Perbandingan Performa Arimax-Garch Dan Lstm Pada Data Harga Penutupan Saham PT Aneka Tambang Tbk (ANTM.JK)

Penulis

  • Dhiya Khalishah Tsany Suwarso Institut Pertanian Bogor, Bogor
  • Akbar Rizki Institut Pertanian Bogor, Bogor
  • Salsabila Dwi Rahmi Institut Pertanian Bogor, Bogor
  • Hakim Zoelva Mahesa Institut Pertanian Bogor, Bogor
  • Windi Gunawan Institut Pertanian Bogor, Bogor
  • Zafira Ilma Fitri Institut Pertanian Bogor, Bogor
  • Yenni Angraini Institut Pertanian Bogor, Bogor
  • Adelia Putri Institut Pertanian Bogor, Bogor
  • Muhammad Rizky Nurhambali Institut Pertanian Bogor, Bogor

DOI:

https://doi.org/10.25126/jtiik.2025128756

Kata Kunci:

ANTM.JK, ARIMAX, GARCH, LSTM, saham

Abstrak

Banyaknya data deret waktu dengan pola nonlinear dan memiliki volatilitas tinggi pada berbagai sektor membuat sulit untuk melakukan pemodelan klasik seperti Autoregressive Integrated Moving Average (ARIMA). Permasalahan ini dapat diatasi salah satunya dengan mengembangkan metode Autoregressive Integrated Moving Average with Exogenous- Generalized Autoregressive Conditional Heteroskedasticity (ARIMAX-GARCH) yang memanfaatkan kovariat eksternal, sehingga memberikan solusi lebih baik pada data yang tidak stasioner. Di sisi lain, metode deep learning seperti Long Short-Term Memory (LSTM) unggul dalam menangkap pola non-linear dan dependensi jangka panjang. Oleh karena itu, penelitian ini membandingkan performa ARIMAX-GARCH dan LSTM dalam memprediksi harga saham PT Aneka Tambang Tbk (ANTM.JK). Data mingguan penutupan harga saham ANTM.JK periode 1 Januari 2018 hingga 30 Oktober 2023 digunakan dalam penelitian ini. Pemodelan ARIMAX-GARCH dengan peubah kovariat berupa data harga nikel berjangka dunia digunakan karena terdapat pengaruh signifikan harga nikel terhadap harga penutupan saham ANTM.JK dan terdeteksi adanya heteroskedastisitas dalam model. Metode berbasis machine learning, LSTM digunakan karena metode ini dikenal memiliki akurasi prediksi yang baik. Pengolahan data dilakukan menggunakan bantuan software R-Studio dan Python. Hasil penelitian menunjukkan LSTM memiliki performa yang lebih baik dengan nilai MAPE sebesar 4,425%, nilai ini lebih kecil jika dibandingkan model terbaik ARIMAX(2,1,2)-GARCH(1,1) dengan MAPE 7,326%.

 

Abstract

The large number of time series data with nonlinear patterns and high volatility in various sectors makes it difficult to perform classical modeling such as Autoregressive Integrated Moving Average (ARIMA). This problem can be overcome by developing the ARIMA with Exogenous- Generalized Autoregressive Conditional Heteroskedasticity (ARIMAX-GARCH) that utilizes external covariates, thus providing a better solution to non-stationary data. On the other hand, deep learning methods such as Long Short-Term Memory (LSTM) excel in capturing non-linear patterns and long-term dependencies. Therefore, this study compares the performance of ARIMAX-GARCH and LSTM in predicting the stock price of PT Aneka Tambang Tbk (ANTM.JK). Weekly closing data of ANTM.JK stock price from January 1, 2018 to October 30, 2023 are used in this study. ARIMAX-GARCH modeling with covariate variables in the form of world nickel futures price data is used because there is a significant effect of nickel prices on the closing price of ANTM.JK shares and heteroscedasticity is detected in the model. Machine learning-based method, LSTM is used because this method is known to have good prediction accuracy. Data processing is done using R-Studio and Python software. The results show that LSTM has better performance with a MAPE value of 4.425%, this value is smaller than the best model ARIMAX(2,1,2)-GARCH(1,1) with a MAPE of 7.326%.

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Referensi

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Diterbitkan

30-06-2025

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Cara Mengutip

Perbandingan Performa Arimax-Garch Dan Lstm Pada Data Harga Penutupan Saham PT Aneka Tambang Tbk (ANTM.JK). (2025). Jurnal Teknologi Informasi Dan Ilmu Komputer, 12(3), 695-704. https://doi.org/10.25126/jtiik.2025128756