Implementation of Artificial Neural Network in Predicting Birth Rate in Batam City Using Backpropagation Method

Jufri, Muhammad

doi:10.33330/jurteksi.v8i1.1228

Cited by 1 publication

(1 citation statement)

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“…Efforts to predict stock prices have many variations, one of which predicts stock prices using computational calculations with certain algorithms, one of which is an artificial neural network. An artificial neural network is one of the algorithms that can be used in the case of prediction [6]. The study [7] implemented an artificial neural network to predict the stock price of KAEF during the COVID-19 pandemic.…”

Section: Introductionmentioning

confidence: 99%

Rectified Linear Units and Adaptive Moment Estimation Optimizer on ANN with Saved Model Prediction to Improve The Stock Price Prediction Framework Performance

Sekhudin,

Purwati,

Utomo

et al. 2023

Ilk. J. Ilm.

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A stock is a high-risk, high-return investment product. Prediction is one way to minimize risk by estimating future prices based on past data. There are limitations to solving the stock prediction problem from previous research: limited stock data, practical aspects of application, and less than optimal stock price prediction results. The main objective of this study is to improve the prediction performance by formulating and developing the stock price prediction framework. Furthermore, the research provides a stock price prediction framework that can produce better prediction results than the previous study with fast computation time. The proposed framework deals with data generation, pre-processing and model prediction. In further, the proposed framework includes two prediction methods for predicting stock closing prices: stored model prediction and current model prediction. This study uses an artificial neural network with Rectified Linear Units as an activation function and Adam Optimizer to predict stock prices. The model we have built for each forecasting method shows a better MAPE value than the model in previous studies. Previous research showed that the lowest MAPE was 1.38% for TLKM shares and 0.81% for BBRI. Our proposed framework based on the stored model prediction method shows a MAPE value of 0.67% for TLKM shares and 0.42% for BBRI. While the current model prediction method shows a MAPE value of 0.69% for TLKM shares and 0.89% for BBRI. Furthermore, the stored model prediction method takes 1.0 seconds to process a single prediction request, while the current model prediction takes 220 seconds.

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Section: Introductionmentioning

confidence: 99%