Jurnal Statistika Dan Komputasi (STATKOM), Vol. 2 No. 1 (June 2023) ISSN 2963-038X (print), E-ISSN 2963-0398 DOI : https://doi.org/10.32665/statkom.v2i1.1594 https://journal.unugiri.ac.id/index.php/statkom Published Online June, 30 2023 12 Application of Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) for Stock Forecasting Mega Silfiani 1 , Farida Nur Hayati 2 , Muhammad Azka 3 1,2 Department of Statistics, Institut Teknologi Kalimantan 3 Department of Actuarial Science, Institut Teknologi Kalimantan megasilfiani@lecturer.itk.ac.id 1 , farida.nur@lecturer.itk.ac.id 2 , muhammad.azka@lecturer.itk.ac.id 3 Submitted 2 Mei 2023 Revised 24 Juni 2023 Accepted 27 Juni 2023 Abstract Background: Stock price forecasting assists investors to anticipate risks and opportunities in making prudent investments and maximizing returns. Objective: This study aims to identify the most accurate model for stock forecasting. Methods: This paper utilized the daily closing stock price of Unilever Indonesia, Tbk (UNVR) from January 1, 2018 to July 31, 202. Double Seasonal Autoregressive Integrated Moving Average (DSARIMA), was utilized in this study. Mean Absolute Scaled Error (MASE) and Median Absolute Percentage Error (MdAPE) are used to compare forecasting accuracy. Results: Following conducting each model, we assessed that the best models are DSARIMAX (0,1,[4]) ([3],1,1) 5 (1,1,0) 253 , regarding MASE and MdAPE corresponding to approximately 1.423 and 0.111. The scope of this study has limitations to a test set for one-month forecast periods. Conclusion: As stock prices rise, investors require precise forecasts. Models of forecasting must perform well. This analysis shows how the DSARIMA generate forecasts stock prices more accurately. This investigation evaluated the closing stock price of UNVR. Both MASE and MdAPE assess prediction. After analyzing each model, DSARIMAX (0,1,[4])([3],1,1) 5 (1,1,0) 253 has the lowest MASE and MdAPE values, 1.423 and 0.111, respectively. The procedure lasted one month. Research may combine forecasts and improve their accuracy. Keywords : Double SARIMA, Forecasting, MASE, MdAPE, Stock. Abstrak Latar Belakang: Peramalan harga saham dapat membantu investor untuk mengantisipasi risiko dan peluang dalam investasi dan memaksimalkan pengembalian. Tujuan: Tujuan dari penelitian ini adalah untuk mendapatkan model terbaik untuk peramalan saham. Metode: Penelitian ini menerapkan harga saham penutupan harian Unilever Indonesia, Tbk (UNVR), dari 1 Januari 2018 hingga 31 Juli 2022 dengan metode double seasonal autoregressive integrated moving average (DSARIMA). Mean Absolute Scaled Error (MASE) and Median Absolute Percentage Error (MdAPE) digunakan untuk membandingkan akurasi peramalan. Hasil: Setelah dilakukan peramalan menggunakan masing-masing model, model terbaik yang kami dapatkan adalah DSARIMAX (0,1,[4]) ([3],1,1) 5 (1,1,0) 253 yang memiliki MASE dan MdAPE masing-masing sebesar 1,423 dan 0,111. Penelitian ini dibatasi testing data adalah satu bulan. Kesimpulan: Ketika harga saham naik, investor membutuhkan ramalan yang tepat. Model peramalan harus berkinerja baik. Analisis ini menunjukkan bagaimana DSARIMA menghasilkan peramalan harga saham yang lebih akurat. Investigasi ini mengevaluasi harga penutupan saham UNVR. Baik MASE dan MdAPE menilai prediksi. Setelah dilakukan analisis terhadap masing-masing model, DSARIMAX (0,1,[4])([3],1,1) 5 (1,1,0) 253 memiliki nilai MASE dan MdAPE terkecil, masing-masing sebesar 1,423 dan 0,111. Penelitian dapat menggabungkan peramalan dan meningkatkan keakuratannya. Kata kunci: Double SARIMA, Forecasting, MASE, MdAPE, Stock.