Sistem Prediksi Harga Saham LQ45 Dengan Random Forest Classifier
Keywords:
saham, LQ45, technical analysis, random forestAbstract
Data yang digunakan adalah ringkasan saham harian perusahaan yang terdaftar pada indeks LQ45 versi Agustus 2018 – Januari 2019 mulai tanggal 1 Juli 2015 hingga 31 Desember 2018. Technical indicator yang digunakan dalam penelitian ini adalah On-Balance Volume, Chaikin Oscillator, Moving Average Convergence/Divergence, dan Bollinger Bands. Data tersebut kemudian dibentuk modelnya untuk setiap kode saham, rentang waktu, dan tipe fitur. Rentang waktu prediksi yang digunakan adalah 1 hari, 5 hari, dan 20 hari. Tipe fitur yang digunakan untuk membentuk model adalah plain yang menggunakan seluruh nilai ringkasan saham harian dan technical indicator-nya, grouped yang fiturnya merupakan kondisi technical indicator terhadap hari sebelumnya (naik, tetap, dan turun) dan nilainya terhadap garis nol (positif, nol, negatif), serta onehot yang fiturnya merupakan hasil one-hot encoding terhadap fitur grouped. Model yang dibentuk kemudian digunakan untuk memprediksi perubahan harga saham dengan kemungkinan nilai naik, tetap, atau turun. Nilai akurasi dihitung menggunakan confusion matrix. Hasil pengujian terhadap data latih menunjukkan nilai yang sangat baik, dimana tipe fitur plain dengan rentang waktu 5 dan 20 hari mencapai 100%. Hasil pengujian terhadap data uji menunjukkan penurunan dibanding data latih, namun tipe fitur plain tetap menunjukkan performa paling baik dimana terdapat tiga kode saham dengan akurasi lebih besar dari 60% untuk rentang waktu satu hari, enam kode saham untuk rentang waktu lima hari, dan empat belas kode saham untuk rentang waktu dua puluh hari, sembilan di antaranya di atas 70%, dengan kode ANTM mencapai akurasi sebesar 80,6%.
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