PREDIKSI TEMPERATUR CUACA DI NEGARA NORWEGIA MENGGUNAKAN METODE LSTM

HIDAYATULLAH, SYARIF (2023) PREDIKSI TEMPERATUR CUACA DI NEGARA NORWEGIA MENGGUNAKAN METODE LSTM. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

This journal aims to implement the Long Short-Term Memory (LSTM) recurrence neural network method in predicting the average minimum temperature (tmin) value in Norway from 2015 to 2019. Historical weather data with the attribute tmin is collected and processed for training and testing. LSTM models. The LSTM model was developed with an optimized LSTM layer and trained using the Mean Absolute Error (MAE) evaluation metric. The experimental results show an accurate predictive ability compared to traditional methods. The use of LSTM in weather prediction in Norway is expected to make an effective contribution to understanding and anticipating minimum temperature changes. Keywords: LSTM, prediction, minimum temperature, weather, Norway Jurnal ini bertujuan untuk mengimplementasikan metode jaringan saraf rekurensi LSTM (Long Short-Term Memory) dalam memprediksi nilai rata-rata suhu minimum (tmin) di Nowergia pada rentang waktu 2015 hingga 2019. Data historis cuaca dengan atribut tmin dikumpulkan dan diproses untuk pelatihan dan pengujian model LSTM. Model LSTM dikembangkan dengan lapisan LSTM yang dioptimalkan dan dilatih menggunakan metrik evaluasi Mean Absolute Error (MAE). Hasil eksperimen menunjukkan kemampuan prediksi yang akurat dibandingkan metode tradisional. Penggunaan LSTM dalam prediksi cuaca di Nowergia diharapkan memberikan kontribusi efektif dalam memahami dan mengantisipasi perubahan suhu minimum. Kata kunci : LSTM, prediksi, suhu minimum, cuaca, Nowergia

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