Prediksi Belanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM)

Sautomo, Sabar; Pardede, Hilman F.

doi:10.29207/resti.v5i1.2815

Cited by 8 publications

(7 citation statements)

References 4 publications

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“…Root Mean Square Error (RMSE) is used to measure the difference between the estimated target and the actual target by calculating the square root value of the MSE. The higher the value produced by the RMSE, the lower the level of accuracy, and vice versa, if the value of the resulting RMSE is lower, the level of accuracy is higher [31]. The RMSE formula is shown in the following equation.…”

Section: Resultsmentioning

confidence: 99%

Algorithm Optimizer in GA-LSTM for Stock Price Forecasting

Sukestiyarno,

Wiyanti,

Azizah

et al. 2024

Contemp. Math.

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Fluctuating stock prices make it difficult for investors to see investment opportunities. One tool that can help investors overcome this is represented by forecasting techniques. Long Short-Term Memory (LSTM) is one of deep learning methods used in forecasting time series. The training and success of deep learning is strongly influenced by the selection of hyperparameters. This research uses a hybrid method between the Genetic Algorithm (GA) and LSTM to find a suitable model for predicting stock prices. GA is used in optimizing the architecture such as the number of epochs, window size, and the number of LSTM units in the hidden layer. Tuning optimizer is also carried out using several optimizers to achieve the best value. From method that has been applied, it shows that the method has a good level of accuracy with MAPE values below 10% in every optimizer used. The error rate generated is quite low, in case-1 with a minimum RMSE value of 93.03 and 94.40, & in case-2 with an RMSE value of 104.99 and 150.06 during training and testing. A fairly stable and small value is generated by setting it using the Adam Optimizer.

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Section: Resultsmentioning

confidence: 99%

Algorithm Optimizer in GA-LSTM for Stock Price Forecasting

Sukestiyarno,

Wiyanti,

Azizah

et al. 2024

Contemp. Math.

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“…In building the LSTM and GRU models, this research builds and analyzes both models using Google Colab (Sautomo et al, 2021). This research carried out several stages of test analysis to determine hyperparameters to obtain the best results.…”

Section: Discussionmentioning

confidence: 99%

Python-Powered Precision: Unraveling Consumer Price Index Trends in Makassar City through a Duel of Long Short-Term Memory and Gated Recurrent Unit Models

Abd. Rahman,

First Wanita,

Rose Arisha

et al. 2023

ceddi j. inf. syst. technol.

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This research aims to carry out a predictive analysis of the Consumer Price Index in the city of Makassar to anticipate possible impacts on inflation and deflation in the future. The Consumer Price Index is an indicator that can be used as a basis for measuring changes in the prices of goods and services purchased by consumers which have an impact on inflation in a region. The CPI is very useful for knowing the level of increase in prices, services, and income, as well as measuring the amount of production costs. This data was obtained through the official website of the Central Statistics Agency (BPS) for the Makassar city area. The methods used in this research are Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results of this research show that based on analysis and testing, the LSTM model has an MAE of 1.0849 and the GRU model has an MAE of 0.9915, which shows that there is no significant difference between the two methods and both methods can work very well, however, The lowest error value was obtained in the GRU model using a 70:30 dataset ratio, 9 number of sequences, 16 neurons in hidden layer 1 and 32 neurons in hidden layer 2, and 1000 number of epochs.

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“…Pada penelitian sebelumnya, pendekatan Deep Learning dalam melakukan prediksi telah banyak dilakukan seperti melakukan prediksi harga ponsel dengan Random Forest [6], Prediksi mata uang Bitcoin menggunakan LSTM dan sentiment analisis pada Sosial Media [7], Price movement prediction of cryptocurrencies menggunakan sentiment analysis and Machine Learning [8], Prediksi harga cryptocurrency menggunakan algoritme LSTM [9], Prediksi harga minyak mentah menggunakan Jaringan Syaraf Tiruan (JST) [10], Prediksi belanja pemerintah Indonesia menggunakan Long Short-Term Memory (LSTM) [11], Prediksi penggunaan energi listrik pada rumah hunian menggunakan Long Short-Term Memory [12]. Namun pada penelitian ini, prediksi dilakukan pada komoditas pangan dari hasil pertanian menggunakan algoritme LSTM.…”

Section: Pendahuluanunclassified

Prediksi Harga Komoditas Pangan Menggunakan Algoritma Long Short-Term Memory (LSTM)

Adi¹,

Sudianto²

2022

bits

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Food is a basic need for human survival. The existence of food is influenced by production and selling prices. The problem that exists is that food producers lose out with the dynamics of selling prices. In addition, the low selling price is not commensurate with the production costs that have been spent, especially for food producers in agricultural commodities, namely local farmers. Local farmers lose money because they do not know the price of commodities when selling their agricultural products. In addition, the game of intermediaries causes local farmers to sell their crops at low prices. So from the existing problems, it is necessary to predict commodity prices to help farmers determine the commodity prices before selling their agricultural products to the market. This study aims to predict the price of food commodities, especially in Banyumas, so that local farmers can find the price of commodities before they are sold to the market. The Deep Learning method used is Long Short-Term Memory (LSTM), which can remember a collection of information that has been stored for a long time with time series data. The results obtained, the model can predict food commodity prices. Meanwhile, the prediction model with epoch 50 shows the lowest Root Mean Squared Error (RMSE) with a value of 79.19%

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Prediksi Belanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM)

Cited by 8 publications

References 4 publications

Algorithm Optimizer in GA-LSTM for Stock Price Forecasting

Algorithm Optimizer in GA-LSTM for Stock Price Forecasting

Python-Powered Precision: Unraveling Consumer Price Index Trends in Makassar City through a Duel of Long Short-Term Memory and Gated Recurrent Unit Models

Prediksi Harga Komoditas Pangan Menggunakan Algoritma Long Short-Term Memory (LSTM)

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