Prediksi indeks harga saham di Indonesia dengan metode ARFIMA = Forecasting stock price index in Indonesia with ARFIMA method

Setiawan, Natassya (2020) Prediksi indeks harga saham di Indonesia dengan metode ARFIMA = Forecasting stock price index in Indonesia with ARFIMA method. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Pada data yang memiliki ketergantungan jangka panjang, model ARFIMA (Autoregressive Fractionally Integrated Moving Average) adalah salah satu model deret waktu yang dapat digunakan untuk memprediksi data. Tujuan dari penelitian ini adalah memodelkan data saham di Indonesia dengan menggunakan metode ARFIMA dan memilih model ARFIMA(p,d,q) terbaik yang akan digunakan untuk memprediksi data. Pada penelitian ini, dilakukan pengujian jangka panjang pada data dengan metode Hurst Exponent. Model terbaik dipilih dengan cara membandingkan dan memilih nilai MSE (Mean Square Error) dan MAPE (Mean Absolute Percentage Error) terkecil pada setiap mode terbaik dalam kandidat-kandidat model ARFIMA terpilih. Model terbaik digunakan untuk memprediksi harga saham di Indonesia. Pada penelitian ini, data yang digunakan adalah data IHSG (Indeks Harga Saham Gabungan) yang diambil secara mingguan dari 5 Januari 2009 hingga 30 September 2019. Pada penelitian ini diperoleh model ARFIMA terbaik, yaitu ARFIMA(1, 0,0862971, 2) dengan nilai MSE sebesar 0,000162844 dan nilai MAPE sebesar 1,08732609. Hasil prediksi untuk data lima minggu menunjukkan close price IHSG mengalami kenaikkan. Prediksi close price IHSG tertinggi adalah sebesar 6.263,67 dan yang paling rendah adalah sebesar 6.210,655. / On long memory data, ARFIMA (Autoregressive Fractionally Integrated Moving Average) model is one of the time series models that can be used to forecast data. The purpose of this research is to model the stock data in Indonesia using the ARFIMA method and choose the best ARFIMA (p,d,q) model that will be used to forecast the data. In this study, Hurst Exponent method is used to test the long memory of data. The best model is chosen by comparing and choosing the smallest MSE value (Mean Square Error) and the smallest MAPE (Mean Absolute Percentage Error) in each best mode in the selected ARFIMA model candidates. In this study, the data used is IDX Composite which is taken on a weekly basis from January 5, 2009 to September 30, 2019. In this study, the best ARFIMA model has been obtained, it is ARFIMA (1, 0.0862971, 2) with the MSE value of 0.000162844 and MAPE value of 1.08732609. The forecasting results for the next five weeks show that the close price of IDX Composite increases over a period of time. The highest close price forecasting for the Jakarta Composite Index is 6,263.67 and the lowest rate is 6,210,655.

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