Peramalan Harga Emas Indonesia Menggunakan SETAR Dan SETAR-TREE

Ashilla, Aurell Faza (2023) Peramalan Harga Emas Indonesia Menggunakan SETAR Dan SETAR-TREE. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Emas digunakan sebagai standar keuangan di banyak negara dan juga sebagai alat tukar yang relatif abadi dan diterima di seluruh negara. Emas dipilih sebagai instrumen investasi karena sifatnya yang likuid atau mudah dikonversi menjadi uang tunai sebagai alat tukar pembayaran yang sah. Selain itu, kelebihan dari investasi emas adalah risiko yang rendah, sebagai hedging tool, dan harga yang tidak dipengaruhi oleh kebijakan suku bunga. Meskipun resiko investasi nya rendah, tidak semua investor emas dapat mencetak laba dari harga emas tersebut. Jika dilihat dari pergerakan harganya yang cenderung fluktuatif dan memiliki volatilitas tinggi, harga emas dapat mengandung komponen non-linier. Untuk dapat mengakomodasi pola nonlinier pada harga emas, diperlukan pemodelan nonlinier untuk meramalkan harga emas di masa depan. Metode yang dilakukan pada penelitian ini yaitu SETAR dan SETAR-Tree untuk meramalkan harga emas Indonesia. Berdasarkan hasil simulasi, model SETAR dan SETAR-Tree memiliki performa yang sama baik untuk jumlah data kecil maupun besar. Berdasarkan hasil analisis yang dilakukan, pada pemodelan pada in sample dan out sample, SETAR(2,1,1) dan SETAR-Tree memiliki performa yang hampir sama karena jika dibandingkan melalui nilai AIC, model SETAR-Tree memiliki nilai AIC yang lebih kecil. Sedangkan jika dibandingkan melalui nilai RMSE dan MAPE, model SETAR(2,1,1) memiliki nilai RMSE dan MAPE yang lebih kecil. Pada penelitian ini juga dilakukan simulasi pada data bangkitan yang mengikuti SETAR(2,2,2) dengan jumlah data yang berbeda yaitu 200 dan 2000 data. Model SETAR(2,1,1) meramalkan harga emas Indonesia akan menurun nilainya sedangkan dari hasil peramalan yang diperoleh model SETAR-Tree, harga emas Indonesia cenderung fluktuatif pergerakannya selama 14 hari dari 17 April hingga 4 Mei 2023.
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Gold is used as a financial standard in many countries and also as a relatively enduring and accepted medium of exchange across countries. Gold was chosen as an investment instrument because it is liquid or easily converted into cash as a legal medium of exchange. In addition, the advantages of investing in gold are low risk, as a hedging tool, and prices that are not affected by interest rate policies. Even though the investment risk is low, not all gold investors can make a profit from the gold price. When viewed from the price movements that tend to fluctuate and have high volatility, the price of gold can contain a non-linear component. In order to accommodate nonlinear patterns in gold prices, nonlinear modeling is needed to predict gold prices in the future. The method used in this research is SETAR and SETAR-Tree to predict the price of Indonesian gold. Based on the simulation results, the SETAR and SETAR-Tree model has the same performance both for data with a small amount of data and a large amount of data. Based on the results of the analysis carried out, in the in sample and out sample modeling, SETAR(2,1,1) and SETAR-Tree have almost the same performance because when compared through AIC values, the SETAR-Tree model has a smaller AIC value. Meanwhile, when compared through RMSE and MAsPE values, the SETAR(2,1,1) model has smaller RMSE and MAPE values. In this study, a simulation was also carried out on the generated data following SETAR(2,2,2) with a different amount of data, namely 200 and 2000 data. The SETAR model (2,1,1) predicts that Indonesia's gold price will decrease in value, while from the forecasting results obtained by the SETAR-Tree model, Indonesia's gold price tends to fluctuate for 14 days from 17 April to 4 May 2023.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	harga emas, nonlinier, peramalan, tree regression, SETAR, time series, gold price, nonlinear, forecasting, trees regression, SETAR, time series
Subjects:	H Social Sciences > HA Statistics > HA30.3 Time-series analysis T Technology > T Technology (General) > T57.8 Nonlinear programming. Support vector machine. Wavelets. Hidden Markov models.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49101-(S2) Master Thesis
Depositing User:	Aurell Faza Ashilla
Date Deposited:	29 Aug 2023 04:10
Last Modified:	29 Aug 2023 04:10
URI:	http://repository.its.ac.id/id/eprint/103432

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