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Journal : JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI

Forecasting Stock Price PT. Telkom Using Hybrid Time Series Regression Linear– Autoregressive Integrated Moving Average Model Kartika Ramadani; Sri Wahyuningsih; Memi Nor Hayati
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.18837

Abstract

The hybrid method is a method of combining two forecasting models. Hybrid method is used to improve forecasting accuracy. In this study, the Time Series Regression (TSR) linear model will be combined with the Autoregressive Integrated Moving Average (ARIMA) model. The TSR linear model is used to obtain the model and residual value, then the residual value of the TSR linear model will be modeled by the ARIMA model. This combination method will produce a hybrid TSR linear-ARIMA model. The case study in this research is stock closing price (daily) of PT. Telkom Indonesia Tbk. The stock closing price (daily) of PT. Telkom Indonesia Tbk in 2020 showed an decreasing and increasing trend pattern. The results of this study obtained the best model of hybrid TSR linear-ARIMA (2,1,1) with the proportion of data training and testing is 70:30. In the best model, the MAD value is 56.595, the MAPE value is 1.880%, and the RMSE value is 78.663. It is also found that the hybrid TSR linear-ARIMA model has a smaller error value than the TSR linear model. The results of forecasting the stock price of PT. Telkom Indonesia Tbk for the period 02 January 2021 to 29 January 2021 formed a decreasing trend pattern.
Peramalan Jumlah Kunjungan Wisatawan Mancanegara ke Indonesia Menggunakan Analisis Intervensi Fungsi Step Adelia Ramadhani; Sri Wahyuningsih; Meiliyani Siringoringo
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i1.21607

Abstract

   Intervention analysis is a method for processing time series data that can be used to explain the effect of an intervention that is influenced by external and internal factors. One application of this method is the data on the number of foreign tourist visits. Since the emergence of COVID-19 in Indonesia, especially in March 2020, Indonesia has begun to implement a lockdown policy and restrict foreign tourists from entering Indonesia. Lockdown policy caused the number of foreign tourist arrivals to decreased drastically. The purpose of this study was obtained a model and forecast results for the number of foreign tourist arrivals for the period November 2021 to November 2022 used a step function intervention analysis. The results of the analysis was shown that the ARIMA intervention model (0,1,1) with a step function with an intervention orde of b=0, s=0, and r=0 was the best model. The results of forecasting the number of foreign tourist visits to Indonesia will increase slowly from November 2021 to November 2022 with a MAPE value 9.91%.