Forecasting JPFA Share Price using Long Short Term Memory Neural Network

Authors

  • I Ketut Agung Enriko Institut Teknologi Telkom Purwokerto
  • Fikri Nizar Gustiyana
  • Hedi Krishna

DOI:

https://doi.org/10.32497/jaict.v8i1.4285

Keywords:

RNN, LSTM, AI, Saham, Epoch

Abstract

To invest or buy and sell on the stock exchange requires understanding in the field of data analysis. The movement of the curve in the stock market is very dynamic, so it requires data modeling to predict stock prices in order to get prices with a high degree of accuracy. Machine Learning currently has a good level of accuracy in processing and predicting data. In this study, we modeled data using the Long-Short Term Memory (LSTM) algorithm to predict the stock price of a company called Japfa Comfeed. The main objective of this journal is to analyze the level of accuracy of Machine Learning algorithms in predicting stock price data and to analyze the number of epochs in forming an optimal model. The results of our research show that the LSTM algorithm has a good level of accurate prediction shown in mape values and the data model obtained on variations in epochs values. All optimization models show that the higher the epoch value, the lower the loss value. Adam's Optimization Model is the model with the highest accuracy value of 98.44%.

References

B. E. Indonesia, “Saham Indonesia.” https://www.idx.co.id/produk/saham/

I. Oktavia and K. Genjar, “FAKTOR-FAKTOR YANG MEMPENGARUHI HARGA SAHAM,” J. Ris. Akunt. Multiparadigma, vol. 6, no. 1, pp. 29”“39, 2019.

JAPFA, “Sekilas JAPFA.” https://www.japfacomfeed.co.id/ (accessed Feb. 22, 2023).

M. T. S. Putra and I. G. A. M. A. D. Putri, “Pengaruh Pengungkapan Corporate Social Responsibility terhadap Nilai Perusahaan dengan Good Corporate Governance sebagai Variabel Pemoderasi,” E-Jurnal Akunt., vol. 32, no. 5, p. 1317, 2022, doi: 10.24843/eja.2022.v32.i05.p15.

P. F and Fawcett, Data Science and it Relationship to Big Data and Data Driven Decision Making. 2013.

A. S. Talita and A. Wiguna, “Implementasi Algoritma Long Short-Term Memory (LSTM) Untuk Mendeteksi Ujaran Kebencian (Hate Speech) Pada Kasus Pilpres 2019,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 19, no. 1, pp. 37”“44, 2019, doi: 10.30812/matrik.v19i1.495.

T. Lattifia, P. Wira Buana, and N. K. D. Rusjayanthi, “Model Prediksi Cuaca Menggunakan Metode LSTM,” JITTER J. Ilm. Teknol. dan Komput., vol. 3, no. 1, pp. 994”“1000, 2022, [Online]. Available: https://ojs.unud.ac.id/index.php/jitter/article/view/85000/43781

Kusrini and L. T. Emha, Algoritma Data Mining. Yogyakarta, 2015.

J. Cao, J., Li, Z., & Li, Financial Time Series Forecasting Model Based in CEEMDAN and LSTM. Physic A: Statistical Mechanics and its Applications. 2019.

R. D. W. Santosa, M. A. Bijaksana, and A. Romadhony, “Implementasi Algoritma Long Short-Term Memory ( LSTM ) untuk Mendeteksi Penggunaan Kalimat Abusive Pada Teks Bahasa Indonesia,” e-Proceeding Eng., vol. 8, no. 1, pp. 691”“702, 2021.

D. D. Pramesti, D. C. R. Novitasari, F. Setiawan, and H. Khaulasari, “Long-Short Term Memory (Lstm) for Predicting Velocity and Direction Sea Surface Current on Bali Strait,” BAREKENG J. Ilmu Mat. dan Terap., vol. 16, no. 2, pp. 451”“462, 2022, doi: 10.30598/barekengvol16iss2pp451-462.

A. Khumaidi, R. Raafi”™udin, and I. P. Solihin, “Pengujian Algoritma Long Short Term Memory untuk Prediksi Kualitas Udara dan Suhu Kota Bandung,” J. Telemat., vol. 15, no. 1, pp. 13”“18, 2020, [Online]. Available: https://journal.ithb.ac.id/telematika/article/view/340

L. Wiranda and M. Sadikin, “Penerapan Long Short Term Memory Pada Data Time Series Untuk Memprediksi Penjualan Produk Pt. Metiska Farma,” J. Nas. Pendidik. Tek. Inform., vol. 8, no. 3, pp. 184”“196, 2019.

M. K. Wisyaldin, G. M. Luciana, and H. Pariaman, “Pendekatan Long Short-Term Memory untuk Memprediksi Kondisi Motor 10 kV pada PLTU Batubara,” J. Kilat, vol. 9, no. 2, pp. 311”“318, 2020.

Y. Finance, “Saham History JAPFA.” https://finance.yahoo.com/quote/JPFA.JK/history/

L. Gao, Z. Guo, H. Zhang, X. Xu, and H. T. Shen, “Video Captioning with Attention-Based LSTM and Semantic Consistency,” IEEE Trans. Multimed., vol. 19, no. 9, pp. 2045”“2055, 2017, doi: 10.1109/TMM.2017.2729019.

A. Pulver and S. Lyu, “LSTM with working memory,” Proc. Int. Jt. Conf. Neural Networks, vol. 2017-May, pp. 845”“851, 2017, doi: 10.1109/IJCNN.2017.7965940.

Downloads

Additional Files

Published

2023-03-10

Issue

Vol. 8 No. 1 (2023)

Section

Articles

License

Authors who publish with this journal agree to the following terms:

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).