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<title><![CDATA[METODE LASSO BilSTM PADA PREDIKSI INDONESIA RUPIAH TERHADAP DOLLAR AMERIKA]]></title>
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<note>Fluktuasi nilai tukar mata uang berdampak signifikan pada ekonomi global, termasuk perdagangan internasional dan investasi. Penelitian ini bertujuan memprediksi nilai tukar Rupiah Indonesia terhadap Dollar Amerika menggunakan metode ekstraksi fitur Least Absolute Shrinkage and Selection Operator (LASSO) dan prediksi Bidirectional Long Short-Term Memory (BiLSTM). Langkah awal penelitian adalah mengumpulkan data historis Rupiah dan Variabel Makroekonimi. Langkah awal penelitian ini adalah ekstraksi fitur menggunakan LASSO dengan penalti regularisasi L1 untuk mendapatkan 5 dataset yang berkorelasi denga varaibel respon. Efektivitas nilai lambda diukur menggunakan metriks R^2 pada K-fold cross-validation pencarian lamda terbaik. Model BiLSTM memiliki 3 lapis dan hyperparameter jumlah neuron, dropout rate, dan learning rate dioptimalkan mengunakan Random search untuk mencari kombinasi Hyperparameter terbaik. Pengujian dilakukan dengan mengukur hyperparameter timestep dan epoch. Hasil pengujian terbaik menjukan timesteps 3 dan epoch 100 menunjukkan metriks MAE = 0.041, RMSE = 0.0664, dan MAPE = 5.13%. Kata kunci: lasso, bidirectional lstm, prediksi, nilai tukar</note>
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