Analisis Komparasi Model Peramalan Prophet Dan Arima Dalam Memprediksi Harga Saham Penutupan PT ANTM
DOI:
https://doi.org/10.30983/lattice.v5i1.9478Keywords:
ARIMA, Prophet , Saham, Time SeriesAbstract
PT Aneka Tambang Tbk (ANTM) is a major mining company in Indonesia whose shares are actively traded on the Indonesia Stock Exchange. Its stock price is influenced by both internal and external factors. Time series forecasting methods, such as ARIMA and Prophet, are used to predict future stock movements. ARIMA is known for its flexibility and high prediction accuracy, while Prophet is capable of handling missing values and shifting trends, making it suitable for complex financial data. This study aims to compare the performance of ARIMA and Prophet models in forecasting ANTM stock prices. The dataset consists of monthly closing stock prices from January 2016 to May 2025. The models are evaluated using Mean Absolute Percentage Error (MAPE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results show that the ARIMA model performs better to predict PT ANTM’s traded than Prophet model, with lower MAPE, AIC, and BIC values of 7.88, 433.24, and 423.63, respectively.
PT Aneka Tambang Tbk (ANTM) adalah perusahaan pertambangan besar di Indonesia yang sahamnya aktif diperdagangkan di Bursa Efek Indonesia. Harga sahamnya dipengaruhi oleh faktor internal dan eksternal. Memprediksi pergerakan harga saham di masa depan bagi PT ANTM dapat digunakan metode peramalan deret waktu seperti ARIMA dan Prophet. ARIMA dikenal fleksibel dan mampu memberikan hasil prediksi yang akurat. Sementara itu, Prophet unggul dalam menangani data yang memiliki nilai hilang dan tren yang berubah, menjadikannya cocok untuk data pasar keuangan yang dinamis. Penelitian ini bertujuan membandingkan performa model ARIMA dan Prophet dalam memprediksi harga saham ANTM. Data yang digunakan adalah harga penutupan saham bulanan selama periode Januari 2016 hingga Mei 2025. Perbandingan dilakukan berdasarkan nilai Mean Absolute Percentage Error (MAPE), Akaike Information Criterion (AIC), dan Bayesian Information Criterion (BIC). Hasil analisis menunjukkan bahwa model ARIMA memiliki performa lebih baik dengan nilai MAPE 7.88, AIC 433.24, dan BIC 423.63, yang lebih rendah dibandingkan dengan model Prophet.
References
P. K. Dewi Lubis, H. H. Br Silalahi, A. Fitria Sinaga, P. Nidia Sapma, and V. Sitio, “Pasar Modal Dan Pengaruhnya Terhadap Perekonomian Di Indonesia,” JAKA (Jurnal Akuntansi, Keuangan, dan Audit., vol. 5, no. 1, pp. 196–214, 2024, doi: 10.56696/jaka.v5i1.10755.
A. Wiejaya and I. Fenriana, “Prediksi Harga Saham Top 10 NASDAQ dengan Time Series Prophet,” vol. 7, no. 2, 2024, doi: 10.32877/bt.v7i2.1736.
R. J. Hyndman and A. V Kostenko, “Minimum sample size requirements for seasonal forecasting models,” Foresight Int. J. Appl. Forecast., vol. 6, no. 6, pp. 12–15, 2007.
M. K. Douglas C. Montgomery, Cheryl L. Jennings, Introduction to Time Series Analysis and Forecasting. Inggris: Willey, 2008.
B. Hendrawan, “Penerapan Model ARIMA Dalam Memprediksi IHSG,” 205 | J. Integr. |, vol. 4, no. 2, pp. 205–211, 2012.
Zulhamidi and R. Hardianto, “Peramalan Penjualan Teh Hijau Dengan Metode ARIMA (Studi Kasus Pada PT. MK),” J. PASTI (Penelitian dan Apl. Sist. dan Tek. Ind., vol. 11, no. 3, pp. 231–244, 2017.
C. Chandra and S. Budi, “Analisis Komparatif ARIMA dan Prophet dengan Studi Kasus Dataset Pendaftaran Mahasiswa Baru,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, pp. 278–287, 2020, doi: 10.28932/jutisi.v6i2.2676.
A. Afnanda, M. Maiyastri, and D. Devianto, “Model Volatilitas Return Index Saham Syariah Indonesia Melalui Pendekatan Bayesian Markov Switching GARCH,” Lattice J. J. Math. Educ. Appl., vol. 4, no. 1, pp. 14–26, 2024, doi: 10.30983/lattice.v4i1.8381.
I. Yenidogan, A. Cayir, O. Kozan, T. Dag, and C. Arslan, “Bitcoin Forecasting Using ARIMA and PROPHET,” UBMK 2018 - 3rd Int. Conf. Comput. Sci. Eng., vol. C, pp. 621–624, 2018, doi: 10.1109/UBMK.2018.8566476.
