Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing

Abstract

Long Short Term Memory (LSTM) is known as optimized Recurrent Neural Network (RNN) architectures that overcome RNN’s lact about maintaining long period of memories. As part of machine learning networks, LSTM also notable as the right choice for time-series prediction. Currently, machine learning is a burning issue in economic world, abundant studies such predicting macroeconomic and microeconomics indicators are emerge. Inflation rate has been used for decision making for central banks also private sector. In Indonesia, CPI (Consumer Price Index) is one of best practice inflation indicators besides Wholesale Price Index and The Gross Domestic Product (GDP). Since CPI data could be used as a direction for next inflation move, we conducted CPI prediction model using LSTM method. The network model input consists of 28 variables of staple price in Surabaya and the output is CPI value, also the entire development of prediction model are done in Amazon Web Service (AWS) Cloud. In the interest of accuracy improvement, we used several optimization algorithm i.e. Stochastic Gradient Descent (sgd), Root Mean Square Propagation (RMSProp), Adaptive Gradient(AdaGrad), Adaptive moment (Adam), Adadelta, Nesterov Adam (Nadam) and Adamax. The results indicate that Nadam has 4,008 RMSE’s value, less than other algorithm which indicate the most accurate optimization algorithm to predict CPI value.

Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM)

“…Salah satu studi yang telah digunakan secara luas oleh para ahli untuk membangun model prediksi adalah data mining [10]. Deep learning memiliki beragam algoritma yang berfokus pada pembelajaran representasi data (nonlinear) bertingkat [11].…”

“…Gambar 3. Arsitektur LSTM Pada lapisan tersembunyi terdapat memory cell yang terdiri dari tiga gerbang, yaitu input gate( 𝑖 𝑡 ), forget gate (𝑓 𝑡 ), dan output gate (𝑜 𝑡 )[10]. Gambaran lebih jelasnya dapat dilihat pada Gambar 4.…”

Daily Forecasting Trend Jakarta Composite Index (JCI) Using Multivariate Long Short Term Memory

“…The results of the three LST models carried out, namely LSTM regression, LSTM with sliding windows, and LSTM with time steps, no model provides optimal training and testing results simultaneously. Soffa Zahara, Sugianto, and M. Bahril Ilmiddafiq (2019), using seven optimization algorithms showed that Nesterov Adam (Adam) has the highest level of accuracy compared to other algorithms. Both research by Ahmad Fauzi (2019) and by Kevin Johan, Julio C. Young, and Seng Hansun (2019) produced accurate models.…”

Pemanfaatan Aplikasi Owncloud Pada Sistem Keamanan Cloud Computing

“…Keterbatasan dalam penyimpanan data sering dijumpai ketika kita akan menyimpan file-file yang akan dipindahkan ke database internet seperti yang biasa dilakukan saat ini dengan melakukan penyimpanan data pada email, google drive, ataupun dropbox. Cara seperti ini sering disarankan untuk menanggulangi kekhawatiran kita terhadap kehilangan data, dengan melakukan hal ini kita menjadi tidak khawatir akan kehilangan data, karena telah memiliki back-up data yang tersimpan di email, google drive, dan dropbox (Suryati et al, 2019) (Zahara et al, 2019).…”