Analisis Data Time Series Menggunakan LSTM (Long Short Term Memory) Dan ARIMA (Autocorrelation Integrated Moving Average) Dalam Bahasa Python.

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Title Analisis Data Time Series Menggunakan LSTM (Long Short Term Memory) Dan ARIMA (Autocorrelation Integrated Moving Average) Dalam Bahasa Python.
Analisis Data Time Series Menggunakan LSTM (Long Short Term Memory) Dan ARIMA (Autocorrelation Integrated Moving Average) Dalam Bahasa Python.
 
Creator Bayangkari Karno, Adhitio Satyo
 
Description Abstract — This study aims to predict time series data by using two methods, the first method commonly used is statistics Autocorrelation Integrated Moving Average (ARIMA model) and the second method which is relatively new, namely machine learning Long Short Term Memory (LSTM). Before the data is processed by both methods, data cleaning and data optimization are carried out. Data optimization is a transformation process to eliminate elements of trends and variations from data. The transformation consists of 7 results of a combination from Log processes, Moving Average (MA), Exponential Weigh Moving Average (EWMA), and Differencing (Diff). The seven processes are each used in the ARIMA and LSTM processes. So that 14 predictions will be obtained (7 from the ARIMA process and 7 from the LSTM process). From the 14 prediction results obtained the smallest RMSE value for ARIMA is 2% and the smallest RMSE value for LSTM is 1%. The results of this study using 7 combinations of transformation processes, can increase the level of accuracy of predictions from ARIMA and LSTM. Where the accuracy of LSTM learning machines by using Telkom's stock data has higher accuracy than ARIMA.
Abstrak - Penelitian ini bertujuan untuk memprediksi data deret waktu dengan menggunakan dua metode, metode pertama yang umum digunakan adalah statistik Autocorrelation Integrated Moving Average (model ARIMA) dan metode kedua yang relatif baru, yaitu pembelajaran mesin Long Short Term Memory (LSTM). Sebelum data diproses dengan kedua metode, pembersihan data dan pengoptimalan data dilakukan. Optimalisasi data adalah proses transformasi untuk menghilangkan elemen tren dan variasi dari data. Transformasi terdiri dari 7 hasil kombinasi dari proses Log, Moving Average (MA), Exponential Weigh Moving Average (EWMA), dan Differencing (Diff). Tujuh proses masing-masing digunakan dalam proses ARIMA dan LSTM. Sehingga 14 prediksi akan diperoleh (7 dari proses ARIMA dan 7 dari proses LSTM). Dari 14 hasil prediksi diperoleh nilai RMSE terkecil untuk ARIMA adalah 2% dan nilai RMSE terkecil untuk LSTM adalah 1%. Hasil penelitian ini menggunakan 7 kombinasi proses transformasi, dapat meningkatkan tingkat akurasi prediksi dari ARIMA dan LSTM. Dimana akurasi mesin pembelajaran LSTM dengan menggunakan data stok Telkom memiliki akurasi lebih tinggi dari ARIMA.
 
Publisher Universitas Multimedia Nusantara
 
Date 2020-07-02
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
 
Identifier http://ejournals.umn.ac.id/index.php/SI/article/view/1223
10.31937/si.v9i1.1223
 
Source Ultima InfoSys : Jurnal Ilmu Sistem Informasi; Vol 11 No 1 (2020): Ultima InfoSys : Jurnal Ilmu Sistem Informasi; 1-7
ULTIMA InfoSys; Vol 11 No 1 (2020): Ultima InfoSys : Jurnal Ilmu Sistem Informasi; 1-7
2549-4015
2085-4579
10.31937/si.v9i1
 
Language ind
 
Relation http://ejournals.umn.ac.id/index.php/SI/article/view/1223/959
 
Rights Copyright (c) 2020 Adhitio Satyo Bayangkari Karno
http://creativecommons.org/licenses/by-sa/4.0
 

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