, jumlah clustering 3, dan nilai ridge dari RBF Network 1,0 e-7. Performa yang dihasilkan sangat baik yakni akurasi 99%, presisi 100%, sensitivitas 71%, dan spesivisitas 100%. Oleh karena itu metode hybrid neural network dapat digunakan untuk mendeteksi anomali ============================================================================================ Fraud is a fraudulent that results a huge financial loss to companies. Fraud is able to be detected by the anomaly shown in the business process. The inappropriate time execution, unauthorized resource activities, intentionally by pass activity, and wrong decision making are the examples of frequent anomaly. From the aforementioned cases, the ontology method can be used to gain the attributes value. Then, the rating can be calculated the MCDM method as well as the label is gained from the expert verification and the value of triangular fuzzy. The label will be an input for hybrid neural network method which has a function to detect the anomaly. This method was the development of neural network with the addition of Gaussian function in the hidden layer to solve the approximation problem from values of attributes and the ridge as a solution of the anomaly attributes multicolinearity problem that unable to be overtaken by the previous neural network method. This method was tested using 1200 dataset with cross validation and the combination of five parameters. The most optimum clasification was using the minimum seed of 2, infinity maximal iteration, minimal deviation standard of 0.02, the total clustering of 3, as well as the value of ridge from RBF Network of 1.0e-7. This performance had a good accurate number which is 99%, presision 100%, sensitivity 71%, specificity 100%. Therefore this hybrid neural network method can be used to detect the anomaly

Item Type: Thesis (Undergraduate)
Additional Information: RSIf 658.403 801 1 Umm d
Uncontrolled Keywords: Anomali, Ontologi, MCDM, Hybrid Neural Network
Subjects: T Technology > T Technology (General) > T58.6 Management information systems
Divisions: Faculty of Information Technology > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User: EKO BUDI RAHARJO
Date Deposited: 10 Jul 2020 02:36
Last Modified: 10 Jul 2020 02:36
URI: https://repository.its.ac.id/id/eprint/76349

Actions (login required)

View Item View Item