Tsalitsa Farhat, NIM.: 21106010027 (2025) PERBANDINGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN SUPPORT VECTOR REGRESSION (SVR) DALAM PERAMALAN DERET WAKTU UNIVARIAT (STUDI KASUS: HARGA PENUTUPAN SAHAM PT ASPIRASI HIDUP INDONESIA TBK (ACES) PERIODE 17 JANUARI 2024 SAMPAI 24. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.
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Text (PERBANDINGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN SUPPORT VECTOR REGRESSION (SVR) DALAM PERAMALAN DERET WAKTU UNIVARIAT (STUDI KASUS: HARGA PENUTUPAN SAHAM PT ASPIRASI HIDUP INDONESIA TBK (ACES) PERIODE 17 JANUARI 2024 SAMPAI 24)
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Text (PERBANDINGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN SUPPORT VECTOR REGRESSION (SVR) DALAM PERAMALAN DERET WAKTU UNIVARIAT (STUDI KASUS: HARGA PENUTUPAN SAHAM PT ASPIRASI HIDUP INDONESIA TBK (ACES) PERIODE 17 JANUARI 2024 SAMPAI 24)
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Abstract
Stocks are one of the instruments in the capital market that represent ownership of capital in a company. Through stock investment, investors have rights to the company’s income. In investing, stock price prediction is important because it can help investors reduce risk and increase potential profit. However, the stock market is non-stationary and non-linear, so stock price prediction is not easy. This study aims to compare the performance of stock price forecasting models using the ARIMA and SVR methods. The data used are the closing stock price of PT Aspirasi Hidup Indonesia Tbk (ACES). Based on the research results, it was found that the best model for forecasting the closing stock prices of PT Aspirasi Hidup Indonesia Tbk (ACES) using the ARIMA method is the ARIMA(1,1,2) model, which has an RMSE value of 18,68798, while for the SVR method, the best model is SVR with an RBF kernel, with optimized parameters
| Item Type: | Thesis (Skripsi) |
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| Additional Information / Supervisor: | Aulia Khifah Futhona, M.Sc. |
| Uncontrolled Keywords: | ARIMA, Peramalan, Saham, Support Vector Regression |
| Subjects: | 500 Sains Murni > 510 Mathematics (Matematika) |
| Divisions: | Fakultas Sains dan Teknologi > Matematika (S1) |
| Depositing User: | Muh Khabib, SIP. |
| Date Deposited: | 16 Jan 2024 10:21 |
| Last Modified: | 11 Aug 2025 12:10 |
| URI: | http://digilib.uin-suka.ac.id/id/eprint/62960 |
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