Comparison of Financial Distress Prediction Model Accuracy Using Support Vector Machines and Discriminant Analysis Methods

Herlina; Siti Mundari

Authors

Herlina Universitas 17 Agustus 1945 Surabaya Author
Siti Mundari Universitas 17 Agustus 1945 Surabaya Author

Keywords:

Support Vector Machines, Discriminant Analysis , Data Mining , Financial Distress, Miscellaneous Industry

Abstract

Financial distress is a stage before the company goes bankrupt. For this reason, the ability to predict financial can be useful information for companies and investors. This information is useful for companies to be able to improve their financial condition so that the company does not go bankrupt. For investors, this information is useful to avoid investor losses in capital investment. Studies on financial distress have been conducted for a long time, starting with using statistics until now being developed using artificial intelligence methods. The purpose of this study was to compare the accuracy of the financial distress prediction model for publicly traded manufacturing companies in miscellaneous industry sectors listed on the Indonesia Stock Exchange using the data mining method, namely Support Vector Machines, which is one of artificial intelligence method and the statistical method, namely Discriminant Analysis. From the research results, the two methods provide equally good accuracy. Based on the processed data, the accuracy of the two methods is 100%.

References

6. References

[1] Arif, M.F., 2022, Analisis Perbandingan Model Pendeteksi Financial Distress, Jurnal Riset Terapan Akuntansi, Vol. 6 No. 1

[2] Chen, iB. i–T. idan iChen, iM. i–Y., i2010, iApplying iParticles iSwarm iOptimization iFor Support iVector iMachines iOn iPredicting iCompany iFinancial iCrisis,International iConference on iBusiness iand iEconomics iResearch, iVol. i1, iKuala iLumpur, iMalaysia.

[3] Ismiati, E., Wibowo, E., and Utami, S. S., 2020, Pengaruh Rasio Keuangan Terhadap Prediksi Financial Distress (Studi pada perusahaan manufaktur sector aneka industry yang terdaftar di BEI periode 2015 – 2018), Jurnal Ekonomi dan Kewirausahaan, Vol. 20 No. 2

[4] Irfan, F.M. and Supriyanto, R., 2024, Comparative Analysis of Machine Learning and Deep Learning Models Integrated with Altman Z-Score for Financial Distress Prediction in Companies Listed on the Indonesia Stock Exchange (IDX), Ekombis Review: Jurnal Ilmiah Ekonomi dan Bisnis, Vol. 12 No. 2 April 2024 page: 2259 – 2278

[5] Kholifah, iN., iDjumali, iHartono, iS., i2020, iMengukur iFinancial iDistress iDengan iMetode Grover, iAltman iZ-Score, iSpringate iDan iZmijewski iPada iPT. i iSolusi iBangun iIndonesia Tbk, iEdunomika, iVol. i4 iNo.2.

[6] Kurniadi, A., 2021, Analisis Rasio Keuangan Untuk Memprediksi Financial Distress Perusahaan Manufaktur Di BEI, JIMKES Jurnal Ilmiah Manajemen Kesatuan Vol. 9 No. 3

[7] Kusuma, J. and Hadiprajitno, B., 2021, Prediksi Financial Distress Perusahaan Di Indonesia Menggunakan Rasio Keuangan Dan Analisis Diskriminan, DIPONEGORO JOURNAL OF ACCOUNTING, Volume 10, Nomor 4

[8] Nisa, iU. iZ., i2013, iModel iPrediksi iFinansial iDistress iPada iPerusahaan iManufaktur iGo Public idi iIndonesia. iThesis iProgram iMagister iBidang iOptimasi iSistem iIndustri iJurusan Teknik iIndustri iInstitut iTeknologi iSepuluh iNopember.

[9] Pelitawati, D. and Kusumawardana, R.A., 2020, Analisis Komparasi Model Altman, Zmijewski Dan Springate Untuk Memprediksi Financial Distress Pada Perusahaan Yang Terdaftar Di Bursa Efek Indonesia, Jurnal Economics and Sustainable Development Vol. 5 No.02

[10] Riyanti, iS. iD., i2020, iAnalisis iRasio iKeuangan iterhadap iKondisi iFinancial iDistress ipada Perusahaan iManufaktur, iJurnal iREAD, iVol.1 iNo.2, ihal. i56-65.

[11] Santosa, iB. idan iWilly, iP., i2011, iMetoda iMetaheuristik iKonsep idan iImplementasi, iGuna Widya, iSurabaya.

[12] Sari, M. and Diana, H., 2020, Financial Ratio Analysis To Predict The Financial Distress Conditions Of Pulp And Paper Companies Listed On The Iindonesia Stock Exchange In 2012-2017 With The Altman Z-Score Model, Research In Accounting Journal, Vol 1(1) 2020 : 32-48

[13] Stephanie at al, 2020, Pengaruh Likuiditas, Leverage Dan Ukuran Perusahaan Terhadap Financial Distress Pada Perusahaan Properti Dan Perumahan, COSTING:Journal of Economic, Business and Accounting, Volume 3 Nomor 2

[14] Suhardoyo and Muktiningrum, 2024, Prediksi Financial Distress Pada Perusahaan Tektil Yang TerdaftarDi Bursa Efek Indonesia (BEI) Melalui Analisis Rasio Keuangan, J-CEKI : Jurnal Cendekia Ilmiah, Vol.3, No.6

[15] Suhendi, iA., i2021, iAnalisis iAltman iZ-Core ipada iPerusahaan iManufaktur iSub iSektor Otomotif iyang iTerdaftar di Bursa Efek Indonesia, Jurnal Gentiaras Manajemen dan Akuntasi, Vol. 13 No. 2.