Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization JOURNAL OF APPLIED INFORMATICS AND COMPUTING Data Science: Journal of Computing and Applied Informatics
Saputri, Prilyandari Dina
Unknown Affiliation
Computer Science & IT

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Stock Market Index Prediction using Bi-directional Long Short-Term Memory Majid, Muhammad Althaf; Saputri, Prilyandari Dina; Soehardjoepri, Soehardjoepri
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7195

Abstract

The IHSG (Indonesia Stock Exchange Composite Index) is a stock price index in the Indonesia Stock Exchange (BEI) that serves as an indicator reflecting the performance of company stocks through stock price movements. Therefore, IHSG becomes a reference for investors in making investment decisions. Advanced stock exchanges generally have a strong influence on other stock exchanges. Several studies have proven the influence of one global index on another. Global index is a term that refers to each country's index to represent the movement of its country's stock performance. Forecasting IHSG can be one of the analyses that help investors make wise decisions when investing. To obtain an IHSG forecasting model, an appropriate and suitable method is needed, especially for data that has a large amount. LSTM is a development of Recurrent Neural Network (RNN) which has the ability to remember information in a longer period of time, while Bi-LSTM is a development of LSTM which has the ability to remember information longer and can understand more complex patterns than LSTM. This research provide the IHSG forecasting based on global index factors. The results showed that the best Bi-LSTM model (6-9-1) had a better performance in predicting and forecasting JCI movements with a MAPE value of 0.572314% better than the best LSTM model (4-10-1) which had a MAPE of 0.74326%. With forecasting based on the Bi-LSTM model, it is expected to help investors in making decisions on the Indonesia Stock Exchange (IDX).
Preventing recession through GDP growth prediction: A classical and machine learning classification approach Saputri, Prilyandari Dina; Angrenani, Arin Berliana; Fitriana, Ika Nur Laily
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 2 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v7.i2-10507

Abstract

Classification methods are a popular method applied in many various fields of science. To represent the effect of predictor factors on categorical response variables, different machine learning classification algorithms are used, namely logistic regression, neural network (NN), random forest, support vector machine (SVM), and bayesian model averaging (BMA). Every classifier has its unique characteristic, performing well in certain datasets but not in others. Hence, it is always a quest to find the best classifier to use for a certain dataset. Economic growth, most commonly using a gross regional domestic product, is experiencing a recession or acceleration, especially before and during the COVID-19 pandemic. This research proposed a comparison of classification methods using regional GDP data for 2019-2020, before and during the COVID-19 pandemic, by predictor variables; percentage of workers, foreign direct investment (PMA), regional revenue (PAD), general allocation fund (DAU), revenue sharing fund (DBH), and the dummy of COVID-19. The results are that all selected machine learning models can classify the regional GDP growth perfectly for the training data, but, NN model outperforms the other methods with an accuracy of 100% in training and testing data. COVID-19 and the PMA are the most significant variables predicting regional GDP growth for all models. Further research relating to interpretable machine learning, such as feature interaction, global surrogate, and Shapley values, is also necessary to predict regional GDP growth using machine learning methods.
Prediction Intervals for Extreme Rainfall in Indonesia using Monotone Composite Quantile Regression Neural Networks Saputri, Prilyandari Dina; Azwarini, Rahmania; Adipradana, Dimaz Wisnu
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3186

Abstract

Rainfall data may contain nonlinear, complex, and extreme characteristics. Weather monitoring can be performed by predicting rainfall as the cause of flooding and providing early warnings to ensure smooth evacuation. Classical methods, such as ARIMA, are unable to capture rainfall data patterns. A standard method for forecasting complex datasets is the use of neural networks. The neural network method failed to produce a prediction interval due to the limitation of the standard error calculation. The use of the Monotone Composite Quantile Regression Neural Network (MCQRNN) enables the accommodation of complex patterns and the production of interval predictions through its quantiles. The crossing problems in the quantile estimation were also resolved. In this study, we utilized four rainfall datasets from different locations: Central Java, West Java, South Sumatra, and North Sumatra. The lower and upper bounds were compiled from 2.5% and 97.5%, respectively. The point forecasts are constructed from the 50% quantile. Furthermore, the point forecast and interval prediction were compared to the standard classical forecasting method, i.e., ARIMA. The results demonstrated that the MCQRNN model outperforms the ARIMA model in terms of point forecasting. As the forecasting period is extended, the interval prediction of MCQRNN tends to become more consistent, whereas the width prediction of the ARIMA model becomes broader. Hence, the MCQRNN interval predictions are also suitable for long-term forecasting. Further research was required to evaluate the performance of prediction intervals.
Co-Authors Adipradana, Dimaz Wisnu Alfajriyah, Aimmatul Ummah Angrenani, Arin Berliana Azwarini, Rahmania Fitriana, Ika Nur Laily Hakiki, M. Taufik Imam Safawi Ahmad, Imam Safawi Majid, Muhammad Althaf Mohamad Atok Muslichah Erma Widiana Siswono, Galuh Oktavia Soehardjoepri, Soehardjoepri Ulil Azmi Widyaningrum, Mahmudah Enny Yulius Hari