Analisis Perbandingan Metode ARIMA Dan LSTM Untuk Prediksi Penjualan Harga Saham BNI

Anisa Tunggal(1*), Rastri Prathivi(2),

(1) Universitas Semarang, Indonesia
(2) Universitas Semarang, Indonesia
(*) Corresponding Author

Abstract


This study aims to compare the Autoregressive Integrated Moving Average (ARIMA) model and Long Short-Term Memory (LSTM) in predicting the closing stock prices of Bank Negara Indonesia (BNI) from September 2021 to September 2024. Historical stock data was obtained through web scraping from Yahoo Finance and analyzed using evaluation metrics such as MAPE and RMSE. The results show that ARIMA outperforms LSTM in prediction accuracy, with lower MAPE and RMSE values for both training and testing data. Additionally, the 7-day ahead stock price predictions indicate that LSTM experienced a 3.42% decrease compared to ARIMA. Based on this study, ARIMA can be concluded as a more accurate model in predicting BNI stock prices compared to LSTM

Full Text:

PDF

References


P. Melinda Silfiana, A. Martha Rahayu, And I. Fauzia, “Bni ’S Strategy To Become Campus Bank In The Digital Era,” Filos. Publ. Ilmu Komunikasi, Desain, Seni Budaya, Vol. 1, No. 3, Pp. 167–178, 2024, [Online]. Available: Https://Doi.Org/10.62383/Filosofi.V1i3.175

U. T. Yanuar, Y. Sudaryo, And N. A. Sofiati, “Analisis Pengaruh Return On Asset, Return On Equity, Dan Earning Per Share Terhadap Harga Saham (Studi Pada Perusahaan Perbankan Bumn Yang Listed Di Bursa Efek Indonesia Periode 2014-2018),” J. Indones. Membangun, Vol. 20, No. 3, Pp. 1–20, 2019.

I. Roseniati, Y. Ernitawati, Roni, M. Badrun Zaman, And T. Rahmawati, “Mengukur Kinerja Keuangan Pt Bni (Persero) Tbk Dengan Menggunakan Rasio Keuangan,” J. Account. Financ., Vol. 1, No. 2, Pp. 96–110, 2019.

B. R. I. Bbri Et Al., “Perbandingan Kenaikan Harga Saham Bank Bni ( Bbni ) Dan Bank,” Vol. 03, No. 10, Pp. 1518–1525, 2024.

I. Oktavia And K. Genjar, “Sinergitas Quadruple Helix: E-Business Dan Fintech Sebagai Daya Dorong Pertumbuhan Ekonomi Lokal Faktor-Faktor Yang Mempengaruhi Harga Saham,” J. Ris. Akunt. Multiparadigma, Vol. 6, No. 1, Pp. 29–39, 2019.

K. Azhura And M. Rosha, “Perbandingan Peramalan Peluang Pergerakan Harga Saham Bni Dan Bri Menggunakan Hidden Markov Model Program Studi Matematika , Universitas Negeri Padang,” Vol. 8, Pp. 29309–29319, 2024.

P. Kartikasari, “Prediksi Harga Saham Pt. Bank Negara Indonesia Dengan Menggunakan Model Autoregressive Fractional Integrated Moving Average (Arfima ),” J. Stat., Vol. 8, No. 1, Pp. 1–7, 2020.

E. Wibowo, “Analisis Penentuan Saham Yang Akan Dibeli Suatu Tinjauan Umum,” J. Ekon. Dan Kewirausahaan, Vol. 11, No. 1, Pp. 151–158, 2011.

A. Fitriadini, T. Pramiyati, And A. B. Pangaribuan, “Penerapan Backpropagation Neural Network Dalam Prediksi Harga Saham,” Semin. Nas. Mhs. Ilmu Komput. Dan Apl., Pp. 561–573, 2020.

Sesotyaning Harum Prabuningrat, M. Al Haris, Nadia Khoirunnafisa Salma, Putri Wahyu Muharamah, And Muhammad Saifuddin Nur, “Peramalan Indeks Harga Konsumen Kota Semarang Dengan Metode Autoregressive Integrated Moving Average,” J. Data Insights, Vol. 1, No. 1, Pp. 1–9, 2023, Doi: 10.26714/Jodi.V1i1.124.

A. A. Shelemo, “Implementasi Metode Hybrid Autoregressive Integrated Moving Average (Arima) – Long Short Term Memory (Lstm) Dalam Peramalan Harga Saham Bank Bri,” Nucl. Phys., Vol. 13, No. 1, Pp. 104–116, 2023.

F. Gumelar Et Al., “Peramalan Harga Saham Bank Bumn Indonesia Menggunakan Long Short-Term Memory (Lstm),” Semin. Nas. Stat. Aktuaria I, Vol. 1, No. 1, Pp. 1–8, 2022, [Online]. Available: Http://Prosiding.Statistics.Unpad.Ac.Id

I. N. C. Janastu And D. U. Wutsqa, “Prediksi Harga Saham Pada Sektor Perbankan Menggunakan Algoritma Long Short-Term Memory,” J. Stat. Dan Sains Data, Vol. 1, No. 2, Pp. 1–14, 2024, [Online]. Available: Https://Journal.Student.Uny.Ac.Id/Index.Php/Jssd

D. Novanti Et Al., “Pemodelan Dan Peramalan Harga Penutupan Saham Perbankan Dengan Metode Arima Dan Family Arch,” Vol. 1, No. 2, Pp. 94–105, 2020.

C. E. P. Joko Riyono, “Prediksi Harga Saham Harian Closing Price Pt. Bni Tbk.Dengan Model Autoregressive Integrated Movingaverage,” Int. J. Therm. Sci., Vol. 160, No. 1, Pp. 1–8, 2020, Doi: 10.1016/J.Ijthermalsci.2020.106676.




DOI: https://doi.org/10.30645/kesatria.v6i1.557

DOI (PDF): https://doi.org/10.30645/kesatria.v6i1.557.g552

Refbacks

  • There are currently no refbacks.


Published Papers Indexed/Abstracted By: