Penerapan Hybrid Metode ARFIMA-ANN Menggunakan Algoritma Backpropagation pada Peramalan Indeks Harga Saham Gabungan

Rayhanul Jannah Buhungo; Isran K Hasan; Nurwan Nurwan

doi:10.37905/euler.v12i2.28474

Penerapan Hybrid Metode ARFIMA-ANN Menggunakan Algoritma Backpropagation pada Peramalan Indeks Harga Saham Gabungan

Rayhanul Jannah Buhungo, Isran K Hasan, Nurwan Nurwan

Abstract

The Composite Stock Price Index (IHSG) is a of the key indicator a country uses to assess its economic condition. The fluctuating movements of stock prices create uncertainly in the stock market, complicating decision-making for investors and government entities. Therefore, there is a need for a method that can forecast the Composite Stock Price Index to monitor such fluctuations. The objective of this study is to model the Composite Stock Price Index Utilizing a hybrid method and to assess the accuracy of this hybrid approach. The hybrid method employed is the Autoregressive Fractionally Integrated Moving Average (ARFIMA)-Artificial Neural Network (ANN). The results of this study show that the best ARFIMA model is ARFIMA (1,d,1) with a differencing parameter of d_R/S = 0,362. The ANN model's optimal architecture obtained through the backpropagation algorithm is ANN (3,2,1). The accuracy of the hybrid ARFIMA-ANN model, measured by the Mean Absolute Percentange Error (MAPE), yielded of 1,0164%, lower than the MAPE value of 1,7326% for the standalone ARFIMA model. This suggests that the hybrid model improves forecasting accuracy and is the most efferctive model for predicting the IHSG.

Keywords

ARFIMA; ANN; Hybrid; Composite stock price Index

Full Text:

PDF

References

U. Tarumanagara, "Faktor-Faktor Yang Mempengaruhi Pengambilan Keputusan Investasi Saham" J. Ekon., vol. XXV, no. 2, pp. 251-269, 2020, [Online]. Available: doi.org/10.24912/je.v25i2.669

Y. A. Putri, M. Muchtar, and P. R. Sihombing, "Analisis Makroekonomi Terhadap Pergerakan Indeks Harga Saham Gabungan ( IHSG) Di Indonesia" J. Bayesian, vol. 3, no. 2, pp. 210-223, 2023, [Online]. Available: doi: 10.46306/bay.v3i2.62

H. Handika and A. Damajanti, "Faktor Penentu Fluktuasi Indeks Harga Saham Gabungan ( IHSG ) Di Bursa Efek Indonesia ( BEI )" J. SOLUSI, vol. 19, no. 3, pp. 153-165, 2021, [Online]. Available: doi: 10.26623/slsi.v19i3.3503

A. Oktavia and M. Y. Fajar, "Peramalan Laju Inflasi , BI Rate dan Indeks Harga Saham Gabungan" J. Ris. Mat., vol. 2, no. 1, pp. 17-24, 2022, [Online]. Available: doi.org/10.29313/jrm.v2i1.789

N. N. Layla and E. Kurniati, "Peramalan Indeks Harga Saham dengan Autoregressive Moving Average Generelized Autoregressive Conditional Heteroscedasticity ( ARMA- GARCH )" J. Ris. Mat., vol. 1, no. 1, pp. 7-12, 2021, [Online]. Available: doi.org/10.29313/jrm.v1i1.103

P. Kartikasari, H. Yasin, and D. A. I. Maruddani, "Autoregressive fractional integrated moving average (arfima) model to predict covid-19 pandemic cases in indonesia" Media Stat., vol. 14, no. 1, pp. 44-55, 2021, doi: 10.14710/medstat.14.1.44-55.

