Prediction of stock purchase decisions using artificial neural network method

(1) * U Pujianto Mail (Department of Electrical Engineering, Universitas Negeri Malang, Indonesia)
(2) D P P Setyadi Mail (Department of Electrical Engineering, Universitas Negeri Malang, Indonesia)
(3) M I Akbar Mail (Department of Electrical Engineering, Universitas Negeri Malang, Indonesia)
*corresponding author

Abstract


The difficulty of determining a stock purchase decision is a problem to benefit from stock transactions. This study aims to give a person's decision to buy one of the issuer's company shares to get a profit on the same day. The dataset used in this study came from the investing.com website in the form of daily data shares of PT Indofood CBP Sukses Makmur Tbk with ICBP stock code for the period January - September 2019. The attributes used in this study were the opening price, highest price, lowest price, closing price, transaction volume, day representation, and decisions. The dataset that has been collected was normalized using the Min-Max method to facilitate data processing. This research used the backpropagation neural network method and used the 10-Fold Cross Validation and Confusion Matrix for validation. The results of this study indicate that the backpropagation neural network method uses the bipolar activation function with training cycles of 2000 and learning rate of 0.03 has the best performance namely 69.35% of accuracy, 67.65% of precision, 74.19% of recall and 30.65% of error rate for prediction of stock purchase decisions per day in the form of buy or not.

   

DOI

https://doi.org/10.31763/aet.v2i1.686
      

Article metrics

10.31763/aet.v2i1.686 Abstract views : 848 | PDF views : 253

   

Cite

   

Full Text

Download

References


[1] A. Suci, W. 1, E. Masitoh, and Y. Chomsatu, “Faktor-faktor yang mempengaruhi return saham sebelum dan selama pandemi,” INOVASI, vol. 18, no. 1, pp. 85–94, Feb. 2022, doi: 10.30872/JINV.V18I1.10500.

[2] M. A. Boyacioglu and D. Avci, “An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange,” Expert Syst. Appl., vol. 37, no. 12, pp. 7908–7912, Dec. 2010, doi: 10.1016/J.ESWA.2010.04.045.

[3] A. Dwi Amalia, D. Kartikasari, and P. Administrasi Bisnis Terapan Politeknik Negeri Batam, “Analisis Perbandingan Kinerja Saham Perusahaan Manufaktur Terindeks Syariah dan Konvensional,” J. AKUNTANSI, Ekon. dan Manaj. BISNIS, vol. 4, no. 2, pp. 128–135, Dec. 2016, doi: 10.30871/JAEMB.V4I2.69.

[4] H. Yasin, A. Prahutama, T. W. Utami, D. Jurusan, and S. Undip, “Prediksi Harga Saham Menggunakan Support Vector Regression Dengan Algoritma Grid Search,” MEDIA Stat., vol. 7, no. 1, pp. 29–35, Jun. 2014, doi: 10.14710/MEDSTAT.7.1.29-35.

[5] M. Guntur, J. Santony, and Y. Yuhandri, “Prediksi Harga Emas dengan Menggunakan Metode Naïve Bayes dalam Investasi untuk Meminimalisasi Resiko,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 2, no. 1, pp. 354–360, Apr. 2018, doi: 10.29207/RESTI.V2I1.276.

[6] R. R. Fitriani, E. Ernastuti, and E. R. Swedia, “Algoritma Learning Vector Quantization Dan Fuzzy K-Nn Untuk Prediksi Saham Berdasarkan Pesaing,” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 1, pp. 1–9, Dec. 2019, doi: 10.35760/TR.2019.V24I1.1929.

[7] S. Kr.Srivastava and S. Kr.Singh, “Multi-Parameter Based Performance Evaluation of Classification Algorithms,” Int. J. Comput. Sci. Inf. Technol., vol. 7, no. 3, pp. 115–125, Jun. 2015, doi: 10.5121/IJCSIT.2015.7310.

[8] N. Atthina and L. Iswari, “Klasterisasi Data Kesehatan Penduduk untuk Menentukan Rentang Derajat Kesehatan Daerah dengan Metode K-Means,” Semin. Nas. Apl. Teknol. Inf., pp. 21–2014, Jun. 2014, Accessed: Mar. 23, 2023. [Online]. Available at : journal.uii.ac.id.

[9] J. Syaraf et al., “Jaringan Syaraf Tiruan Perambatan Balik Untuk Pengenalan Wajah,” JSINBIS (Jurnal Sist. Inf. Bisnis), vol. 6, no. 1, pp. 30–37, Nov. 2016, doi: 10.21456/VOL6ISS1PP30-37.

[10]M. Agustin and T. Prahasto, “Penggunaan Jaringan Syaraf Tiruan Backpropagation Untuk Seleksi Penerimaan Mahasiswa Baru Pada Jurusan Teknik Komputer Di Politeknik Negeri Sriwijaya,” JSINBIS (Jurnal Sist. Inf. Bisnis), vol. 2, no. 2, pp. 089–097, Jun. 2012, doi: 10.21456/VOL2ISS2PP089-097.

[11] “Vol 1 No 2 (2012): Komputa : Jurnal Ilmiah Komputer dan Informatika | Komputa : Jurnal Ilmiah Komputer dan Informatika.”,doi : 10.34010/komputa.v1i2.

[12]Y. Bing, J. K. Hao, and S. C. Zhang, “Stock Market Prediction Using Artificial Neural Networks,” Adv. Eng. Forum, vol. 6–7, pp. 1055–1060, Sep. 2012, doi: 10.4028/WWW.SCIENTIFIC.NET/AEF.6-7.1055.

[13]J. (Julpan) Julpan, E. B. (Erna) Nababan, and M. (Muhammad) Zarlis, “Analisis Fungsi Aktivasi Sigmoid Biner dan Sigmoid Bipolar dalam Algoritma Backpropagation pada Prediksi Kemampuan Siswa,” Teknovasi, vol. 2, no. 1, pp. 103–116, 2015, Accessed: Mar. 23, 2023. [Online]. Available: neliti.com.

[14]J. G. Moreno-Torres, J. A. Saez, and F. Herrera, “Study on the impact of partition-induced dataset shift on k-fold cross-validation,” IEEE Trans. Neural Networks Learn. Syst., vol. 23, no. 8, pp. 1304–1312, 2012, doi: 10.1109/TNNLS.2012.2199516.

[15]V. M. Patro and M. R. Patra, “Augmenting Weighted Average with Confusion Matrix to Enhance Classification Accuracy,” Trans. Eng. Comput. Sci., vol. 2, no. 4, pp. 77–91, Aug. 2014, doi: 10.14738/TMLAI.24.328.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 U Pujianto, D P P Setyadi, M I Akbar

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Applied Engineering and Technology
ISSN: 2829-4998
Email: aet@ascee.org | andri.pranolo.id@ieee.org
Published by: Association for Scientic Computing Electronics and Engineering (ASCEE)
Organized by: Association for Scientic Computing Electronics and Engineering (ASCEE), Universitas Negeri Malang, Universitas Ahmad Dahlan

View My Stats AET
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.