Klasifikasi kondisi finansial pada perusahaan sektor finansial yang terdaftar di BEI periode tahun 2020 menggunakan support vector machine

Kurniawan, Anang (2022) Klasifikasi kondisi finansial pada perusahaan sektor finansial yang terdaftar di BEI periode tahun 2020 menggunakan support vector machine. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Stocks are the investment instrument most favored by investors, according to the OJK or the financial services authority, the growth of stock investors in August 2021 is 50.7% higher than in 2020. This opportunity is misused by investment organizations to become a new fraud mode for novice investors. . Therefore we need an analysis of the classification of the company's financial condition that can assist investors in determining the company's investment objectives. According to Mark E Zmijewski, financial conditions are categorized into 2, namely financial distress and non-financial distress. This study was analyzed to determine the results of the classification of the financial condition of financial sector companies listed on the Indonesia Stock Exchange for 2020 based on financial ratios, namely liquidity ratios, solvency ratios, and profitability ratios. All of these variables will be classified using the support vector machine (SVM) method. The SVM method is used because it can bring up an effective barrier between data. The results of the analysis show that the majority of financial sector companies in Indonesia are categorized as healthy companies with a proportion of 75,5%, while the other 24,5% have unhealthy financial conditions. On average, financial sector companies in Indonesia are liquid companies, able to meet obligations in the event of company dissolution, and able to generate profits from their capital and assets, this can be seen from the positive financial ratios. The best classification results are using a linear kernel and the proportion of training:testing is 80:20 which produces an accuracy of 0,952, a specificity of 1, and a precision of 0,938.
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Stocks are the investment instruments most favored by investors. According to the OJK, or Financial Services Authority, the growth in the number of stock investors in August 2021 was 50.7% higher than in 2020. This opportunity has been misused by investment organizations as a new form of fraud targeting novice investors. Therefore, an analysis of the classification of companies’ financial conditions is needed to assist investors in determining their investment objectives. According to Mark E. Zmijewski, financial conditions are categorized into two types, namely financial distress and non-financial distress. This study was conducted to determine the classification of the financial conditions of financial sector companies listed on the Indonesia Stock Exchange in 2020 based on financial ratios, namely liquidity ratios, solvency ratios, and profitability ratios. All of these variables were classified using the Support Vector Machine (SVM) method. The SVM method was used because it can establish an effective boundary between data. The results of the analysis show that the majority of financial sector companies in Indonesia are categorized as financially healthy companies, with a proportion of 75.5%, while the remaining 24.5% have unhealthy financial conditions. On average, financial sector companies in Indonesia are liquid, able to meet their obligations in the event of company dissolution, and able to generate profits from their capital and assets, as indicated by their positive financial ratios. The best classification results were obtained using a linear kernel with a training-to-testing proportion of 80:20, resulting in an accuracy of 0.952, a recall of 1, and a precision of 0.938.

Item Type:	Thesis (Other)
Additional Information:	658.15 Kur k-1
Uncontrolled Keywords:	Klasifikasi, Kondisi Finansial, Support Vector Machine, Classification, Financial Condition, Support Vector Machine
Subjects:	H Social Sciences > HG Finance > HG4529.5 Portfolio management
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	magang .
Date Deposited:	06 Oct 2026 01:58
Last Modified:	06 Oct 2026 01:58
URI:	http://repository.its.ac.id/id/eprint/145287

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