Long Short Term Memory For Comparison Between Bank Syariah Indonesia And PT Bank Artos Indonesia Shares

Zulfanita Dien Rizqiana, Izzat Muhammad Akhsan, Intan Indrasara Priyanto, Aninda Sabila Maharani

Abstract


The growth of the capital market in Indonesia has increased from year to year. Based on data from the Indonesia Central Securities Corporation (KSEI), there has been an increase in investor growth in the capital market by 2.34%, mutual funds by 2.44%, and shares by 1.34% until August 2024. The demographic of individual investors in the capital market is dominated by Generation Z who are younger than from 30 years as much as 55.07% in August 2024 (KSEI, 2024). Shares are a form of investment that has the potential for large profits but with small risks. One sector that Gen Z is interested in investing in is the financial sector. The aim of this research is to compare the share prices of Bank Syariah Indonesia and Bank PT Ban Artos Indonesia Tbk using a Neural Network with the Long Short Term Memory (LTSM) algorithm. The data used in this research is secondary data on BSI and PT Bank Artos Indonesia Tbk share prices taken from the investing.com website. The data period used is from 01 September 2021 – 01 September  2024. Based on the results of stock price forecasting using a Neural Network with the LTSM algorithm, RMSE value for both models is  for BSI 75.0757 and 91.795 for PT. Bank Artos Indonesia Tbk. A comparison of the predicted share prices of PT Bank Arto Indonesia Tbk and BSI shows that BSI's share price performance is superior to that of PT Bank Arto Indonesia Tbk.


Keywords


Financial Sector; Neural Network; Shares

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References


Ajuna, L. H., Dukalang, H. H., & Ardi, M. (2022). Bank Syariah Indonesia Share Price Prediction Using Fuzzy Time Series Model Lee Method. Madania: Jurnal Kajian Keislaman, 25(2), 233. https://doi.org/10.29300/madania.v25i2.5453

Anisa, O. N., Wibowo A, R. E., & Nurcahyono, N. (2022). Faktor-Faktor yang Mempengaruhi Harga Saham: Berdasarkan Signaling Theory. Jurnal Akuntansi Indonesia, 11(2), 85. https://doi.org/10.30659/jai.11.2.85-95

Ary, W. W. (2021). Entrepreneurship Bisnis Manajemen Akuntansi Dinamika harga dan volume perdagangan saham bank digital Indonesia : sebuah pendekatan VECM. 4(2), 213–229.

Asykarulloh, Azam; Araffi, Mayogi; Mahmudah, Desi; Prihatin, Rina, Al Umar, A. U. A. (2023). PENGARUH FAKTOR FUNDAMENTAL TERHADAP HARGA SAHAM BANK DIGITAL DI INDEKS SAHAM SYARIAH Azam Asykarulloh Magister Ekonomi Syariah , UIN Sunan Kalijaga , Indonesia Mayogi Araffi Magister Ekonomi Syariah , UIN Sunan Kalijaga , Indonesia Desi Mahmudah Rina Pri. Analisa Akuntansi Dan Perpajakan, 7(1), 19–28.

Cahyani, J., Mujahidin, S., & Fiqar, T. P. (2023). Implementasi Metode Long Short Term Memory (LSTM) untuk Memprediksi Harga Bahan Pokok Nasional. Jurnal Sistem Dan Teknologi Informasi (JustIN), 11(2), 346. https://doi.org/10.26418/justin.v11i2.57395

Christyanti, S., Afriyani, F., & Wulandari, T. (2023). Analisis Kinerja Perbankan Syariah Indonesia Sebelum Dan Sesudah Merger. Jurnal Ilmiah Manajemen, Ekonomi, & Akuntansi (MEA), 7(3), 196–209. https://doi.org/10.31955/mea.v7i3.3328

Fadhilah, D. N., Parmikanti, K., & Ruchjana, B. N. (2024). Peramalan Return Saham Subsektor Perbankan Menggunakan Model ARIMA-GARCH. Jurnal Fourier, 13(1), 1–19. https://doi.org/10.14421/fourier.2024.131.1-19

Habibi, M. R., & Diah, R. (2022). Peran Perbankan Syari’ah dalam Perkembangan Perekonomian di Indonesia. Maliyah : Jurnal Hukum Bisnis Islam, 12(1), 1–25. https://doi.org/10.15642/maliyah.2022.12.1.1-25

