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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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MODEL PREDIKSI HARGA SAHAM MEDIA SOSIAL BERDASARKAN ALGORITMA SVM YANG DIOPTIMASIKAN DENGAN PSO
Keywords:
Particle Swarm Optimization, PSO, Support Vector Machine, SVMPSO, Prediksi Harga Saham
Abstract
Saham adalah satuan nilai atau pembukuan dalam berbagai instrumen financial yang mengacu pada bagian kepemilikan sebuah perusahaan. Dengan menerbitkan saham, memungkinkan perusahaan-perusahaan yang membutuhkan pendanaan jangka panjang untuk ‘menjual’ kepentingan dalam bisnis saham (efek ekuitas) – dengan imbalan uang tunai. Ini adalah metode utama untuk meningkatkan modal bisnis selain menerbitkan obligasi. Saham dijual melalui pasar primer (primary market) atau pasar sekunder (secondary market). Penelitian ini bertujuan untuk membuktikan penerapan optimasi Particle Swarm Optimization (PSO) pada Support Vector Machine (SVM) dapat memberikan keakurasian yang lebih baik dari pada hanya menggunakan algoritma SVM dalam memprediksi harga saham. Setelah dilakukan pengujian dengan menggunakan dua model yaitu algoritma SVM dan SVMPSO setelah sebelumnya ditemukan parameter-parameter optimasi pada SVM, maka dari eksperimen yang dilakukan terlihat bahwa kernel yang memberikan hasil terbaik adalah DOT dengan memberikan nilai akurasi sebesar 94.8% untuk training, 94.6% untuk testing dibandingkan dengan hasil yang diperoleh dari kernel RBF sebesar 94.4% untuk training dan 90.5% untuk testing dan kernel Polynomial sebesar 58.6% untuk training dan 47.3% untuk testing.
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Alamili, M., (2011). Exchange Rate Prediction using Support Vector Machines. Technische Universiteit Delft.
Aydin, I., Karakose, M., Akin, E., (2011). A Multi-Objective Artificial Immune Algorithm for Parameter Optimization in Support Vector Machine, Applied Soft Computing 11, 120-129.
Bodt, E., Rynkiewicz, J., Cottrell, M. (2001). Some known facts about financial data. European symposium on artificial neural networks 25-27. pp. 223-236.
Bramer, M., (2007). Principles of Data Mining. London : Springer.
Jung Hsieh, T., Fen Hsiao, H., Chang Yeh, W., (2012). Mining Financial Distress Trend Data using Penalty Guided Support Vector Machines based on Hybrid of Particle Swarm Optimization and Artificial Bee Colony Algorithm, Neurocomputing 82, 196-206.
Kecman, V., (2001). Learning and Soft Computing. The MIT Press Cambridge, Massachusetts London, England.
Kim, K. (2003). Financial time series forecasting using Support Vector Machines. Neurocomputing 55, 307-319.
Larose, D. T., (2005). “ Discovering Knowledge in Data,” . Canada : Wiley Interscience .
Lento, C., Gradojevic , N., (2007) “The Profitability Of Technical Trading Rules: A Combined Signal Approach,” vol. 23, no. 1, pp. 13–28.
Liu, L. and Wang, W. (2008). Exchange Rates Forecastiing with Least Squares SVM. International Conference on Computer Science and Software Engineering.
Lo, A. W., Mamaysky, H., Wang, J., (2000). “ Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation,” The Journal of Finance, Vol LV, No.4.
Pilbeam, K. (2010). Finance and Financial Markets.Palgrave (ISBN-13 978-0230233218).
Premanode, B. and Toumazou, C., (2013) “Improving prediction of exchange rates using Differential EMD,” Expert Systems with Applications, vol. 40, no. 1, pp. 377–384.
Oanda Corporation (2013). Historical foreign exchange data . Source: https://fxtrade.oanda.com/trade-forex/fxtrade/historical-data
Ratna Wati, D. A., (2011). Sistem Kendali Cerdas. Yogyakarta : Graha Ilmu.
Santosa, B., (2007). Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta : Graha Ilmu.
Sewell, M. and Shawe-Taylor, J., (2012). “Forecasting foreign exchange rates using kernel methods,” Expert Systems with Applications, vol. 39, no. 9, pp. 7652–7662.
Shioda, K., Deng, S. and Sakurai, A. , (2011). “Prediction of Foreign Exchange Market States with Support Vector Machine,” 2011 10th International Conference on Machine Learning and Applications and Workshops, vol. 1, pp. 327–332.
Supriyanto, H., (2012). “Implementasi Support Vector Machines untuk Memprediksi Arah Pergerakan Harga Harian Valuta Asing (EUR/USD, GBP/USD, dan USD/JPY) dengan Metode Kernel Trick menggunakan Fungsi Kernel Radial Basis Function,” pp. 1–4.
Susanto, S., Suryadi, D,. (2010). Pengantar Data Mining menggali Pengetahuan dari Bongkahan Data. Yogyakarta : Andi Offset.
Thissen, U., van Brakel, R., de Weijer, A.P., Melssen, W.J., Buydens, L.M.C. (2003). Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems 69, 35 – 49.
Wei Fei, S., Bin Miao, Y. and Liang Liu, C,. (2009). Chinese Grain Production Forecasting Method Based on Particle Swarm Optimization-based Support Vector Machine, Recent Patents on Engineering, 3, 8 – 12.
Yu, L., Wang, S., Huang, W. and Keung Lai, K., (2007). “Are Foreign Exchange Rates Predictable? A Survey From Artificial Neural Networks Perspective,” Scientific Inquiry, vol. 8, no. 2, pp. 207 – 228
Yuan Shu, L., Jinn Horng, S., He, M., Fan, P., Wann Kao, T., Khan, M. K., Shine Run, R., Lin Lai, J., Jian Chen, R., (2011). Mutual Funds Trading Strategy based on Particle Swarm Optimization, Expert System with Applications 38, 7582 – 7602.
Yusup, N., Zain, A. M. and Hashim, S. Z. M., (2012). “Overview of PSO for Optimizing Process Parameters of Machining,” Procedia Engineering, vol. 29, pp. 914–923.
Zhou, J., Fang, R., Li, Y., Zhang, Y., Peng, B., (2009). Parameters Optimization of Nonlinear Grey Bernoulli Model using Particle Swarm Optimization, Applied Mathematics and Computation 207, 292-299.
Zŭperl, U., Cŭs, F., & Gecevska, V. (2007). Optimization of the characteristic parameters in milling using the PSO evolution technique. Journal of Mechanical Engineering 6, 354-368
Published
2016-09-15
How to Cite
Sari, E. (2016). MODEL PREDIKSI HARGA SAHAM MEDIA SOSIAL BERDASARKAN ALGORITMA SVM YANG DIOPTIMASIKAN DENGAN PSO. Jurnal Pilar Nusa Mandiri, 12(2), 161-170. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/270
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Articles
Copyright (c) 2016 Eka Puspita Sari
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