Implementasi Metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam Prediksi Harga Saham X

Adelia Damayanti, Dwi Agustina

Abstract


Today, the stock market is one of the most important financial vehicles. Investors were concerned about X shares because it fluctuated significantly during Elon Musk's acquisition process. This study was aimed to predict the future price trend of X stocks. Thus, this analysis can assist investors in controlling X stocks. Data for this study were gathered from the Kaggle website. This study uses data from January 2016 to October 2022. The Adaptive Neuro Fuzzy Inference System (ANFIS) will be used to estimate the price of X stocks. The results demonstrated that the ANFIS approach accurately captured the pattern of stock price changes. Based on the accuracy test results, this method has an RMSE of 0.005. It demonstrates that the ANFIS method can accurately anticipate the price of X stock.

Keywords


ANFIS; Investment; Prediction; RMSE; X Stock

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References


P. Wihartati and F. Efendi, “Decision Support System for Share Investment Using The Capital Assetpricing Method (CAPM),” Int. J. Comput. Inf. Syst., vol. 02, no. 01, pp. 19–23, 2021, doi: https://doi.org/10.29040/ijcis.v2i1.25.

I. P. W. Mandala, M. A. Wahyuni, and A. T. Atmaja, “Determinasi Trader Dalam Pengambilan Keputusan Analisis Trading di Pasar Valas (Study Kasus pada Grup Trader Olymp Trade Bitcoin Indonesia),” JIMAT (Jurnal Ilm. Mhs. Akuntansi) Undiksha, vol. 10, no. 1, pp. 161–172, 2019, doi: https://doi.org/10.23887/jimat.v10i1.20557.

J. Jia, H. Pan, and J. Su, “Analysis of Motivations, Process, and Implications of Elon Musk’s Acquisition of Twitter”, BCP Bus. Manag., vol. 47, pp. 145–153, Jul. 2023, doi: https://doi.org/10.54691/bcpbm.v47i.5185.

Y. Zhang, Z. Luo, and R. Ma, “The Impact of Twitter’s Acquisition on Stock Price: An Empirical Study on Event Analysis”, [Online]. Available: http://www.fevision.com/uploads/20231229/17c1b7854ee2900813b0e6f96cead038.pdf.

E. Ong, Technical Analysis For Mega Profit (Hc). Jakarta: Gramedia Pustaka Utama, 2016.

M. Goykhman and A. Teimouri, “Machine learning in sentiment reconstruction of the simulated stock market,” Physica A: Stat. Mech. App.,” vol. 492, pp. 1729–1740, 2018, doi: https://doi.org/10.1016/j.physa.2017.11.093.

J. Z. Wang, J. J. Wang, Z. G. Zhang, and S. P. Guo, “Expert Systems with Applications Forecasting stock indices with back propagation neural network,” Exp. Sys. App., vol 38, no. 11, pp. 14346–14355, 2011, doi: https://doi.org/10.1016/j.eswa.2011.04.222.

P. Singla, D. Kumar, A. M. Rai, and S. Singla, “Local Monsoonal Precipitation forecasting using ANFIS Model: a case study for Hisar,” Int. J. Res. Rev. Comput. Sci., vol. 2, no. 3, pp. 832-838, 2011.

Maulana Rizki, “Prediksi Curah Hujan dan Debit Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS) Studi Kasus Citarum Hulu,” Institut Teknologi Bandung, 2012.

Y. T. Nugraha, K. Ghabriel, and I. F. Dharmawan, “Implementasi ANFIS Dalam Prakiraan Konsumsi Energi Listrik Di Kota Medan Pada Tahun 2030,” Rekayasa Elektr. Energi, vol. 4, no. 1, pp. 55–59, 2021, doi: https://doi.org/10.30596/rele.v4i1.7826.

M. Marfuah, M. Bettiza, and A. Uperiati, “Implementasi Adaptive Neuro Fuzzy Inference System (Anfis) Untuk Prediksi Curah Hujan,” Student Online J., vol. 2, no. 1, pp. 112–118, 2021.

V. I. Osubor and S. C. Chiemeke, “An Adaptive Neuro Fuzzy Inference System for the Diagnosis of Malaria,” Niger. Soc. Exp. Biol., vol. 14, no. 4, p. 11, 2014.

M. Ridwan, “Model Prediksi Harga Lampu Gedung Dalam Penyusunan Penawaran Harga Lelang Building Management Menggunakan Adaptive Neuro Fuzzy Inference System : Studi Kasus PT Garuda Karya Mandiri,” JUTIS (Journal Tek. Inform. UNIS), vol. 4, no. 2, pp. 20–31, 2016.

N. Nurfalinda, E. Oktafiansyah, and A. Uperiati, “Prediksi Pendistribusian Air di Perusahaan Daerah Air Minum (PDAM) dengan Metode Adaptive Neuro Fuzzy Inference System (ANFIS),” J. Sustain. J. Has. Penelit. dan Ind. Terap., vol. 10, no. 1, pp. 32–36, 2021, doi: https://doi.org/10.31629/sustainable.v10i1.1404.

D. Fauziah, I. Irzani, “Aplikasi Adaptive Neuran Fuzzy Inference System (Anfis ) Sebagai Model Diagnosis Konsentrasi Jurusan Pada Siswa SMA/MA,” Pros. Semin. Mat. dan Pendidik. Mat., 2016. pp. 951–965. [Online]. Available: https://jurnal.fkip.uns.ac.id/index.php/snmpm/article/view/10925.

D. Saepuloh, M. Ramdhan, R. Bramawanto, and S. Sukoraharjo, “Metode Adaptive Neuro Fuzzy Inference System Pada Aplikasi Sistem Cerdas Pendugaan Produksi Garam,” J. Kelaut. Nas., vol. 14, no. 2, 2019, doi: https://doi.org/10.15578/jkn.v14i2.7910.

M. Pandya, “Twitter Stocks Dataset,” kaggle, 2022.

L. Afifah, “3 Metode Normalisasi Data (Feature Scaling) di Python,” 2022. [Online]. Available: https://ilmudatapy.com/metodenormalisasi-data/.

Trivusi, “Normalisasi Data: Pengertian, Tujuan, dan Metodenya,” Trivusi. Accessed: Apr. 03, 2023. [Online]. Available: https://www.trivusi.web.id/2022/09/normalisasi-data.html.

M. Rafało, “Cross Validation Methods: Analysis based on Diagnostics of Thyroid Cancer Metastasis,” ICT Express, vol. 8, no. 2, pp. 183-188, 2022, doi: https://doi.org/10.1016/j.icte.2021.05.001.

S. Chidambaram, et al., “Diagnosing Breast Cancer Based on the Adaptive Neuro-Fuzzy Inference System,” Comput. Math. Methods Med., vol. 2022, p. 11, 2022, doi: https://doi.org/10.1155/2022/9166873.

L. V. Fausett, Fundamentals of Neural Network: Architectures, Fundamentals, and Applications. India: Pearson Education, 2006.

L. Vanajakshi dan L. R. Rilett, “A Comparasion of The Performance of Artificial Neural Networks and Support Vector Machines for the Prediction of Traffic Speed,” IEEE Intell. Veh. Symp., pp. 194–199, 2004, doi: https://doi.org/10.1109/IVS.2004.1336380.




DOI: https://doi.org/10.37905/euler.v12i1.25278

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