Prediksi Indeks Harga Saham Menggunakan Model Dinamik Hukum Pendingin Newton

Masriyah, Siti (2021) Prediksi Indeks Harga Saham Menggunakan Model Dinamik Hukum Pendingin Newton. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Investasi merupakan upaya penanaman modal untuk mendapatkan return yang sebesar-besarnya di masa depan. Investasi saham merupakan salah satu bentuk investasi yang dapat memberikan return yang tinggi. Namun, semakin tinggi return yang didapatkan maka, semakin besar pula kemungkinan risiko yang dihadapi. Salah satu cara untuk meminimalisir risiko kerugian dalam investasi saham adalah dengan memprediksi indeks harga saham dengan menganalisis data indeks harga saham sebelumnya. Fenomena pengembalian rata-rata (mean reversion) yang terjadi pada indeks harga saham menyerupai proses perpindahan panas yang dicirikan pada hukum pendingin Newton. Pada tugas akhir ini dibahas mengenai prediksi Indeks Harga Saham Gabungan (IHSG) dengan menggunakan model dinamis yang didapat dari modifikasi hukum pendingin Newton. Terdapat tiga model dinamis hasil modifikasi hukum pendingin Newton yang akan digunakan untuk memprediksi indeks harga saham, yaitu Price Reversion Model, Price Reversion-Quasi Logistic Model dan Velocity Reversion Model. Ketiga model tersebut diterapkan pada Indeks Harga Saham Gabungan (IHSG) untuk melihat kemampuan prediksi dari masing-masing model. Berdasarkan hasil validasi model didapatkan model terbaik untuk memprediksi IHSG adalah Price Reversion Model dengan MAPE sebesar 8.4159%. Kemudian, Price Reversion Model digunakan untuk memprediksi IHSG untuk bulan April 2021 sampai Juli 2021, didapat bahwa IHSG akan mengalami tren turun dalam selang waktu tersebut.
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Investment is a capital implantation to get the maximum return in the future. Stock investment is one form of investment that can provide high returns. However, the higher return obtained, the greater the possibility of risk faced. One way to minimize losses in stocks is to predict the stock price index by analyzing previous stock price index data. The phenomenon of the mean reversion that occurs in the stock price index is similar to the heat characterized by Newton's law of cooling. This final project discusses the prediction of the Jakarta Composite Index (JCI) using a dynamic model obtained from a modification of Newton's law of cooling. There are three dynamic models modified by Newton's law of cooling that will be used to predict stock price index, namely Price Reversion Model, Price Reversion-Quasi Logistic Model and Velocity Reversion Model. The three models are applied to the Jakarta Composite Index (JCI) to see the predictive ability of each model. The error of the model is measured using the Mean Absolute Percentage Error (MAPE). Based on the results of model validation, the Price Reversion Model has the smallest MAPE, which is 8.4159%. Price Reversion Model can also show the movement trend of the Jakarta Composite Index (JCI) well. Then, the Price Reversion Model is used to predict the Jakarta Composite Index (JCI) for April 2021 to July 2021, it is found that the Jakarta Composite Index (JCI) will experience a down trend in that time interval.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Prediksi, IHSG, Hukum Pedningin Newton, Model Dinamis, MAPE, Forecast, JCI, Newton`s Law of Cooling, Dynamic Models, MAPE
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Siti Masriyah
Date Deposited:	26 Aug 2021 07:29
Last Modified:	26 Aug 2021 07:29
URI:	http://repository.its.ac.id/id/eprint/90533

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