PREDIKSI PERGERAKAN HARGA SAHAM MENGGUNAKAN QUANTUM MACHINE LEARNING BERBASIS VARIATIONAL QUANTUM CIRCUITS

Safira Hasna Setiyani

doi:10.23960/jitet.v13i3S1.8038

Authors

Safira Hasna Setiyani Universitas Karya Husada Semarang

DOI:

https://doi.org/10.23960/jitet.v13i3S1.8038

Abstract Views: 104 File Views: 93

Keywords:

quantum machine learning, variational quantum circuit, prediksi saham, bursa efek indonesia

Abstract

Pergerakan harga saham bersifat kompleks, non-linear dan dipengaruhi oleh berbagai faktor ekonomi sehingga prediksinya menjadi tantangan bagi metode tradisional. Penelitian ini bertujuan untuk memanfaatkan Quantum Machine Learning (QML) berbasis Variational Quantum Circuits (VQC) dalam memprediksi arah pergerakan harga saham di Bursa Efek Indonesia. Dataset yang digunakan merupakan data harga harian saham (open, high, low, close, volume) selama periode 2020 - 2025 yang diperoleh dari Yahoo Finance dan IDX. Metode penelitian meliputi preprocessing data, transformasi time series menggunakan sliding window, serta pelatihan model QML untuk memprediksi tren naik atau turun saham. Hasil eksperimen menunjukkan bahwa model QML mampu mencapai akurasi prediksi sebesar 99,70%. Evaluasi dilakukan menggunakan metrik akurasi, mean squared error (MSE) dan confusion matrix, menunjukkan kemampuan VQC menangkap pola non-linear yang kompleks. Penelitian ini menegaskan potensi QML sebagai teknologi inovatif untuk analisis pasar saham dan membuka peluang pengembangan sistem prediksi saham berbasis komputasi kuantum di masa depan.

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