Peramalan Harga Saham PT Bank Central Asia TBK Menggunakan Metode Hybrid SOFM-SVR

Raditya, Muhammad Rangga Hata (2025) Peramalan Harga Saham PT Bank Central Asia TBK Menggunakan Metode Hybrid SOFM-SVR. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Saham merupakan salah satu produk investasi yang sangat fluktuatif yang dimana naik dan turunnya harga saham terjadi secara tidak menentu. Fluktuasi harga saham menjadi indikator bagi pelaku investasi untuk melakukan tindakan jual beli saham, dimana penjualan terjadi saat harga saham mengalami kenaikan dan pembelian terjadi pada saat harga saham mengalami penurunan. Untuk memudahkan pelaku investasi melakukan aktivitas jual beli saham, diperlukan peramalan atau prediksi dalam menentukan harga saham tersebut. Penelitian ini bertujuan meramalkan pergerakan harga saham yang terjadi di pasar saham dengan menggunakan metode hybrid antara Self Organizing Feature Map (SOFM) dan Support Vector Regression (SVR). Data yang digunakan dalam penelitian ini adalah data harga open, high, low, dan close dari ringkasan saham PT Bank Central Asia Tbk (BBCA) dengan periode 2 Januari 2020 hingga 28 Juni 2024. Hasil peramalan menggunakan metode hybrid SOFM-SVR dengan data training mendapatkan nilai MAPE sebesar 0.1590%, sedangkan untuk data testing sebesar 0.1829%
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Stocks are one of the most volatile investment products, where the prices fluctuate unpredictably. Price fluctuations serve as an indicator for investors to make buy and sell decisions, with selling occurring when the stock price increases and buying happening when the stock price decreases. To assist investors in making these buy and sell decisions, forecasting or prediction is required to determine the future price of the stock. This study aims to forecast stock price movements in the stock market using a hybrid method combining Self-Organizing Feature Map (SOFM) and Support Vector Regression (SVR). The data used in this study consists of the open, high, low, and close prices of PT Bank Central Asia Tbk (BBCA) stocks from January 2, 2020, to June 28, 2024. The forecasting results using the hybrid SOFM-SVR method with training data achieved a Mean Absolute Percentage Error (MAPE) of 0.1590%, while the testing data resulted in a MAPE of 0.1829%

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Peramalan saham, Self-Organizing Feature Map, Support Vector Regression, Metode hybrid, Saham BBCA, Stock forecasting, Self Organizing Feature Map, Support Vector Regression, Hybrid method, BBCA Stock
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Muhammad Rangga Hata Raditya
Date Deposited:	05 Aug 2025 03:58
Last Modified:	11 Aug 2025 03:03
URI:	http://repository.its.ac.id/id/eprint/125224

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