Prediksi Pola Keuangan pada Pasar Saham Bursa Efek Indonesia Menggunakan Algoritma Support Vector Machine For Regression (SVR)
DOI:
https://doi.org/10.26418/jp.v10i3.86833Keywords:
Algorithm, Stock Market, Vector Machine RegressionAbstract
Di era modern perdagangan saham yang dinamis, penggunaan algoritma Support Vector Machine Regression menjadi perhatian utama bagi para investor dan trader. Penelitian ini bertujuan untuk menganalisis cara kerja algoritma dalam memprediksi pola pasar saham menggunakan Support Vector Machine Regression. Metode penelitian yang digunakan adalah Support Vector Regression dengan kernel Radial Basis Function. Hasil penelitian menunjukkan tingkat akurasi yang baik, dengan Mean Absolute Percentage Error (MAPE) sebesar 6,04%. Hal ini menunjukkan bahwa model ini efektif dalam memprediksi pergerakan harga saham di Bursa Efek Indonesia. Sebaliknya, penggunaan kernel Linear dan Polynomial tidak memberikan hasil yang memuaskan, dengan tingkat kesalahan yang cukup signifikan, masing-masing sebesar 16,32% dan 22,47%. Bahkan, kernel Sigmoid menunjukkan tingkat kesalahan yang sangat tinggi, yaitu MAPE sebesar 808,46%, yang mengindikasikan bahwa model ini tidak cocok untuk prediksi harga saham. Penelitian ini berkontribusi dengan menunjukkan bahwa penggunaan Support Vector Regression dengan kernel Radial Basis Function dapat memberikan hasil prediksi yang akurat dalam konteks pergerakan harga saham. Kontribusi utama terletak pada pemahaman lebih lanjut mengenai efektivitas model Support Vector Regression dalam prediksi di pasar saham Indonesia, yang memberikan manfaat signifikan bagi investor, perusahaan keuangan, pemerintah, dan masyarakat.
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