Musytari : Jurnal Manajemen, Akuntansi, dan Ekonomi

Vol. 25 No. 8 (2026): Musytari: Neraca Manajemen, Akuntansi, dan Ekonomi, ISSN 3025-9495
Articles

ANALISIS PERGERAKAN INDEX IDX-30 MENGGUNAKAN METODE AUTO REGRESIVE INTEGRATED MOVING AVERAGE (ARIMA) SAAT DAN SETELAH PANDEMI COVID-19

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  • Achmad Ilham Fauzan,
  • Agus Herta Sumarto
Achmad Ilham Fauzan
Universitas Mercu Buana
Agus Herta Sumarto
Universitas Mercu Buana
DOI: https://doi.org/10.5281/zenodo.21323118

Published 2026-07-12

Keywords

  • ARIMA,
  • IDX30,
  • COVID-19,
  • Stock Forecasting,
  • Time Series,
  • Peramalan Saham
    • ...More
    Less

How to Cite

ANALISIS PERGERAKAN INDEX IDX-30 MENGGUNAKAN METODE AUTO REGRESIVE INTEGRATED MOVING AVERAGE (ARIMA) SAAT DAN SETELAH PANDEMI COVID-19. (2026). Musytari : Jurnal Manajemen, Akuntansi, Dan Ekonomi, 25(8). https://doi.org/10.5281/zenodo.21323118

Abstract

This study aims to analyze the movement of the IDX30 index during two different periods, namely during the COVID-19 pandemic (2020–2022) and after the pandemic (2023–2025), using the Autoregressive Integrated Moving Average (ARIMA) method. IDX30 was selected as the research object because it represents blue-chip stocks in the Indonesia Stock Exchange with high liquidity and large market capitalization. ARIMA was chosen due to its effectiveness in modeling non-stationary time series data and its proven performance in various financial studies. The data used consists of daily closing prices obtained from Investing.com. This study is expected to provide a comprehensive understanding of the reliability of the ARIMA model in forecasting stock price movements under different economic conditions, as well as offer valuable insights for investors and policymakers in developing responsive investment strategies. Penelitian ini bertujuan untuk menganalisis pergerakan indeks IDX30 selama dua periode berbeda, yaitu saat pandemi COVID-19 (2020–2022) dan setelah pandemi (2023–2025), menggunakan metode Autoregressive Integrated Moving Average (ARIMA). IDX30 dipilih karena mencerminkan saham-saham unggulan di Bursa Efek Indonesia yang memiliki likuiditas tinggi dan kapitalisasi pasar besar. Metode ARIMA dipilih karena kemampuannya dalam menangani data deret waktu non-stasioner dan telah terbukti efektif dalam berbagai studi keuangan. Data yang digunakan merupakan data harga penutupan harian yang diperoleh dari situs Investing.com. Analisis dilakukan dengan membagi data menjadi dua periode, mengestimasi model ARIMA untuk masing-masing periode.Hasil penelitian ini diharapkan dapat memberikan pemahaman mendalam tentang keandalan model ARIMA dalam memprediksi pergerakan harga saham dalam kondisi ekonomi yang berbeda, serta memberikan referensi bagi investor dan pengambil kebijakan dalam menyusun strategi investasi yang responsif terhadap dinamika pasar.
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