Peramalan Konsentrasi PM2.5 Menggunakan Model ARCH/GARCH dan Long Short-Term Memory (Studi Kasus: Kota Jakarta Pusat)
DOI:
https://doi.org/10.23969/infomatek.v26i1.12603Keywords:
ARCH/GARCH, Jakarta Pusat, LSTM, PM2.5Abstract
Polusi udara merupakan masalah serius di Jakarta akibat revolusi industri dan aktivitas komuter yang tak pernah berhenti. Particulate matter (PM) 2.5 merupakan salah satu dari 6 polutan berupa partikel tersuspensi berukuran diameter aerodinamis lebih kecil dari 2,5 µm. PM2.5 menyebabkan terganggunya sistem kardiovaskular dan respiratory, mengakibatkan kejadian kelahiran prematur, dan kejadian berat badan lahir rendah. Oleh karena itu, dibutuhkan kebijakan dan aturan dari pemerintah terkait pengendalian polusi udara agar masyarakat memperoleh kesehatan yang lebih baik, mengurangi kemacetan, mitigasi perubahan iklim, dan efisiensi energi. Peramalan konsentrasi PM2.5 menjadi salah satu hal penting yang dilakukan untuk pengambilan kebijakan demi tercapainya visi jangka panjang tahun 2030, yaitu kota yang aman, nyaman, produktif, berkelanjutan, sebanding dengan kota besar lainnya dan rumah bagi warga (Menuju Udara Bersih Jakarta). Penelitian ini bertujuan untuk mendapatkan model terbaik untuk peramalan konsentrasi PM2.5 di Wilayah Jakarta Pusat. Metode yang digunakan, yaitu Generalized Autoregressive Conditional Heteroskedasticity (GARCH) dengan Long Short-Term Memory (LSTM) dan menguji akurasi model dengan data sebenarnya. Hasil penelitian memberikan kesimpulan bahwa metode LSTM lebih baik dalam meramalkan konsentrasi PM2.5 berdasarkan MAE, MAPE, MSE, dan RMSE. Hasil peramalan dengan metode LSTM menunjukkan bahwa konsentrasi PM2.5 di Wilayah Jakarta Pusat selama 48 jam kedepan berada di rentang tidak sehat bagi kelompok sensitif dan di beberapa jam berikutnya masuk ke kategori tidak sehat. Maka dari itu, dibutuhkan perhatian lebih dari pemerintah untuk melakukan pengendalian polusi udara demi kualitas udara dan kesehatan masyarakat kota.Downloads
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