Miftahur Ro’ifah

Program Studi Statistika Universitas PGRI Argopuro Jember, Indonesia

Fita Fatimah

Program Studi Statistika Universitas PGRI Argopuro Jember, Indonesia

Dwi Noviani Sulisawati

Program Studi Pendidikan Matematika Universitas PGRI Argopuro Jember

DOI: https://doi.org/10.19184/j-agt.v16i01.28209 

ABSTRACT 

The stock price movements are important in the stock market because they provide information about company performance, market sentiment and other economic factors. This makes stock price movements important to monitored by investors, analysts and other market participants to make the right investment decisions when to buy or sell these stocks. The aims of the research is to predict the stock price of PT. Bank Rakyat Indonesia (Persero) Tbk using the Auto Regressive Integrated Moving Average (ARIMA) method. The ARIMA method is used to predict future values based on historical data. The data used is the stock price of PT. Bank Rakyat Indonesia (Persero) Tbk from 3 July 2023 to 28 March 2024 that obtained from the Yahoo Finance website. There are four steps that will be used. First, explore the data to get a general description of the used data. Second, identify data stationarity. Third, identify the ARIMA model that will be used. Fourth, predict the stock price of PT. Bank Rakyat Indonesia (Persero) for April 2024 period. The result is the most optimal model for predicting stoke prices of PT. Bank Rakyat Indonesia (Persero) in the April 2024 period is ARIMA (1,1,1).   

Keywords: ARIMA, PT. Bank Rakyat Indonesia (Persero) Tbk, stock price, time series. 

REFERENCES

[1] Putri, D. M., & Aghsilni. (2019). Estimasi Model Terbaik Untuk Peramalan Harga Saham PT. Polychem Indonesia Tbk. dengan ARIMA. MAp (Mathematics and Applications) Journal, 1(2), 1-12.

[2] Setiawan, C. D., Sulandari, W., & Susanti, Y. (2023). Peramalan Harga Saham PT. Unilever Indonesia Menggunakan Metode Hibrida Arima-Neural Network. In Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi), Vol. 7, No. 1.

[3] Pratama, M. I., Adikara, P. P., & Adinugroho, S. (2018). Peramalan Harga Saham Menggunakan Metode Extreme Learning Machine (ELM) Studi Kasus Saham Bank

[4] Trimono, Maruddani, D. A. I., & Ispriyanti, D. (2017). Pemodelan Harga Saham dengan Geometric Brownian Motion dan Value at Risk PT. Ciputra Development Tbk. JURNAL GAUSSIAN, 6(2), 261–270. http://ejournal-s1.undip.ac.id/index.php/gaussian.

[5] Abdillah, M. F. P., Zukhronah, E., & Respatiwulan. (2021). Peramalan Harga Saham Pt. Bank Central Asia Tbk Menggunakan Metode Auto Regressive Integrated Moving Average (ARIMA). PROSIDING SNAST, 110-117.

[6] Bastian, F., & Islami, F. S. (2021). Peramalan Harga Saham PT. Bank Mandiri (Persero) Tbk. Menggunakan Metode ARIMA. Jurnal Paradigma Multidispliner, 2(2), 473685.

[7] Hijrah, M., Rachman, H., & Rahayu, P. I. (2023). Peramalan Harga Saham Perusahaan Perbankan dengan Market Capitalization Terbesar di Indonesia Pasca-Covid19. Journal of Mathematics: Theory and Applications, 5(2), 95–99. https://doi.org/10.31605/jomta.v5i2.3238.

[8] Hariadi, W., & Sulantari. (2019). Penerapan Model Arima Dalam Peramalan Anak Usia 5–14 Th yang Terinfeksi HIV Di Indonesia. Alifmatika: Jurnal Pendidikan dan Pembelajaran Matematika, 1(1), 74–82. https://doi.org/10.35316/alifmatika.2019.v1i1.74-82.

[9] Kamalakannan, Sengupta, I., & Chaudhury, S. (2018). Stock Market Prediction Using Time Series Analysis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3168423.

[10] Rusyida, W. Y., & Pratama, V. Y. (2020). Prediksi Harga Saham Garuda Indonesia di Tengah Pandemi Covid-19 Menggunakan Metode ARIMA. Square : Journal of Mathematics and Mathematics Education, 2(1), 73. https://doi.org/10.21580/square.2020.2.1.5626.

[11] Ningrum, I. T. W., Pramana, E. R., Yunindasari, D. R., Qanisak, N. N., & Sofiana, L. (2022). Prediksi Pergerakan Saham JKSE Dengan Metode ARIMA Menggunakan Software R Guna Keputusan Investasi Pada Masa Pandemi Omicron. In Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) 4, 120-130.

[12] Susanti, R., & Adji, A. R. (2020). Analisis Peramalan IHSG dengan Time Series Modeling ARIMA. Jurnal Manajemen Kewirausahaan, 17(1), 97. https://doi.org/10.33370/jmk.v17i1.393.

[13] Fauziah, & Pratomo, W. A. (2013). Analisis Peramalan Harga Saham Dalam Keputusan Investasi Pada Perusahaan Perkebunan di PT Bursa Efek Indonesia. Jurnal Ekonomi dan Keuangan, 2(1), 1-12.

[14] Wahyudin. (2019). Prediksi Inflasi Indonesia Memakai Model ARIMA dan Artificial Neural Network. Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi, 5(1). https://doi.org/10.34010/jtk3ti.v5i1.2297.

[15] Purnaningrum, E., Cahyaningtias, S., & Kusumawardhani, D. A. (2021). Augmentation Time Series Model With Kalman Filter to Predict Foreign Tourist Arrivals in East Java. Journal of Physics: Conference Series, 1869(1), 012116. https://doi.org/10.1088/1742-6596/1869/1/012116.