Gold Price Prediction Using Support Vector Regression

Authors

  • Yupie Kusumawati Universitas Dian Nuswantoro Semarang
  • Karis Widyatmoko Universitas Dian Nuswantoro
  • Candra Irawan Universitas Dian Nuswantoro Semarang

DOI:

https://doi.org/10.33633/jais.v7i1.6124

Abstract

In this modern era, one of the businesses that continues to grow is investment. Gold has a more stable value. In Indonesia, there are futures exchange companies that offer gold investment with an online transaction system (E-Trade). The amount of demand and supply, the rate of inflation, economic conditions, and many more can affect the high and low prices of gold. Due to changes in the conditions above, the price of gold may increase, decrease, or remain constant every day. The price of gold that can go up and down causes the need for gold price predictions so that future gold trading investment prospects can be seen. In this final project, the accuracy of Support Vector Regression will be investigated to find out how accurate it is in predicting gold prices with High, Low, Open, Close, and Volume variables. Based on the calculation of the best RMSE in the study, it was found that the best RMSE was to use a Linear kernel with a C of 35 and using a Y variable dataset of 7.4615. The Support Vector Regression Algorithm can predict quite well, as evidenced by the acquisition of fairly good RMSE results. It is necessary to do a simulation of buying and selling gold based on the prediction results and comparing the advantages of the testing data and the actual data.

References

Dwi, N. (2015). Penerapan Algoritma Support Vector Machine untuk Prediksi Harga Emas. Jurnal Informatika Upgris, 1(1), 10–19. https://doi.org/10.26877/jiu.v1i1 Juni.805

Kusumodestoni, R. H., & Sarwido, Komparasi Support Vector Machines (SVM) dan Neural Network Untuk Mengetahui Tingkat Akurasi Prediksi Tertinggi Harga Saham, 2017.

Guntur, S. R., Kumar, N. S. S., Hegde, M., & Dirsala, V, R, In Vitro Studies of the Antimicrobial and Free-Radical Scavenging Potentials of Silver Nanoparticles Biosynthesized From the Extract of Desmostachya bipinnata, 2018.

Yang, Y. and Liu, 1999, “Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval “, (SIGIR'99, pp 42--49).

28. Kim S, Jhong JH, Lee J, Koo JY. Meta-analytic support vector machine for integrating multiple omics data. BioData Min. 2017;10(1):2.

Enri, U. (2018). Optimasi Parameter Support Vector Machines Untuk Prediksi Nilai Tukar Rupiah Terhadap Dollar Amerika Serikat. Jurnal Gerbang, 8(1), 12–16.

Pudjianto, E., & Supriyanto, C. (2015). Prediksi harga komoditas emas dan batubara di pasar dunia dengan algoritma support vector machine. 11(April), 13–23.

Sari, E. P. (2016). Model Prediksi Harga Saham Media Sosial Berdasarkan Algoritma Svm Yang Dioptimasikan Dengan Pso. None, 12(2), 161–170.

Panggabean, V., Nababan, E., & Bu, F. (2013). Analisis Fundamental Dan Analisis Teknikal Pada Investasi Trading Emas Online Dengan Value At Risk. Saintia Matematika, 1(4), 369–382.

Ahmad, A. (2017). Mengenal Artificial Intelligence, Machine Learning, Neural Network, dan Deep Learning. October.

Yasin, H., Prahutama, A., & Utami, T. W. (2014). Prediksi Harga Saham Menggunakan Support Vector Regression Dengan Algoritma Grid Search. Media Statistika, 7(1), 29–35. https://doi.org/10.14710/medstat.7.1.29-35

Sari, Y. (2018). Prediksi Harga Emas Menggunakan Metode Neural Network Backropagation Algoritma Conjugate Gradient. Jurnal ELTIKOM, 1(2), 64–70. https://doi.org/10.31961/eltikom.v1i2.21

Azam, D. F., Ratnawati, D. E., & Adikara, P. P. (2018). Prediksi Harga Emas Batang Menggunakan Feed Forward Neural Network Dengan Algoritme Genetika. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(8), 2317–2322.

Zulfikar, W. B., & Lukman, N. (2016). Perbandingan Naive Bayes Classifier Dengan Nearest Neighbor Untuk Identifikasi Penyakit Mata. Jurnal Online Informatika, 1(2), 82–86. https://doi.org/10.15575/join.v1i2.33

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Published

2022-05-19

Issue

Vol. 7 No. 1 (2022): Journal of Applied Intelligent System

Section

Articles

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