Peramalan Data Univariat Menggunakan Metode Long Short Term Memory
Abstract
Peramalan data univariat mengacu pada kegiatan meramalkan nilai pada data dengan satu variabel independen yang mungkin muncul di masa depan berdasarkan nilai-nilai yang ada di masa lalu. Penelitian ini bertujuan untuk memperoleh model yang dibangun menggunakan pendekatan deep learning jenis supervised learning yaitu metode Long Short Term Memory (LSTM) yang diterapkan pada data univariat. Metode LSTM merupakan pengembangan dari metode Recurrent Neural Network (RNN) dengan menambahkan 3 gate yang mampu memilih informasi yang dibutuhkan untuk pelatihan sel sehingga mampu mengurangi kemungkinan exploding gradients dan vanishing gradients. Model dibangun dengan input layer LSTM dengan unit sel dan output dense layer dengan tambahan hyperparameter tuning yang diset menggunakan optimizer, fungsi aktivasi  dan , dan nilai epoch. Performa model peramalan diuji menggunakan mean absolute percentage error (MAPE).
Copyright (c) 2023 Helma Syifa Izzadiana, Herlina Napitupulu, Firdaniza Firdaniza
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