A Komparasi Model Gerak Brown Geometrik Termodifikasi dan Model Kecerdasan Buatan untuk Prediksi Harga Saham Sektor Kesehatan di Indonesia
DOI:
https://doi.org/10.23887/jstundiksha.v12i2.48960Keywords:
stock prices, geometric brownian motion, artificial intelligenceAbstract
Selama pandemi COVID-19 di Indonesia, pergerakan saham di berbagai sektor mengalami dampak yang signifikan. Akan tetapi, harga saham pada sektor kesehatan mengalami pertumbuhan sebesar 3,71%. Penelitian ini bertujuan menerapkan model Gerak Brown Geometrik yang Termodifikasi (MBM) dan model kecerdasan buatan untuk memprediksi pergerakan penutupan harga pada data harga penutupan saham PT. Kalbe Farma Tbk. Lalu, hasil prediksi dibandingkan untuk mengetahui model terbaik. Pada MBM, dilakukan konstruksi tren dan volatilitas yang memuat variabel waktu. Hasil ini kemudian dibandingkan dengan model kecerdasan buatan, yaitu Light Gradient Boosting Machine (LightGBM) dan Multi Layer Perceptron (MLP). Selain itu, pada konstruksi model kecerdasan buatan, dilakukan pendekatan baru yang mana input dari model tersebut adalah tanggal dan titik urutan deret waktu. Hasil simulasi menunjukkan bahwa MBM mampu memberikan nilai MAPE yang lebih kecil dibandingkan GBM. Akan tetapi, model kecerdasan buatan, khususnya LightGBM, masih lebih unggul bila digunakan untuk memodelkan harga saham sektor kesehatan. Model ini memberikan nilai MAPE dibawah 1%.
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