Optimasi Model Support Vector Machine (SVM) Menggunakan GridSearchCV untuk Prediksi Harga Penutupan Saham PT Aneka Tambang Tbk (ANTAM)

Andriansyah  Latif; Dina Kusuma Astuti; Rini Arianty

doi:10.51454/decode.v5i3.1416

Authors

Andriansyah Latif Manajemen Universitas Gunadarma
Dina Kusuma Astuti Psikologi Universitas Gunadarma
Rini Arianty Sistem Informasi Universitas Gunadarma

DOI:

https://doi.org/10.51454/decode.v5i3.1416

Keywords:

Antam, Harga Penutupan, RMSE, Saham, SVM

Abstract

Pasar modal merupakan salah satu instrumen penting dalam perekonomian, di mana fluktuasi harga saham dapat memengaruhi keputusan investasi. PT Aneka Tambang Tbk (ANTAM) merupakan salah satu emiten di Bursa Efek Indonesia dengan tingkat volatilitas harga yang cukup tinggi sehingga diperlukan model prediksi yang akurat. Penelitian ini bertujuan untuk membangun model prediksi harga penutupan saham ANTAM tahun 2024 menggunakan algoritma Support Vector Machine (SVM). Data historis saham diolah melalui tahap pra-pemrosesan, meliputi penanganan missing value dan normalisasi, kemudian dilakukan pelatihan model dengan tiga rasio pembagian data. Evaluasi model dilakukan menggunakan metrik RMSE, MSE, dan R² Score. Hasil penelitian menunjukkan bahwa model SVM mampu memberikan prediksi dengan tingkat akurasi yang sangat tinggi, ditunjukkan oleh nilai RMSE yang rendah (0.024–0.029) dan R² Score mendekati 1 (0.9938–0.9953). Performa terbaik diperoleh pada rasio pembagian data 7:3 dengan MSE terkecil 0.0006 dan R² Score tertinggi 0.9953. Dengan demikian, penelitian ini membuktikan bahwa algoritma SVM dapat menjadi metode yang andal dalam memprediksi harga penutupan saham ANTAM serta berpotensi memberikan kontribusi praktis bagi investor dalam meminimalisasi risiko dan meningkatkan keuntungan investasi.

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