IMPLEMENTATION OF AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) METHOD FOR PT XL AXIATA TBK STOCK PRICE PREDICTION WITH WEBSITE-BASED DASHBOARD VISUALIZATION
Abstract
The financial market is a dynamic and uncertain sector, with stocks being one of the most commonly used investment instruments. PT XL Axiata Tbk, a telecommunications company listed on the Indonesia Stock Exchange as a blue chip stock, attracts the attention of many investors due to its financial stability and consistent performance. Technical analysis, particularly the ARIMA (Autoregressive Integrated Moving Average) method is used to predict prices. This research focuses on the use of the ARIMA method in predicting the closing price of PT XL Axiata Tbk shares and the implementation of visualization of prediction results through a web-based dashboard. The results of the analysis obtained the best model for stock prediction is ARIMA (2,1,2) with RMSE and MAPE are 50.743 and 0.01653, respectively. The closing price prediction results for 10 consecutive days are 2,190; 2,194; 2,193; 2,196; 2,194; 2,197; 2,195; 2,197; 2,195; and 2,197. Visualization for the results of this prediction is based on a website with the Streamlit framework that presents the results of stock prediction analysis. The existence of a website-based dashboard visualization can help readers find out the prediction results easily and interactively.
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References
I. K. Nti, A. F. Adekoya, dan B. A. Weyori, A systematic review of fundamental and technical analysis of stock market predictions, vol. 53, no. 4. Springer Netherlands, 2020. doi: 10.1007/s10462-019-09754-z.
A. N. Girsang, H. D. Tambun, A. Putri, D. Rarasati, D. S. S. Nainggolan, dan P. Desi, “Analisis Pengaruh EPS, DPR, dan DER terhadap Harga Saham Sektor Trade, Services, & Investment di BEI,” Jesya (Jurnal Ekon. Ekon. Syariah), vol. 2, no. 2, hal. 351–362, 2019, doi: 10.36778/jesya.v2i2.97.
E. Febrianti, M. Muchran, dan A. Jaya, “Analysis of Optimal Portfolio Formation Using Single Index Model on Stocks Included in the Lq 45 Market Index on the Indonesia Stock Exchange,” Ajar, vol. 5, no. 02, hal. 207–225, 2022, doi: 10.35129/ajar.v5i02.345.
C. Maniil, R. J. Kumaat, dan M. T. B. Maramis, “Pengaruh Inflasi, Suku Bunga Bank Indonesia dan Nilai Tukar Rupuah Terhadap Harga Indeks Saha, LQ45 Pada Bursa Efek Indonesia Periode 2017:Q1-2021Q4,” J. Berk. Ilm. Efisiensi, vol. 23, no. 1, hal. 97–108, 2023, [Daring]. Tersedia pada: https://ejournal.unsrat.ac.id/v3/index.php/jbie/article/view/45165
N. H. E. N. Hendra Perdana, “Analisis Teknikal Saham Lq-45 Menggunakan Indikator Bollinger Bands,” Bimaster Bul. Ilm. Mat. Stat. dan Ter., vol. 8, no. 4, hal. 943–951, 2019, doi: 10.26418/bbimst.v8i4.36653.
J. Purnama dan A. Juliana, “Analisa Prediksi Indeks Harga Saham Gabungan Menggunakan Metode Arima,” Cakrawala Manag. Bus. J., vol. 2, no. 2, hal. 454, 2020, doi: 10.30862/cm-bj.v2i2.51.
V. I. Kontopoulou, A. D. Panagopoulos, I. Kakkos, dan G. K. Matsopoulos, “A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks,” Futur. Internet, vol. 15, no. 8, hal. 1–31, 2023, doi: 10.3390/fi15080255.
S. P. Fauzani dan D. Rahmi, “Penerapan Metode ARIMA Dalam Peramalan Harga Produksi Karet di Provinsi Riau,” J. Teknol. dan Manaj. Ind. Terap., vol. 2, no. 4, hal. 269–277, 2023, doi: 10.55826/tmit.v2i4.283.
Siti Afifatul Farichah, “Analisis Inflasi Di Indonesia: Pendekatan Autoregressive Distributed Lag (Ardl),” J. Cakrawala Ilm., vol. 1, no. 10, hal. 2467–2484, 2022, doi: 10.53625/jcijurnalcakrawalailmiah.v1i10.2577.
M. Q. Hendikawati dan P. Walid, “Time Series Modelling of Stock Price By Modwt-Arima Method Semarang,” UNNES J. Math., vol. 8, no. 2, hal. 79–89, 2019, [Daring]. Tersedia pada: http://journal.unnes.ac.id/sju/index.php/ujm
W. W. S. Wei, Time Series Analysis Univariate and Multivariate Methods, Second Edi. United States of America: Pearson Education, Inc., 2006. doi: 10.1016/B978-008044910-4.00546-0.
S. Aktivani, “Uji Stasioneritas Data Inflasi Kota Padang Periode 2014-2019,” J. Stat. Ind. dan Kompetasi, vol. 6, no. 1, hal. 26–33, 2021.
Li, D. Gumulya, J. S. Sembel, dan M. L. Ginting, “Analisis Peramalan dan Pengelompokan Jumlah Turis ke Jepang,” J. Integr. Syst., vol. 4, no. 2, hal. 150–167, 2021, doi: 10.28932/jis.v4i2.3164.
R. Susanti dan A. R. Adji, “Analisis Peramalan Ihsg Dengan Time Series Modeling Arima,” J. Manaj. Kewirausahaan, vol. 17, no. 1, hal. 97, 2020, doi: 10.33370/jmk.v17i1.393.
B. yafitra Pandji, Indwiarti, dan A. A. Rohmawati, “Perbandingan Prediksi Harga Saham Dengan Model Arima Dan Artificial Neural Network,” Ind. Comput., vol. 4, no. 2, hal. 189–198, 2019, doi: 10.21108/indojc.2019.4.2.344.
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