Hijrah, M. (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

Islam, M., & Jabed, K. (2024). Stock Market Price Prediction using Machine Learning Techniques Sciences and Engineering Research STOCK MARKET PRICE PREDICTION USING MACHINE. (February). https://doi.org/10.46545/aijser.v7i1.308

Khumaidi, A., Raafi’udin, R., & Solihin, I. P. (2020). Pengujian Algoritma Long Short Term Memory untuk Prediksi Kualitas Udara dan Suhu Kota Bandung. Jurnal Telematika, 15(1), 13–18. https://doi.org/10.61769/telematika.v15i1.340

Mushliha, M. (2024). Implementasi CNN-BiLSTM untuk Prediksi Harga Saham Bank Syariah di Indonesia. Jambura Journal of Mathematics, 6(2), 195–203. https://doi.org/10.37905/jjom.v6i2.26509

Pahlevi, M. R. (2023). Prediksi Harga Forex Menggunakan Algoritma Long Short-Term Memory. Jnanaloka, 69–76. https://doi.org/10.36802/jnanaloka.2022.v3-no2-69-76

Purwanto, S., & Perkasa, D. H. (2024). ANALISIS TRANSFORMASI BANK DIGITAL YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2018-2022 Lembaga keuangan seperti perbankan sangat berpengaruh pada perekonomian suatu negara baik secara makro maupun mikro ekonomi . Perbankan sebagai tulang punggung per. 4, 622–633.

Qiu, J., Id, B. W., & Zhou, C. (2020). Forecasting stock prices with long-short term memory neural network based on attention mechanism. 1–15.

Safira, Ariska Asri; Hidayatullah, S. K. (2024). Analisis Faktor Yang Mempengaruhi Kinerja Keuangan Bank Digital Yang Terdaftar di Bursa Efek Indonesia Tahun 2019-2022. 3(3), 1019–1037.

Santoso, H. (2023). Analisis Fundamental Dan Teknikal Saham PT . Bank Syari ’ ah Indonesia Tbk . ( QRIS ). 9(01), 609–617.

Saputra, Septian Rahul Dika; Tarigan, Thia Margaretha; Prasetyo, Christianus Yudi; Setiabudi, A. W. (2024). Komparasi Bank Konvensional dan Bank Digital Dengan Metode RGEC. 18(1), 134–167.

Setiawan, L., & Susanti, D. (2023). Analisis Perbandingan Hasil Peramalan Harga Saham Menggunakan Model Autoregresive Integrated Moving Average dan Long Short Term Memory. 19(2), 223–234. https://doi.org/10.24198/jmi.v19.n2.42164.223-234

Siregar, S. R., & Widyasari, R. (2023). Peramalan Harga Crude Oil Menggunakan Metode Long Short-Term Memory (Lstm) Dalam Recurrent Neural Network (Rnn). Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika Dan Statistika, 4(3), 1478–1489. https://doi.org/10.46306/lb.v4i3.421

Solikhawati, A., & Samsuri, A. (2023). Evaluasi Bank Syariah Indonesia Pasca Serangan Siber : Pergerakan Saham dan Kinerja Keuangan. 9(03), 4201–4208.

Sulha, F. A., & Irawati, Z. (2024). Pengaruh Kinerja Keuangan Terhadap Harga Saham Sub Sektor Farmasi Bei Periode 2019-2022. Jurnal Manajemen Dan Akuntansi, 19(1), 49–60.

Tuzzuhro, F., Rozaini, N., & Yusuf, M. (2023). PERKEMBANGAN PERBANKAN SYARIAH DIINDONESIA Fatimah. PeKA: Jurnal Pendidikan Ekonomi Akuntansi, 11 No 2(23), 78–87.




DOI: https://doi.org/10.31326/jisa.v7i2.2131

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Copyright (c) 2024 Zulfanita Dien Rizqiana, Izzat Muhammad Akhsan, Intan Indrasara Priyanto, Aninda Sabila Maharani

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Journal Name: JISA (Jurnal Informatika dan Sains)
e-ISSN: 2614-8404, p-ISSN: 2776-3234
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JISA (Jurnal Informatika dan Sains) is Published by Program Studi Teknik Informatika, Universitas Trilogi under Creative Commons Attribution-ShareAlike 4.0 International License.