Prediksi Kualitas Udara Dengan Metoda LSTM, Bidirectional LSTM, dan GRU
Abstract
Kualitas udara menjadi salah satu masalah utama di kota besar. Salah satu cara pengendalian kualitas udara adalah dengan cara memprediksi beberapa parameter utama dengan menggunakan algoritma deep learning. Penelitian ini menggunakan metoda deep learning yang merupakan bagian dari Recurrent Neural network yaitu Long Short Term Memory, Bidirectional Long Short Term Memory, dan Gated Recurrent Unit yang diterapkan pada permasalahan memprediksi data time series kualitas udara dengan parameter suhu, kelembaban, particular matter PM10, dan Indeks Standar Pencemar Udara (ISPU). Dari hasil pengujian 3 jenis model prediksi terhadap 4 variabel berdasarkan kreteria penilain menggunakan RMSE dari data testing dan dibandingkan dengan standard deviasi, maka model LSTM dan LSTM Bidirectional telah menunjukan hasil yang bagus untuk permasalahan data yang bersifat time series kualitas udara, Sedangkan model Gated Recurrent Unit (GRU) menampilkan hasil yang kurang bagus.
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