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All Journal Techno.Com: Jurnal Teknologi Informasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknik Informatika (JUTIF)
Pratiwi, Windy Ayu
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Evaluation of Machine Learning Models in Classifying Women's Labor Force Participation in West Java Siregar, Indra Rivaldi; Pratiwi, Windy Ayu; Nugraha, Adhiyatma; Sartono, Bagus; Firdawanti, Aulia Rizki
Techno.Com Vol. 24 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i1.11945

Abstract

This study compares four classification models—Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost)—to predict women's labor force participation in West Java, using a dataset of 62 features. After feature selection, the dataset was reduced to 31 features, followed by modeling with the top 10 most important features from each model. Model performance, evaluated using Balanced Accuracy, F1-Score, and Cohen’s Kappa, showed similar results, with RF and XGBoost slightly outperforming the others. However, the differences were not significant, indicating comparable predictive ability across models. The top 10 features from each model were averaged, and the five most influential features were selected. Key factors influencing women's employment status include household responsibilities, age, education, district minimum wage, and the age of the youngest child. The analysis found that 79.6% of unemployed women manage household duties, while employed women are less involved (18.9%). Age was significant, with employed women mostly in the 35-55 age range, correlating with older children and greater workforce participation. Additionally, employed women are more likely to come from regions with lower minimum wages, suggesting that economic necessity drives their labor market participation. Keywords: female labor force, machine learning, classification, West Java
THE COMPARISON OF ARIMA AND RNN FOR FORECASTING GOLD FUTURES CLOSING PRICES Pratiwi, Windy Ayu; Rizki, Anwar Fajar; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp397-406

Abstract

In the financial markets, accurately forecasting the closing prices of gold futures is crucial for investors and analysts. Traditional methods like ARIMA (Autoregressive Integrated Moving Average) have been widely used for this purpose, particularly for their effectiveness in short-term stable data forecasting. However, the inherent complexity and dynamic nature of financial data, coupled with trends and seasonal patterns, present significant challenges for long-term forecasting with ARIMA. Conversely, advanced methods such as Recurrent Neural Networks (RNN) have shown promise in handling these complexities and providing reliable long-term forecasts. This research seeks to evaluate and compare the performance of ARIMA and RNN in forecasting daily gold futures closing prices using forecast accuracy tests namely RMSE and MAPE, aiming to identify the optimal method that balances accuracy, stability, and adaptability to trends and seasonal variations in the financial market. The daily data for this analysis is sourced from Investing.com (https://www.investing.com).
Stacking Ensemble RNN-LSTM Models for Forecasting the IDR/USD Exchange Rate with Nonlinear Volatility Pratiwi, Windy Ayu; Sumertajaya , I Made; Notodiputro , Khairil Anwar
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5057

Abstract

Abstract - Predicting exchange rates with high volatility and nonlinear patterns presents a critical challenge in financial analysis. Deep learning models such as RNN and LSTM are widely used for their ability to capture temporal dependencies, yet each has limitations when applied individually. This study aims to enhance the prediction accuracy of the Indonesian Rupiah (IDR) to US Dollar (USD) exchange rate by implementing a stacking ensemble approach that combines RNN and LSTM models. The dataset consists of 522 weekly observations from January 2015 to December 2024, sourced from the official website of Bank Indonesia (bi.go.id). In the proposed framework, RNN and LSTM serve as base learners, while linear regression acts as the meta-learner. Model performance is evaluated using RMSE, MAPE, and MSE. The results indicate that the stacking ensemble consistently outperforms the individual models, achieving an RMSE of 117.91, a MAPE of 0.01, and an MSE of 13,901.67. The model effectively captures historical patterns and delivers stable and accurate predictions. In conclusion, the stacking ensemble approach developed in this study contributes to the advancement of ensemble learning techniques in computer science and offers practical value for financial decision-makers, particularly in managing complex and dynamic exchange rate scenarios.
Co-Authors Aulia Rizki Firdawanti Bagus Sartono I Made Sumertajaya Khairil Anwar Notodiputro Mualifah, Laily Nissa Atul Nugraha, Adhiyatma Rizki, Anwar Fajar Siregar, Indra Rivaldi Yenni Angraini