Comparison of Extreme Learning Machine Methods and Support Vector Regression for Predicting Bank Share Prices in Indonesia

  • Williem Kevin Setiadi Program Studi Teknik Informatika, Universitas Surabaya
  • Vincentius Riandaru Prasetyo Universitas Surabaya
  • Fitri Dwi Kartikasari Program Studi Teknik Informatika, Universitas Surabaya
Keywords: Extreme Learning Machine, Support Vector Regression, Stocks Prediction, Banking Indonesia, MAPE


Investing is the practice of postponing current consumption to obtain more significant value in the future. One profitable form of investment is stock investment, where investors buy company shares to benefit from appreciation in share value or dividend payments. Before investing in shares, investors need to pay attention to movements in the Composite Stock Price Index (IHSG), which reflects the performance of the Indonesian stock market. The Indonesian Stock Exchange (BEI) recorded around 740 companies listed in 2021. The BEI also compiled the LQ45 list of 45 stocks with the largest market capitalization, including the four largest banks in Indonesia. However, investing in bank shares only sometimes produces profits due to share price fluctuations. Stock price analysis and price movement predictions are important steps before investing. Extreme Learning Machine (ELM) and Support Vector Regression (SVR) methods are techniques used to predict time series data. This research compares the performance of the two methods in predicting stock prices of the big 4 Indonesian banks. The dataset used in this research comes from the Yahoo Finance site, which was taken since the market crash recovery period due to the Covid-19 pandemic. Based on the evaluation conducted, both the ELM and SVR methods are effective for predicting the share prices of the big four Indonesian banks. In terms of accuracy, the SVR method outperforms the ELM method due to its superior MAPE value. However, when considering computing time, the ELM method is more efficient than the SVR method.


Download data is not yet available.


I. Mutiara and E. Agustian, "Pengaruh Financial Literacy dan Financial Behavior terhadap Keputusan Investasi pada Ibu-Ibu PKK Kota Jambi," Jurnal Manajemen dan Sains, vol. 5, no. 2, pp. 263-268, 2020.

V. W. Utami and R. Kartika, "Investasi Saham pada Sektor Perbankan adalah Pilihan yang Tepat Bagi Investor di Pasar Modal," Jurnal Sains Sosio Humaniora, vol. 4, no. 2, pp. 894-897, 2020.

Pratama Ikbar, "Tata Kelola Perusahaan dan Atribut Perusahaan pada Ketepatan Pelaporan Keuangan: Bukti dari Perusahaan yang Terdaftar di Bursa Efek Indonesia," Journal of Education, Humaniora and Social Sciences, vol. 4, no. 3, pp. 1959-1967, 2021.

S. J. Ahmad and J. Badri, "Pengaruh Inflasi Dan Tingkat Suku Bunga Terhadap Indeks Harga Saham Gabungan Yang Terdaftar Dibursa Efek Indonesia Pada Tahun 2013-2021," Jurnal Economina, vol. 1, no. 3, pp. 679-689, 2022.

M. R. Hutauruk, "Dampak Situasi Sebelum dan Sesudah Pandemi COVID-19 Terhadap Volatilitas Harga Saham LQ45," Jurnal Riset Akuntansi dan Keuangan, vol. 9, no. 2, pp. 241-252, 2021.

M. C. Fakhlevi and F. Kharisma, "Pengaruh Laba terhadap Utang Perusahaan LQ 45 yang Terdaftar di Bursa Efek Indonesia Periode Tahun 2018-2019," Borneo Student Research, vol. 2, no. 2, pp. 1347-1354, 2021.

R. R. M. Abigail and A. Lubis, "Pengaruh Return On Investment (Roi) Terhadap Harga Saham Pt. Bank Central Asia, Tbk Pada Bursa Efek Indonesiaperiode 2018 –2020," Jurnal Akuntasi dan Keuangan Entitas, vol. 3, no. 2, pp. 1-14, 2023.

Z. Pan, Z. Meng, Z. Chen, W. Gao and Y. Shi, "A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings," Mechanical Systems and Signal Processing, vol. 144, pp. 1-17, 2020.

