Implementasi Metode Interpolasi Dan Autoregressive Integrated Moving Average (Arima) Pada Peramalan Indeks Harga Saham Gabungan.

Aliffrianto, M. Prima Teguh (2022) Implementasi Metode Interpolasi Dan Autoregressive Integrated Moving Average (Arima) Pada Peramalan Indeks Harga Saham Gabungan. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Indeks Harga Saham Gabungan (IHSG) merupakan indikator utama yang menggambarkan pergerakan harga saham, dengan memilki fungsi sebagai indikator trend pasar, indikator tingkat keuntungan, tolok ukur kinerja portofolio serta penentuan strategi pasif dan produk derivatif.Penentuan IHSG dapat diprediksikan dengan cara meramalkan IHSG berdasarkan data historis.Jika dilihat dari pola data historis dari realisasi nilai IHSG, maka metode yang cocok dipakai adalah metode peramalan ARIMA dan hasil analisa perbandingan dengan metode interpolasi.Dimana menggunakan data masa lampau pada IHSG untuk menentukan model ARIMA dan interpolasi yang sesuai.Selanjutnya, dihitung tingkat kesalahan pada model interpolasi dan ARIMA dengan menggunakan RMSE (Root Mean Square Error) dan MAPE (Mean Absolute Percentage Error).Dari hasil simulasi, diperoleh bahwa interpolasi linear merupakan jenis model interpolasi yang memiliki tingkat kesalahan terkecil sedangkan model ARIMA(0,1,29) merupakan jenis model ARIMA yang memiliki tingkat kesalahan terkecil.Lalu, dilakukan analisa perbandingan pada model interpolasi linear dan model ARIMA(0,1,29) berdasarkan pada nilai MAPE.Diperoleh hasil bahwa interpolasi linear dan ARIMA(0,1,29) merupakan model dengan akurasi peramalan sangat baik dimana interpolasi linear untuk meramalkan IHSG memiliki nilai MAPE lebih kecil daripada ARIMA(0,1,29).
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The Composite Stock Price Index (JCI) is the main indicator that describes stock price movements, with functions as market trend indicators, profit level indicators, portfolio performance benchmarks as well as determining passive strategies and derivative products.The determination of the JCI can be predicted by forecasting the JCI based on historical data.If viewed from the historical data pattern of the realization of the JCI value, the suitable method used is the ARIMA forecasting method and the results of the comparison analysis using the interpolation method.Where to use past data on the JCI to determine the ARIMA model and the appropriate interpolation.Furthermore, the error rate in the interpolation model and ARIMA is calculated using RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error).From the simulation results, it is found that linear interpolation is the type of interpolation model that has the smallest error rate while the ARIMA model (0,1,29) is the type of ARIMA model that has the smallest error rate.Then, a comparative analysis was performed on the linear interpolation model and the ARIMA(0,1,29) model based on the MAPE value.The result is that linear interpolation and ARIMA(0,1,29) are models with very good forecasting accuracy where linear interpolation to predict JCI has a smaller MAPE value than ARIMA(0,1,29).

Item Type:	Thesis (Other)
Additional Information:	RSMa 516.2 Ali i-1 2022
Uncontrolled Keywords:	IHSG, Interpolasi, ARIMA. IHSG, Interpolation, ARIMA.
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Mathematics, Computation, and Data Science > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	09 Jun 2026 01:03
Last Modified:	09 Jun 2026 01:03
URI:	http://repository.its.ac.id/id/eprint/133645

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