CROSS ENTROPY UNTUK OPTIMASI LAGRANGE MULTIPLIERS PADA SUPPORT VECTOR MACHINES SEBAGAI MODEL PREDIKSI FINANCIAL DISTRESS
DOI:
https://doi.org/10.30996/he.v13i02.878Abstract
The competence in predicting financial distress becomes an important research due to
the advantage in preventing companies financial failure. Besides, financial distress
prediction model will give benefit to the investors and creditors. This research develop
a financial distress prediction model for listed manufacturing companies in Indonesia
using Support Vector Machines (SVM). Mathematically, SVM is formulated in the form
of quadratic programming, which requires high computational time in finding the
optimal solution. In this research, Cross Entropy (CE) is used to optimize one of the
SVM’s parameter that is Lagrange multipliers to find the optimal solution or near
optimal solution of dual Lagrange SVM. The accuracy of the prediction model and
computation time will be compared between standard SVM and CE-SVM. Finally, note
that the CE-SVM can solve classification problems in computing time 9.7 times shorter
than the standard SVM with good accuracy results.
Keywords: cross entropy, lagrange multipliers, support vector machines, financial
distress
Downloads
References
Ismi Lufina 2015, Studi Pemanfaatan Minyak Karet
(Hevea Brasiliensis) Sebagai Bahan Bakar Pada
Kompor Rumah Tangga, Jurnal Keteknikan
Pertanian Tropis dan Biosistem, Universitas
Brawijaya – Malang.
.Edi Mulyadi 2009,Degradasi sampah Kota (rubbish)
dengan Proses Pirolisis, Jurnal ilmiah Teknik
Lingkungan Universitas Pembangunan Nasional,
Surabaya.
Amran Japip 20014, Pembuatan Karbon Aktif Dari
Cangkang Kelapa Sawit Dengan Aktivator
H3PO4, Universitas Sumatra Utara
Aprian Ramadhan P., Munawar Ali,2015 Pengolahan
Sampah Plastik Menjadi Minyak Menggunakan
Proses Pirolisis, Universitas Pembangunan
Nasional, Surabaya
Mulyadi, E., 1989, “Pirolisis Blotong Keringâ€,
Makalah Seminar IPTEK nsono,
Trisunaryanti,W., dan Triyono, 2007, Pembuatan,
Karakterisasi dan Uji Aktivitas Katalis NiMo/Z
pada Reaksi Hidrorengka Id.wikipedia.org/wiki/
poliurethana
Harry H. Nazarudin 2008,
https//smk3ae.wordpress.com/…/poliurethan
polimer Universitas Indonesia
www.surabaya eastjava.com
Wijang Wisnu Raharjo, Dwi Aries Himawanto (2013)
Gitman, L. J., 2009, Principle of Management Finance, 12
th
edition, Pearson Education
Inc., Boston.
Gong, R. Dan Huang, S. H., 2012, A Kolmogorov-Smirnov Statistic Based
Segmentation Approach to Learning from Imbalanced Datasets: With Application
in Property Refinance Prediction, Expert Systems with Applications, vol. 39, hal.
-6200.
Han, J. dan Kamber, M., 2001, Data Mining: Concepts and Techniques, 2
nd
edition,
Elsevier Inc., San Fransisco.
Hogan, K. M., Olson, G. T. dan Rahmlow, H. F., 2000, A Model For The Prediction of
Corporate Bankruptcy Using The Analytic Hierarchy Process, Multi-Criteria
Applications, vol. 10, hal. 85-102.
Hui, X. F. dan Sun, J., 2006, An Application of Support Vector Machine to Companies’
Financial Distress Prediction, Lecture Notes Artificial Intelligent, vol. 3885, hal.
-282.
Koh, H. C. 2004, Going Concern Prediction using Data Mining Techniques,
Managerial Auditing Journal, vol. 19, hal. 462-476.
Li, H. dan Sun, J. 2009, Predicting Financial Failure Using Multiple Case Based
Reasoning Combine With Support Vector Machine, Expert Systems with
Applications, vol. 36, hal. 10085-10096.
Jurnal Teknik Industri HEURISTIC vol. 13 no. 2, Oktober 2016, hal. 77-88, ISSN: 1693-8232
Lin, F., Liang, D., dan Chen, E., 2011, Financial Ratio Selection for Business Crisis
Prediction, Expert Systems with Applications, vol. 38, hal. 15094-15102.
