Pemodelan Time Series untuk Peramalan Web Traffic Menggunakan Algoritma Arima, LSTM, dan GRU

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Insyiraah Oxaichiko Arissinta
Indah Dwi Sulistiyawati
Dedy Kurnianto
Iqbal Kharisudin

Abstract

Saat ini peramalan lalu lintas web merupakan masalah besar karena hal ini dapat menyebabkan kemunduran pada cara kerja utama situs web. Peramalan deret waktu telah menjadi topik hangat untuk penelitian. Meramalkan Web Traffic dataset dari Kaggle dan mengevaluasi keakuratan melalui hasil peramalan akan menjadi salah satu cara yang efisien untuk menyampaikan informasi. Kami mengusulkan menggunakan metode tradisional dan metode baru yaitu Autoregressive Integrated Moving Average (ARIMA), Long-Short Term Memory (LSTM), dan Gated Recurrent Unit (GRU). Penelitian ini bertujuan untuk membandingkan pemodelan time series pada peramalan web traffic dengan algoritma Arima, LSTM, dan GRU.

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How to Cite
Arissinta, I., Sulistiyawati, I., Kurnianto, D., & Kharisudin, I. (2022). Pemodelan Time Series untuk Peramalan Web Traffic Menggunakan Algoritma Arima, LSTM, dan GRU. PRISMA, Prosiding Seminar Nasional Matematika, 5, 693-700. Retrieved from https://journal.unnes.ac.id/sju/prisma/article/view/54712
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