Stock Projection Using ANFIS Method (Case Study of PT. Kalbe Farma, Tbk) Proyeksi Saham Menggunakan Metode ANFIS (Studi Kasus PT. Kalbe Farma, Tbk)

Main Article Content

antamil

Abstract

Sepanjang tahun 2020 dunia dihadapkan pada tantangan pandemi dan resesi ekonomi. Respon market menimbulkan kekuatiran dan berbagai pertanyaan tentang masa depan pasar modal tidak terkecuali pasar saham di Indonesia. Informasi mengenai harga termasuk prediksi kedepan sangat diperlukan oleh investor ritel maupun perusahaan investasi agar dapat menentukan arah investasinya dengan return yang optimal. Dalam melakukan prediksi dipilih metode ANFIS (Adaptive Neuro Fuzzy Inference System) karena merupakan salah satu metode yang dapat digunakan untuk menganalisis data non linier masa lalu. Syarat dari pemodelan berbasis ANFIS adalah data berdasarkan input dan output. Dengan asumsi bahwa telah diperoleh data input dan output dari masa sebelumnya maka if-then rules dapat diterapkan sehingga diperoleh hasil proyeksi harga saham yang presisi

Article Details

Section
Articles

References

Akkaya, E. 2016 “ANFIS based prediction model for biomass heating value using proximate analysis components,” Fuel, vol. 180, pp. 687–693. doi: https://doi.org/10.1016/j.fuel.2016.04.112.

Billah, M. Waheed, S. and Hanifa, A. 2016. “Stock market prediction using an improved training algorithm of neural network,” in 2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE). pp. 1–4, doi: 10.1109/ICECTE.2016.7879611.Boyacioglu M. A. and Avci, D. 2010. “An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange,” Expert Syst. Appl., vol. 37, no. 12, pp. 7908–7912, doi: https://doi.org/10.1016/j.eswa.2010.04.045.

Cheng, C.-H. et al. 2013. “OWA-based ANFIS model for TAIEX forecasting,” Econ. Model., vol. 30, pp. 442–448,doi: https://doi.org/10.1016/j.econmod.2012.09.047.

Desmonda, D. Tursina, T. and Irwansyah, M. A. 2018. “Prediksi Besaran Curah Hujan Menggunakan Metode Fuzzy Time Series,” J. Sist. dan Teknol. Inf., vol. 6, no. 4, p. 141, doi: 10.26418/justin.v6i4.27036.

Farida, Y. 2016. “Sistem Prediksi Saham Menggunakan Adaptive Neuro Fuzzy Inference System (Studi Kasus Saham Mingguan PT Astra Agro Lestari,Tbk),” Syst. Inf. Syst. Informatics J., vol. 2, no. 2, pp. 35–39, doi: 10.29080/systemic.v2i2.113.

Li, B. Lee, Y. Yao, W. Lu, Y. and Fan, X. 2020. “Development and application of ANN model for property prediction of supercritical kerosene,” Comput. Fluids, vol. 209, p. 104665. doi: https://doi.org/10.1016/j.compfluid.2020.104665.

Montaño Moreno, J. J. Palmer Pol, A. Sesé Abad, A. and Cajal Blasco, B. 2013. “Using the R-MAPE index as a resistant measure of forecast accuracy,” Psicothema, vol. 25, no. 4, pp. 500–506. doi: 10.7334/psicothema2013.23.

Naderloo, L. et al. 2012. “Application of ANFIS to predict crop yield based on different energy inputs,” Measurement, vol. 45, no. 6, pp. 1406–1413, 2012, doi: https://doi.org/10.1016/j.measurement.2012.03.025.

Salisu, A. A. , Sikiru, A. A. and Vo, X. V. 2020. “Pandemics and the emerging stock markets,” Borsa Istanbul Rev., vol. 20, pp. S40–S48, doi: 10.1016/j.bir.2020.11.004.

Seputra, Y. E. A. and Meirinaldi, 2020. “Prediksi Indeks Gabungan Harga Saham (ISHG) Menggunakan Adaptive Neural Fuzzy Inference System (ANFIS),” J. Ekon., vol. 22, no. 2, pp. 131–146.

Wibawa, A.P. Qonita, A. Dwiyanto, F. A. and Haviluddin. 2018 “Perbandingan Metode Prediksi pada Bidang Bisnis dan Keuangan,” Pros. Semin. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 1, pp. 129–133.

Wibowo, A and M. S. Mardiyanto. 2012. “Penerapan Metode Adaptive-Network Based Fuzzy Inference System (Anfis) Model Sugeno Untuk Memprediksi Index Saham: Studi Kasus Saham Lq45 Idx,” in Seminar Nasional Inovasi dan Teknologi (SNIT) 2012 PENERAPAN. pp. 136–142.

Yetis, Y. Kaplan, H. and Jamshidi, M. 2014. “Stock market prediction by using artificial neural network,” in 2014 World Automation Congress (WAC). pp. 718–722, doi: 10.1109/WAC.2014.6936118.

Zhang, Y. Shen, Z. Zhang, G. Song, Y. and Zhu, Y. 2017. “Short-term prediction for opening price of stock market based on self-adapting variant PSO-Elman neural network,” in 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). pp. 225–228, doi: 10.1109/ICSESS.2017.8342901.