Prediksi IHSG dengan Backpropagation Neural Network

  • Andy Santoso Universitas Multimedia Nusantara
  • Seng Hansun Universitas Multimedia Nusantara
DOI: https://doi.org/10.29207/resti.v3i2.887
Keywords: backpropagation, IDX composite, investor, prediction, Python

Abstract

IDX Composite is a combination of all common stock and preferred stock which registered on Bursa Efek Indonesia (BEI). IDX Composite is often used by investor to predict the stock price to get profit. But, to predict the stock price is not easy, hence it yields a high risk to investor. This study offers the usage of backpropagation algorithm to minimize the risk. Backpropagation is a supervised algorithm and will be made in Python programming language, in this case, backpropagation will use and learn the past 5 days data to predict the outcome. Also, this study shows that backpropagation have a high accuracy which reflects in Mean Square Error Testing value of 320.49865083640924 to predict IDX Composite using 0.3 learning rate and 3000 epoch.

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References

[1] Darmadji, T. dan Fakhruddin, H.M. 2012. Pasar modal di Indonesia: Pendekatan tanya jawab. Jakarta: Salemba Empat.
[2] Widoatmodjo, S. 2009. Pasar modal Indonesia. Ciawi, Bogor: Ghalia Indonesia.
[3] Suad, H. 2003. Dasar-dasar Teori Portofolio dan Analisis Sekuritas. Yogyakarta: UPP AMP YKPN.
[4] Raharjo, S. 2006. Kiat Membangun Aset Kekakyaan. Jakarta: PT. Gramedia.
[5] Abdillah, H. 2011. Pengaruh Variabel Fundamental dan Teknikal Terhadap Harga Saham Perbankan yang Go Public di Bursa Efek Indonesia Periode 2007-2009. Skripsi. Universitas Pembangunan Nasional Jawa Timur.
[6] Latifah, L.N. 2016. Jaringan Syaraf Tiruan Algoritma Backpropagation Untuk Peramalan Harga Index Saham Syariah Pada Bursa Efek Indonesia. Skripsi. Fakultas Sains dan Teknologi. Universitas Sunan Kalijaga, Yogyakarta.
[7] Novita, A. 2016. Prediksi Pergerakan Harga Saham Pada Bank Terbesar di Indonesia dengan Metode Backpropagation Neural Network. JUTISI, 5(1), pp.965-972.
[8] Malyadi, M., Novawati, N.R., dan Purnama, R.B. 2017. Perancangan Prediksi Untuk menentukan Indeks Harga Saham Menggunakan Jaringan Syaraf Tiruan. Jurnal KINETIK, 2(2).
[9] Kristanto, A. 2004. Jaringan Syaraf Tiruan (Konsep Dasar, Algoritma dan Aplikasi). 1st ed. Yogyakarya: Gava Media.
[10] Fausett, L.V. 2004. Fundamentals of neural networks: Architectures, algorithms, and applications. Delhi, India: Pearson Education.
[11] Hansun, S. 2013. Peramalan Data IHSG Menggunakan Metode Backpropagation. ULTIMATICS, 5(1), pp.26-30.
[12] Hansun, S. 2013. Jakarta Stock Exchange Forecasting using Backpropagation Neural Networks. Prosiding 2013 IEEE International Conference on Electronics Technology and Industrial Development. 23-24 Oktober 2013, Bali, Indonesia.
[13] Sunariyah. 2006. Pengantar pengetahuan pasar modal. Yogyakarta: UPP Akademi Manajemen Perusahaan YKPN.
[14] Siang, J. 2009. Jaringan Syaraf Tiruan & Pemrogramannya Menggunakan Matlab. Yogyakarta: Penerbit Andi.
Published
2019-08-12
How to Cite
Santoso, A., & Hansun, S. (2019). Prediksi IHSG dengan Backpropagation Neural Network. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(2), 313 - 318. https://doi.org/10.29207/resti.v3i2.887
Issue
Vol 3 No 2 (2019): Agustus 2019
Section
Information Technology Articles

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