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All Journal Journal of Advanced in Information and Industrial Technology (JAIIT) INFORMATIKA
Nabilah Selayanti
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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PREDIKSI HARGA PENUTUPAN SAHAM BBRI DENGAN MODEL HYBRID LSTM-XGBOOST Nabilah Selayanti; Dwi Amalia Putri; Trimono Trimono; Mohammad Idhom
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.1011

Abstract

The ease of investing in the digital era has driven Generation Z to dominate stock market participation, particularly in blue-chip stocks such as PT Bank Rakyat Indonesia Tbk (BBRI). However, stock price fluctuations influenced by macroeconomic factors, regulations, and global market sentiment make it difficult for investors to make accurate decisions. Decisions based on insufficient information pose a significant risk of loss, especially for novice investors. This study proposes a hybrid LSTM-XGBoost approach for predicting BBRI stock prices, combining the strengths of LSTM in capturing nonlinear time series patterns and XGBoost's effectiveness in reducing prediction errors. The model leverages both historical data and feature extraction outputs from the LSTM model. Future stock price values are then predicted by XGBoost using this combined dataset. The Hybrid LSTM XGBoost model outperforms the individual base models in terms of prediction accuracy, achieving an RMSE of 117.89, MAE of 92.45, and MAPE of 2.21%.
A Hybrid Neural Network-Time Series Regression Model for Intermittent Demand Forecasting Data Amri Muhaimin; Damaliana, Aviolla Terza; Muhammad Nasrudin; Riyantoko, Prismahardi Aji; Nabilah Selayanti; Putri, Shafira Amanda
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.704

Abstract

Forecasting is a vital tool that helps us make informed decisions by predicting future events based on past data. For forecasts to be accurate, it is important that the data is reliable, complete, and consistent. Yet, the intermittent data is a unique data that is challenging to forecast. Intermittent data contains a characteristic that the data has a lot of long zeros in some periods. The zero value will influence the model to generate a forecasting model. This study aims to tackle those problems by applying a hybrid approach. We integrate the regression model and neural network to create a novel approach for forecasting intermittent data. The dataset used for this data is from Kaggle, sales at Walmart supermarket for one category only. The sales data always produce an intermittent demand pattern, because not every day are the items always sold to customers. This irregular pattern makes the data difficult to forecast using a naïve approach, such as the Croston method, exponential smoothing, and ARIMA. To evaluate the performance of our model, some metrics were calculated. We use mean squared error, root mean squared error, and root mean squared scaled error. The result shows that our proposed method outperforms the benchmark model, with an RMSSE of 0.98, which is the lowest compared to other benchmark models in the root mean squared scaled error value. This result shows promise as an exciting solution for overcoming the challenges posed by irregular data in future forecasting tasks.
Co-Authors Amri Muhaimin Damaliana, Aviolla Terza Dwi Amalia Putri Mohammad Idhom Muhammad Nasrudin Putri, Shafira Amanda Riyantoko, Prismahardi Aji Trimono Trimono