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ITS » Master Theses » Teknik Industri - S2
Posted by tondoindra@gmail.com at 28/10/2015 10:00:52  •  1052 Views


PEMODELAN PREDIKSI FINANCIAL DISTRESS MENGGUNAKAN METODE PARTICLE SWARM OPTIMIZATION-SUPPORT VECTOR MACHINE

FINANCIAL DISTRESS PREDICTION USING PARTICLE SWARM OPTIMIZATION-SUPPORT VECTOR MACHINE

Author :
HERLINA ( 2512202201 )




ABSTRAK

Financial distress adalah sebuah kondisi yang menunjukkan tahap-tahap penurunan kondisi keuangan sebuah perusahaan yang terjadi sebelum perusahaan mengalami kebangkrutan bankruptcy atau likuidasi. Kemampuan untuk memprediksi terjadinya financial distress menjadi topik penelitian yang penting karena dapat memberikan manfaat bagi perusahaan untuk dapat mencegah kebangkrutan. Pada penelitian ini dibuat model prediksi financial distress pada perusahaan manufaktur terbuka di Indonesia dengan menggunakan metode Support Vector Machine SVM yang dioptimasi dengan menggunakan metode Particle Swarm Optimization PSO. Variabel yang akan digunakan dalam penelitian ini meliputi variabel keuangan non-keuangan dan makroekonomi. Teknik pemilihan variabel pada penelitian ini menggunakan metode Analytic Hierarchy Process AHP dimana dalam pemilihan variabel akan melibatkan preferensi dan pengalaman para ahli keuangan didalamnya. Dari hasil seleksi variabel dengan menggunakan AHP didapatkan 16 variabel yang terpilih untuk menjadi variabel input pada model. Dari hasil pengujian didapatkan kesimpulan bahwa metode PSO-SVM lebih unggul dari segi waktu komputasi yang lebih singkat dibandingkan dengan SVM standar dengan tingkat akurasi yang tetap terjaga.


ABSTRACT

Financial distress is a condition refers to a declining stage of financial condition of a company that would be happened before the company is going to be bankrupt. The competence in predicting financial distress becomes an important research topic due to the advantage in preventing companies financial failure. This research will develop a financial distress prediction model for listed manufacturing companies in Indonesia using Support Vector Machine SVM optimized by Particle Swarm Optimization PSO. The variables used for the model take into account financial ratios internal governance firm size auditor opinion and auditor reputation. In order to consider the preference and experience of the expertise for variables selection this research will propose to use the Analytic Hierarchy Process AHP approach. There are 16 variables chosen to be the variables input for the model as a result of variables selection process using AHP approach. The accuracy of the prediction model and computation time will be compared between SVM and PSO-SVM. From the experimental research it can be concluded that our proposed PSO-SVM method outperformed standard SVM in the term of reducing computational time with maintained accuracy.



  • Prof. Ir. Budi Santosa, M.S., Ph.D.
  • KeywordsFinancial Distress, Support Vector Machine, Particle Swarm Optimization, Analytic Hierarchy Process
     
    Subject:  Perangkat lunak komputer
    Date Create: 17/07/2014
    Type: Text
    Format: PDF
    Language: Indonesian
    Identifier: ITS-Master-25103150001421
    Collection ID: 25103150001421
    Call Number: RTI 005.1 Her p


    Source
    Master Theses Of Industrial Engineering RTI 005.1 Her p, 2015

    Coverage
    ITS Community

    Rights
    Copyright @2015 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




    [ Download - Open Access ]

    1.  ITS-Master-39230-2512202201_abstract_id.pdf - 206 KB
    2.  ITS-Master-39230-2512202201-abstract_en.pdf - 206 KB
    3.  ITS-Master-39230-2512202201-conclusion.pdf - 375 KB




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