D. Rizkya, H. Roosaputri, and C. Dewi, “Perbandingan Algoritma ARIMA, Prophet, dan LSTM dalam Prediksi Penjualan Tiket Wisata Taman Hiburan (Studi Kasus: Saloka Theme Park),” J. Penerapan Sist. Infomatika (Komputer Manajemen), vol. 4, no. 3, pp. 507–517, 2023.
G. A. R. J. Hyndman, “Forecasting: Principles and Practice,” Melbourne, Australia: OTexts, 2021.
A. Soejoeti, Analisis Runtun Waktu. Penerbit Karunika. Universitas Terbuka, 1987.
D. C. Gujarati, Damodar N; Porter, “Basic Econometrics,” Fifth Edit., New York: McGraw-Hill/Irwin, 2009.
E. R. Putri et al., “Penerapan Algoritma Prophet Facebook untuk Memprediksi Jumlah Calon Mahasiswa Baru,” vol. 5, no. 4, pp. 1588–1596, 2024.
S. A. Sinaga, “Implementasi Metode Arima (Autoregressive Moving Average) Untuk Prediksi Penjualan Mobil,” J. Glob. Technol. Comput., vol. 2, no. 3, pp. 102–109, 2023, doi: 10.47065/jogtc.v2i3.4013.
Widowati, S. P. Putro, S. Koshio, and V. Oktaferdian, “Implementation of ARIMA Model to Asses Seasonal Variability Macrobenthic Assemblages,” Aquat. Procedia, vol. 7, pp. 277–284, 2016, doi: 10.1016/j.aqpro.2016.07.039.
B. Jange, “Prediksi Harga Saham Bank BCA Menggunakan XGBoost,” Arbitr. J. Econ. Account., vol. 3, no. 2, pp. 231–237, 2022, doi: 10.47065/arbitrase.v3i2.495.
K. M. Riyantoko, Prismahardi Aji ;Fahrudin, Resna Maulana ; Hindrayani, “Water Availability Forecasting Using Univariate and Multivariate Prophet Time Series Model for ACEA (European Automobile Manufacturers Association),” Int. J. Data Sci. Eng. Anaylitics, vol. 1, no. 2, pp. 43–54, 2021, doi: 10.33005/ijdasea.v1i2.12.
E. Giovany Syuhada and M. Y. Helmi Setyawan, “Analisis Komparasi Metode Prophet Dan Metode Exponential Smoothing Dalam Peramalan Jumlah Pengangguran Di Jawa Barat: Systematic Literature Review,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 2, pp. 1369–1377, 2023, doi: 10.36040/jati.v7i2.6827.
A. O. Umrah, “Perbandingan Metode Varmax dan Prophet dalam Peramalan dan Analisis Harga Optimal Umroh di Indonesia,” pp. 937–946, 2024.
F. Oktavia and A. Witanti, “Implementasi Prophet Forecasting Model Dalam Prediksi Kualitas Udara Daerah Istimewa Yogyakarta,” Jl. Jemb. Merah No. 84 C Gejayan Yogyakarta, vol. 11, no. 1, pp. 64–74, 2024.
S. J. Taylor and B. Letham, “Forecasting at Scale,” Am. Stat., vol. 72, no. 1, pp. 37–45, 2018, doi: 10.1080/00031305.2017.1380080.
Solarwinds, “Holt-Winters Forecasting and Exponential Smoothing Simplified,” 2019.
C. Beaumont, S. Makridakis, S. C. Wheelwright, and V. E. McGee, “Forecasting: Methods and Applications,” J. Oper. Res. Soc., vol. 35, no. 1, p. 79, 1984, doi: 10.2307/2581936.
R. Apriliyanti, N. Satyahadewi, and W. Andani, “Application of Extreme Learning Machine Method on Stock Closing Price Forecasting Pt Aneka Tambang (Persero) Tbk,” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 2, pp. 1057–1068, 2023, doi: 10.30598/barekengvol17iss2pp1057-1068.
A. Widarjono, EKONOMETKIKA : Teori dan Aplikasi untuk ekonomi dan bisnis. 2007.
M. A. Gustiansyah et al., “Aplikasi Model ARIMA dalam Peramalan Data Harga Emas Dunia Tahun 2010 – 2022,” vol. 7, no. 1, pp. 84–92, 2023.
Downloads
Published
Issue
Section
Citation Check
License
Copyright (c) 2025 Rohimatul Anwar, Linda Rassiyanti
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with Lattice Journal : Journal of Mathematics Education and Applied agree to the following terms: Authors retain copyright and grant the Lattice Journal : Journal of Mathematics Education and Applied right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) the work for any purpose, even commercially with an acknowledgement of the work's authorship and initial publication in Lattice Journal : Journal of Mathematics Education and Applied. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in Lattice Journal : Journal of Mathematics Education and Applied. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
