PREDIKSI PARAMETER CUACA MENGGUNAKAN DEEP LEARNING LONG-SHORT TERM MEMORY (LSTM)

Eko Supriyadi

Abstract


Saat ini metode deep learning dapat diaplikasikan untuk memprediksi suatu kejadian, seperti memprediksi cuaca suatu wilayah. Salah satu contoh deep learning yang cocok digunakan pada jenis data time series adalah LSTM. Penelitian ini menerapkan metode deep learning LSTM dengan jumlah layer 200, perbandingan data training dengan data test sebesar 9:1, serta mengukur nilai RMSE dan RMSE update hasil validasi dan prediksi beberapa hari ke depan. Data yang digunakan terdiri dari pengukuran suhu udara, kelembaban udara, kecepatan angin, dan tekanan udara selama bulan Januari dan Februari 2019. Data bulan Januari digunakan sebagai data training dan test untuk melakukan validasi prakiraan, sedangkan data bulan Februari digunakan sebagai pembanding dari hasil prediksi deep learning LSTM. Hasil penelitian menunjukkan RMSE seluruh validasi parameter cuaca nilainya semakin baik ketika menggunakan LSTM dengan update. Diperoleh RMSE update untuk parameter suhu, kelembaban, kecepatan angin, dan tekanan udara masing-masing bernilai 0,576; 2,8687; 2,1963; dan 1,0647. Sedangkan prediksi suhu udara, kelembaban, kecepatan angin, dan tekanan udara untuk 1 hari ke depan (1 Februari 2019) masing-masing sebesar 1,0337; 6,3413; 2,8934; dan 1,4313. Dari parameter tersebut hanya parameter suhu dan kelembaban udara yang mengalami pertambahan RMSE seiring bertambahnya waktu. Sedangkan parameter kecepatan angin dan tekanan udara mengalami penurunan di hari ketiga dan meningkat secara kontinu hingga satu bulan ke depan.


Keywords


prediksi, parameter cuaca, deep learning, LSTM

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DOI: http://dx.doi.org/10.31172/jmg.v21i2.619

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