Prediksi Kemunculan Titik Panas Di Lahan Gambut Provinsi Riau Menggunakan Long Short Term Memory

Ulfa Khaira (Universitas Jambi)
Muksin Alfalah (Universitas Jambi)
Pikir Claudia Septiani Gulo (Universitas Jambi)
Robi Purnomo (Universitas Jambi)

Jurnal Informatika: Jurnal Pengembangan IT
Vol 5, No 3 (2020): JPIT, September 2020

 Indonesia is blessed with the largest and most diverse tropical forests in the world. Millions of Indonesians depend on these forests for their lives. But lately forest fires have become an international concern as an environmental and economic issue. One of the causes of the decline in the number of forests is forest fires. Forest fires produce high particle emissions which can endanger human health. For this reason, necessary precautions. One prevention that can be done is to predict the emergence of hotspots, especially in areas where forest fires are frequent. One way to reduce forest fires is to predict the emergence of hot spots on peatlands with the Long Short Term Memory (LSTM) method. This study predicts the emergence of hotspots in Riau Province over the next 6 months, from August 2019 to January 2020. LSTM is able to predict time series with RMSE 363.38.