### APLIKASI JARINGAN SARAF TIRUAN DAN PARTICLE SWARM OPTIMIZATION UNTUK PERAMALAN INDEKS HARGA SAHAM BURSA EFEK INDONESIA

Desy Wartati

^{(1*)}, Nur Aini Masruroh

^{(2)}

(1)

(2) SCOPUS ID= 9846464900, Gadjah Mada University, Yogyakarta

(*) Corresponding Author

#### Abstract

*Jakarta Composite Index (JCI) is the main stock index in Indonesia Stock Exchange, which indicates the movement of the performance of all stocks listed. The data of stock price index often experience rapid fluctuations in a short time, so it is needed to carry out an analysis to help investor making the right investment decisions. Forecasting JCI is one of the activities that can be done because it helps to predict the value of the stock price in accordance with the past patterns, so it can be a consideration to make a decision. In this research, there are two forecasting models created to predict JCI, which are Artificial Neural Network (ANN) model with (1) Backpropagation algorithm (BP) and (2) Backpropagation algorithm model combined with Particle Swarm Optimization algorithm (PSO). The development of both models is done from the stage of the training process to obtain optimal weights on each network layer, followed by a stage of the testing process to determine whether the models are valid or not based on the tracking signals that are generated. ANN model is used because it is known to have the ability to process data that is nonlinear such as stock price indices and PSO is used to help ANN to gain weight with a fast computing time and tend to provide optimal results. Forecast results generated from both models are compared based on the error of computation time and forecast error. ANN model with BP algorithm generates computation time of training process for 4,9927 seconds with MSE of training and testing process is respectively 0,0031 and 0,0131, and MAPE of forecast results is 2,55%. ANN model with BP algorithm combined with PSO generates computation time of training process for 4,3867 seconds with MSE of training and testing process is respectively 0,0030 and 0,0062, and MAPE of forecast result is 1,88%. Based on these results, it can be concluded that ANN model with BP algorithm combined with PSO provides a more optimal result than ANN model with BP algorithm.*

#### Keywords

#### Full Text:

PDF#### References

Abdulmajeed, A.A., Narhi, T.O., Vallitu, P.K., and Lassila L.V., 2011, The Effect of High Fiber Fraction on Some Mechanical Properties of Unidirectional Glass Fiber-Reinforced Composite*. J. Dent. Materials*. 27 : 313-321.

Abolhassani, A.T., dan Yaghoobi, M., 2010, Stock Price Forecasting Using PSO SVM, *Advanced Computer Theory and Engineering, *pp. 352-356.

Adebiyi, A.A., Adewumi, A.O., dan Ayo, C.K., 2014, Stock Price Prediction Using The ARIMA Model, *Computer Society*, pp. 106-112.

Asriningtias, S.R., Dachlan, H.S., dan Yudaningtyas, E., 2015, Optimasi Training Neural Network Menggunakan Hybrid Adaptive Mutation PSO-BP, *Jurnal Electrics, Electronics, Communications, Controls, Informatics, Systems* , 9(1), pp. 79-84.

Banarjee, D., 2014, Forecasting of Indian Stock Market Using Time-Series ARIMA Model,* Business and Information Management*, pp. 131-135.

Chen, C.H., 1994, Neural Networks for Financial Market Prediction, *Neural Networks*, 2, pp. 1199-1202.

Chen, M.Y., Fan, M.H., Chen, Y.L., dan Wei, H.M., 2013, Design of Experiments on Neural Network’s Parameters Optimization for Time Series Forecasting in Stock Markets, *Neural Network World*, 23(4), pp. 369-393.

Cortes, C. dan Vapnik, V., 1995, Support Vector Networks, *Machine Learning,* 20(3), pp. 273–295.

Deng, W., Wang, G., Zhang, X., Xu, J., dan Li, G., 2016, A Multi-Granularity Combined Prediction Model Based on Fuzzy Trend Forecasting and Particle Swarm Techniques, *Neurocomputing*, 173(3), pp. 1671–1682.

