HYBRID SVR-GS UNTUK PREDIKSI SAHAM PT ANEKA TAMBANG TBK
DOI:
https://doi.org/10.35316/.v11i1.35-45Kata Kunci:
Machine Learning, Support Vector Regression, Grid Search, Prediksi, SahamAbstrak
Sektor pertambangan di Indonesia memiliki peran strategis dalam perekonomian nasional, namun pergerakan harga sahamnya bersifat fluktuatif akibat pengaruh faktor eksternal, seperti harga komoditas dan kondisi pasar global. Kondisi tersebut menjadikan prediksi harga saham sebagai permasalahan yang kompleks. Penelitian ini bertujuan untuk memprediksi harga saham PT Aneka Tambang Tbk (ANTM) menggunakan metode Support Vector Regression (SVR) dengan optimasi hyperparameter melalui Grid Search (GS), sehingga membentuk model hybrid SVR-GS. Data yang digunakan berupa data historis saham ANTM periode 2020–2025 sebanyak 1.202 data yang diperoleh dari Investing.com. Tahapan penelitian meliputi preprocessing data, feature engineering dengan pendekatan lag time, normalisasi Min-Max, pembagian data berbasis time series, serta evaluasi model. Kinerja model diukur menggunakan Mean Absolute Error (MAE), Root Mean Square Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa SVR Default menghasilkan MAE 173,78 dan MAPE 9,65%, sedangkan SVR Semi-Tuned menurunkan kesalahan menjadi MAE 71,72 dan MAPE 3,19%. Model SVR-GS memberikan performa terbaik dengan MAE 45,16, RMSE 67,94, dan MAPE 2,24% pada rasio data 70:30. Dengan demikian, optimasi Grid Search terbukti meningkatkan akurasi prediksi harga saham ANTM secara signifikan.
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Referensi
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