G. Bathla, "Stock Price prediction using LSTM and SVR," in 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, 2020.

E. Fitri and D. Riana, "Analisa Perbandingan Model Predictiondalam Prediksi Harga Saham Menggunakan Metode Linear Regression, Random Forest Regression Dan Multilayer Perceptron," Jurnal Manajemen Informatika & Komputerisasi Akuntansi, vol. 6, no. 1, pp. 69-78, 2022.

V. P. Ramadhan and F. Y. Pamuji, "Analisis Perbandingan Algoritma Forecasting dalam Prediksi Harga Saham LQ45 PT Bank Mandiri Sekuritas (BMRI)," Jurnal Teknologi dan Manajemen Informatika, vol. 8, no. 1, pp. 39-45, 2022.

S. A. Laksono, A. R. Pratama and Rahmat, "Perbandingan metode linear regresi dan polynomial regresi untuk memprediksi harga saham studi kasus Bank BCA," INFOTECH: Jurnal Informatika & Teknologi, vol. 4, no. 1, pp. 59-70, 2023.

B. Pratama and L. Y. Banowosari, "Perbandingan Metode Extreme Gradient Boosting (Xgboost) Dengan Long Short-Term Memory (Lstm) Untuk Prediksi Saham Pt. Bank Mandiri Tbk. (Bmri)," Journal of Economic, Business and Accounting, vol. 7, no. 3, pp. 5631-5636, 2024.

M. Mazur, M. Dang and M. Vega, "COVID-19 and the march 2020 stock market crash. Evidence from S&P1500," Finance Research Letters, vol. 38, pp. 1-8, 2021.

L. Alfat, H. Hermawan, A. Z. Rustandiputri, R. M. Y. Inzhagi and R. Tandjilal, "Prediksi Saham PT. Aneka Tambang Tbk. dengan K-Nearest Neighbors," Journal Scientific and Applied Informatics, vol. 5, no. 3, pp. 236-243, 2022.

V. R. Prasetyo, S. Axel, J. T. Soebroto, D. Sugiarto, S. A. Winatan and S. D. Njudang, "Prediksi Harga Emas Berdasarkan Data menggunakan Metode Long Short Term Memory," Jurnal Sistem Informasi, vol. 11, no. 3, pp. 623-629, 2022.

V. R. Prasetyo, M. Mercifia, A. Averina, L. Sunyoto and Budiarjo, "Prediksi Rating Film Pada Website Imdb Menggunakan Metode Neural Network," Jurnal Ilmiah NERO, vol. 7, no. 1, pp. 1-8, 2022.

S. N. Aisah, D. C. R. Novitasari and Y. Farida, "Perbandingan Metode Extreme Learning Machine (ELM) danKernel Extreme Learning Machine (KELM) Pada Klasifikasi Penyakit Cedera Panggul," Jurnal Fourier, vol. 12, no. 2, pp. 69-78, 2023.

J. Siswantoro, H. Arwoko and M. Z. F. N. Siswantoro, "Fruits Classification from Image using MPEG-7 Visual Descriptors and Extreme Learning Machine," in 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, 2020.

D. Parbat and M. Chakraborty, "A python based support vector regression model for prediction of COVID19 cases in India," Chaos, Solitons and Fractals, vol. 138, pp. 1-5, 2020.

C. E. d. S. Santos, R. C. Sampaio, L. d. S. Coelho, G. A. Bestard and C. H. Llanos, "Multi-objective adaptive differential evolution for SVM/SVR hyperparameters selection," Pattern Recognition, vol. 110, pp. 1-10, 2021.

X. Ding, J. Liu, F. Yang and J. Cao, "Random radial basis function kernel-based support vector machine," Journal of the Franklin Institute, vol. 358, no. 18, pp. 10121-10140, 2021.

I. Nabillah and I. Ranggadara, "Mean Absolute Percentage Error untuk Evaluasi Hasil Prediksi Komoditas Laut," Journal of Information System, vol. 5, no. 2, pp. 250-255, 2020.

How to Cite
Setiadi, W. K., Prasetyo, V. R., & Kartikasari, F. D. (2024). Comparison of Extreme Learning Machine Methods and Support Vector Regression for Predicting Bank Share Prices in Indonesia. Teknika, 13(2), 219-225.