Martens, D., et al, 2008, Predicting Going Concern Opinion with Data Mining,
Decision Support Systems, Vol. 45, No. 4, hal. 652-660.
Martens, D., et al., 2010, Credit Rating Prediction Using Ant Colony Optimization,
Journal of Operational Research, vol. 61, hal. 561-573.
Martin, D., 1997, Early Warning of Bank Failure: A Logit Regression Approach,
Journal of Banking and Finance, vol. 1, hal. 249-276.
Min, J. H. dan Lee, Y. C., 2005, Bankruptcy Prediction Using Support Vector Machine
With Optimal Choice of Kernel Function Parameters, Expert Systems with
Applications, vol. 28, hal. 128-134.
Nisa, U. Z. (2013), “Model Prediksi Finansial Distress Pada Perusahaan Manufaktur Go
Public di Indonesiaâ€, Thesis Program Magister tidak diterbitkan, Surabaya:
Institut Teknologi Sepuluh Nopember.
Ohlson, J. A., 1980, Financial Ratios and The Probabilistic Prediction for Bankruptcy,
Journal of Accounting Research, vol. 18, hal. 109-131.
Omelda, I. dan Fernandez, E., 1997, Hybrid Classifiers for Financial Multicriteria
Decision Making: the Case of Bankruptcy Prediction, Computational Economics,
Vol. 10, hal. 317-335.
Outecheva, N., 2007, Corporate Financial Distress: An Empirical Analysis of Distress
Risk. Thesis Ph.D., University of St. Gallen, Russia.
Prastowo, D. (2011), Analisis Laporan Keuangan Konsep dan Aplikasi, Edisi ketiga,
UPP STIM YKPN, Yogyakarta.
Purnanandam, A., 2008, Financial Distress and Corporate Risk Management: Theory
and Evidence, Journal of Financial Economics, vol. 87, hal. 706-739.
Rafiei, F. M., Manzari, S. M. dan Bostanian, S., 2011, Financial Health Prediction
Models Using Artificial Neural Networks, Genetic Algorithm and Multivariate
Discriminant Analysis: Iranian Evidence, Expert Systems with Applications, vol.
, hal. 10210-10217.
Ross, S. A., Westerfield, R. W., dan Jordan, B. D., 2003, Fundamentals of Corporate
Finance, 6
th
edition, McGraw-Hill, New York.
Santosa, B., 2007, Data Mining: Teknik Pemanfaatan Data untuk Keperluan Bisnis,
Graha Ilmu, Yogyakarta.
Santosa, B., 2009, Application of the Cross-Entropy Method to Dual Lagrange Support
Vector Machine, Lectures Notes in Artificial, Springer.
Santosa, B. dan Willy, P., 2011, Metoda Metaheuristik Konsep dan Implementasi,
Guna Widya, Surabaya.
Setyowati, W., 2009, Strategi Manajemen Sebagai Faktor Mitigasi Terhadap
Penerimaan Opini Going Concern, Tesis Program Pasca tidak diterbitkan,
Semarang: Universitas Diponegoro.
Herlina, Cross Entropy untuk Optimasi Lagrange Multipliers . . .
Shin, K. S., Lee, T. S. dan Kim, H. J., 2005, An Application of Support Vector Machine
in Bankruptcy Prediction Model, Expert Systems with Applications, Vol. 28, hal.
-135.
Sun, J., Li, H., Huang, Q. H., dan He, K. Y., 2013, Predicting Financial Distress and
Corporate Failure: A Review From The State-of-the-art Definitions, Modeling,
Sampling, and Featuring Approaches, Knowledge Based Systems.
Tirapat, S. dan Nittayagasetwat, A., 1999, An Investigation of Thai Listed Firms’
Financial Distress Using Macro and Micro Variables, Multinational Finance
Journal, vol. 3, no. 2, hal. 103-125.
Tsai, C. F., 2009, Feature Selection in Bankruptcy Prediction, Knowledge Based
Systems, vol. 22, hal. 120-127.
Xie, C., Luo, C., dan Yiu, X., 2011, Financial Distress Prediction Based on SVM and
MDA Methods: The Case of Chinese Listed Companies, Qual Quant, vol. 45, hal.
-686.
Yang, C., et al., 2009, Constructing Financial Distress Prediction Model Using Group
Method of Data Handling Technique, Proceeding of the Eighth International
Conference on Machine Learning and Cybernetics, Baoding.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with Heuristic agree to the following terms:
- Authors transfer the copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.. that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access)