A. Lembang, Ferry Kondo and Sinay, Lexy Janzen and Irfanullah, "ARFIMA Modelling for Tectonic Earthquakes in The Maluku Region" J. Stat. its Appl., vol. 5, no. 1, pp. 39-49, 2021, [Online]. Available: doi: 10.29244/ijsa.v5i1p39-49

W. Aryanti, "Penerapan artificial neural network dengan algoritma backpropagation untuk memprediksi harga saham" J. Ris. Stat., vol. 3, no. 2, pp. 107-118, 2023, [Online]. Available: doi: 10.29313/jrs.v3i2.2953

M. S. Hamid, Muhammad Rifaldhi and Henny, Henny and Said, "Algoritma backpropagation dalam memprediksi jumlah penduduk yang belum melakukan perekaman ktp elektronik di kota kendari" Simtek J. Sist. Inf. dan Tek. Komput., vol. 8, no. 2, pp. 392-397, 2023, [Online]. Available: doi: 10.51876/simtek.v8i2.326

S. Fadhlia, E. P. Hendri, and D. D. C. A, "Model peramalan nilai tukar rupiah terhadap dollar singapura menggunakan metode hybrid arima-ann" J. Lebesgue J. Ilm. Pendidik. Mat. Mat. dan Stat., vol. 5, no. 3, pp. 1513-1523, 2024, [Online]. Available: doi.org/10.46306/lb.v5i3.720

M. R. Susila, M. Jamil, and B. H. Santoso, "Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia" Jambura J. Math., vol. 5, no. 1, pp. 46-58, 2023, [Online]. Available: doi: 10.34312/jjom.v5i1.14889

I. Tahyudin, R. Wahyudi, U. A. Purwokerto, and H. Nambo, "The mortality modeling of covid-19 patients using a combined time series model and evolutionary algorithm" Int. J. Adv. Intell. Informatics, vol. 8, no. 1, pp. 69-83, 2022, doi: 10.26555/ijain.v8i1.669.

I. K. Hasan and I. Djakaria, "Perbandingan Model Hybrid ARIMA-NN dan Hybrid ARIMA-GARCH untuk Peramalan Data Nilai Tukar Petani di Provinsi Gorontalo" J. Stat. dan Apl., vol. 5, no. 2, pp. 155-165, 2021, [Online]. Available: doi: 10.21009/JSA.05204

R. Kamadewi and A. I. Achmad, "Pemodelan Hybrid ARIMA ( Autoregressive Integrated Moving Average ) - ANN ( Artificial Neural Network ) pada Data Inflasi Indonesia Tahun 2009 - 2020" pp. 33-41, 2020.

Dr. R. A. Zalan and Z. S. Yaseen, "Using Fuzzy-ARFIMA Models to Predict Births in Basra Governorate" J. Phys. Conf. Ser., vol. 1963, no. 1, pp. 1-13, 2021, doi: 10.1088/1742-6596/1963/1/012139.

A. F. Syalsabilla, S. Astutik, A. F. Rozy, U. Brawijaya, and P. Korespondensi, "Optimalisasi Prediksi Harga Ihsg Menggunakan Hybrid Weighted Fuzzy Time Series Hidden Markov Model Dengan Algoritma Evolusi Differensial" J. Teknol. dan Inf. ilmu Komput., vol. 11, no. 4, pp. 837-844, 2024, [Online]. Available: doi: 10.25126/jtiik.1148867.

B. Jange, "Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Prophet" J. Manag. Enterpreneursh., vol. 1, no. 2, pp. 53-59, 2022, [Online]. Available: doi: 10.56445/jme.v1i2.18

M. Arimax and D. Variabel, "Peramalan indeks harga saham gabungan (ihsg) menggunakan arimax dengan variabel eksogen covid-19" Pros. Semin. Edusainstech, pp. 258-267, 2020.

M. Viskha and D. U. Wutsqa, "Peramalan Harga Beras Menggunakan Metode Hybrid Autoregressive Integrated Moving Average dan Neural Network (ARIMA-ANN)" J. Kajian dan Terapan Matematika., vol. 9, no. 3, pp. 148-162, 2023.

DOI: https://doi.org/10.37905/euler.v12i2.28474