Analisis Tingkat Keakuratan Prediksi Harga Saham dengan Metode Nonlinear Auto-Regressive Exogenous Model (NARX) Neural Network Berbasis Data Time Series: Studi Kasus pada Harga Saham PT. Bank Central Tbk. (BBCA)
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Penelitian ini menganalisis tingkat keakuratan prediksi harga saham PT. Bank Central Asia Tbk. (BBCA) menggunakan metode Nonlinear Auto-Regressive Exogenous Model (NARX) Neural Network berbasis data time series. Motivasi penelitian ini didasari oleh keterbatasan metode tradisional dalam memprediksi harga saham secara akurat, terutama dalam kondisi pasar yang volatil. Data penelitian menggunakan data sekunder harga saham BBCA periode Januari 2023 hingga Desember 2024 yang diperoleh dari Yahoo Finance, meliputi 470 data perdagangan harian dengan variabel open price, high price, low price, close price, dan volume perdagangan. Metodologi penelitian menerapkan preprocessing data menggunakan normalisasi min-max scaling, pembagian data menjadi 80% training, 10% validasi, dan 10% testing. Model NARX Neural Network dioptimalkan melalui metode trial-error untuk menentukan kombinasi parameter terbaik meliputi number of delay (1-5), algoritma pelatihan (trainlm, trainbr, trainscg), dan jumlah hidden neuron (1-10). Hasil penelitian menunjukkan bahwa konfigurasi optimal model menggunakan number of delay 4, algoritma Levenberg-Marquardt (trainlm), dan 8 hidden neuron dengan Mean Squared Error (MSE) sebesar 9.834,67. Evaluasi akurasi model menggunakan Mean Absolute Percentage Error (MAPE) menghasilkan nilai 1,018%, yang menurut kriteria Lewis (1982) tergolong highly accurate (MAPE < 10%). Temuan ini mengindikasikan bahwa metode NARX Neural Network efektif untuk prediksi harga saham BBCA dengan tingkat akurasi yang sangat tinggi.
Kata Kunci: NARX Neural Network, Prediksi Harga Saham, BBCA, Time Series Analysis, Mean Absolute Percentage Error.
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DOI: https://doi.org/10.37531/yum.v8i3.9446
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