Perbandingan Algoritma Support Vector Machine dan Decision Tree untuk Klasifikasi Performa Perusahaan - Utomo, Mario; Prathivi, Rastri (Building of Informatics, Technology and Science (BITS), 2024)
The number of stock exchange investors in Indonesia reached 5.34 million by the end of December 2023. This figure is dominated by millennial generation investors, indicating a growing confidence in the fundamentals and economic prospects of the Indonesian capital market. However, the lack of financial literacy among this generation often results in ineffective and high-risk investments. Many millennials choose stocks based on short-term trends or recommendations that lack analysis. To address this issue, a more structured approach to stock selection is required. One method that can be employed is the classification of a company's performance based on its performance using various financial indicators and ratios. As the performance of a company affects the movement of its stock value, this research will compare Support Vector Machine and Decision Tree with the One Against All approach in classifying company performance. The features used for the classification of company performance consist of three financial ratios: profitability (ROA), liquidity (CR), and leverage (DER). The labels or targets in the classification are divided into three categories: normal, good, and unfavorable. This research will consider evaluations such as accuracy, cross validation, and confusion matrix. The results of the Support Vector Machine (SVM) algorithm demonstrated an accuracy of 86.67%, while the Decision Tree (DT) algorithm exhibited an accuracy of 93.33%. Consequently, the DT algorithm produced more accurate results than the SVM algorithm in classification. The number of stock exchange investors in Indonesia reached 5.34 million by the end of December 2023. This figure is dominated by millennial generation investors, indicating a growing confidence in the fundamentals and economic prospects of the Indonesian capital market. However, the lack of financial literacy among this generation often results in ineffective and high-risk investments. Many millennials choose stocks based on short-term trends or recommendations that lack analysis. To address this issue, a more structured approach to stock selection is required. One method that can be employed is the classification of a company's performance based on its performance using various financial indicators and ratios. As the performance of a company affects the movement of its stock value, this research will compare Support Vector Machine and Decision Tree with the One Against All approach in classifying company performance. The features used for the classification of company performance consist of three financial ratios: profitability (ROA), liquidity (CR), and leverage (DER). The labels or targets in the classification are divided into three categories: normal, good, and unfavorable. This research will consider evaluations such as accuracy, cross validation, and confusion matrix. The results of the Support Vector Machine (SVM) algorithm demonstrated an accuracy of 86.67%, while the Decision Tree (DT) algorithm exhibited an accuracy of 93.33%. Consequently, the DT algorithm produced more accurate results than the SVM algorithm in classification.