Devi, K.N., Bhaskaran, V.M., dan Kumar, G.P., 2015, Cuckoo Optimized SVM for Stock Market Prediction, *2015 International Conference on Innovations in Information, Embedded and Communication Systems Proceedings*, pp. 1-5.

Hsieh, L.F., Hsieh, S.C., dan Tai, P.H., 2011, Enhanced Stock Price Variation Prediction Via DOE and BPNN-based Optimization, *Expert Systems with Application*, 38(11), pp. 14178-14184.

Hsu, C.M., 2011, Forecasting Stock/Futures Prices by Using Neural Networks with Feature Selection, *International Information Technology and Artificial Intelligence 2011 Conference Proceedings*, pp. 1-7.

Jabin, S., 2014, Stock Market Prediction Using Feed-forward Artificial Neural Network, *International Journal of Computer *Applications, 99(9), pp. 4-8.

Khirbat, G., Gupta, R., dan Singh, S., 2013, Optimal Neural Network Architecture for Stock Market Forecasting,* Communication Systems and Network Technologies 2013 Proceedings*, pp. 557-561.

Kurniawati, L.Y., Tjandrasa, H., dan Arieshanti, I., 2013, Prediksi Pergerakan Harga Saham Menggunakan Support Vector Regression, *Jurnal Teknologi Informasi dan Komunikasi*, 8(2), pp. 11-21.

Kustodian Sentral Efek Indonesia, 2016, *Raih Rekor Baru, Jumlah Investor Tercatat Naik 26%*, http://www.ksei.co.id, (diakses 16 Desember 2016).

Neto, M., Petry, G.G., Aranildo, R.L., dan Ferreira, T.A., 2009, Combining Artificial Neural Network and Particle Swarm System for Time Series Forecasting, *International Joint Conference on Neural Networks, *pp. 2230-2237.

Nugraha, H.G., dan Azhari, S.N., 2014, Optimasi Bobot Jaringan Saraf Tiruan Menggunakan Particle Swarm Optimization, *Indonesian Journal of Computing and Cybernetics Systems*, 8(1), pp. 25-36.

Ratyanaka, R.M.K.T., Seneviratne, D.M.K.N., Jianguo, W., dan Arumawadu, H.I., 2015, A Hybrid Statistical Approach for Stock Market Forecasting Based on Artificial Neural Network and ARIMA Time Series Models,* Behavioral*, *Economic, and Socio-Cultural Computing*,* *pp. 54-60.

Sadono, Y.A., 2016, *BEI: Jumlah Investor Pasar Modal Domestik Mencapai 500.037*, http://www.antaranews.com/berita/584991/bei-jumlah-investor-pasar-modal-domestik-mencapai-500037, (diakses 4 Desember 2016).

Singh, P., dan Borah, B., 2014, Forecasting Stock Index Price Based on M-factors Fuzzy Time Series and Particle Swarm Optimization, *International Journal of Approximate Reasoning, *55(3), pp. 812-833.

Sirijunyapong, W., Leelasantitham, A., Kiattisin, S., dan Wongseree, W., 2014, Predict The Stock Exchange of Thailand-Set, *Information and Communication Technology,Electronic and Electrical Engineering*, pp. 978-982.

*Technological, Forecasting, and Social Change*, 69(1), pp. 71-87.

DOI: https://doi.org/10.22146/teknosains.27616

#### Article Metrics

Abstract views : 1506 | views : 1593### Refbacks

- There are currently no refbacks.

Copyright (c) 2017 Desy Wartati, Nur Aini Masruroh

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

**Copyright © 2019 Jurnal Teknosains ** Submit an Article Tracking Your Submission

Editorial Policies Publishing System Copyright Notice Site Map Journal History Visitor Statistics Abstracting & Indexing