Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal bit-Tech Jurnal Informatika dan Teknik Elektro
Salsabilah, Andini Fitriyah
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementasi Algoritma Regresi Linear untuk Memprediksi Harga Emas Salsabilah, Andini Fitriyah; Hanafi, Achmad Arbi; Nurilhaq, Muhammad Sabili; Wira, Putra Dwi
INTRO : Journal Informatika dan Teknik Elektro Vol 3 No 2 (2024): INTRO : Jurnal Informatika dan Teknik Elektro Edisi Desember 2024
Publisher : Fakultas Teknik dan Informatika Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/intro.v3i2.2132

Abstract

This study aims to predict gold prices using several independent variables, including the silver exchange rate (SLV), the S&P 500 index (SPX), the United States Oil Fund (USO) stock exchange rate, and the Euro (EUR) to United States dollar (USD) exchange rate. The data used in this study is secondary data sourced from "Gold Price Data," comprising a total of 2290 observations and 7 columns. The method employed is regression, which is a technique for building predictive models based on given input values. The prediction results are evaluated based on the Root Mean Square Error (RMSE) value, where a smaller RMSE indicates better accuracy. The study's results show that the single-variable model has an accuracy of 73%, while the multi-variable model has an accuracy of 84%. To improve prediction accuracy, this study recommends using alternative predictive models and improving the dataset division to ensure a more representative distribution. This research not only contributes to gold price prediction but also to the development of more accurate predictive models by utilizing relevant economic variables. Keywords: gold price prediction, regression, silver exchange rate, S&P 500 index, RMSE.
Stacking Ensemble of XGBoost, LightGBM, and CatBoost for Green Economy Index Prediction Salsabilah, Andini Fitriyah; Rahmat, Basuki; Puspaningrum, Eva Yulia
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2530

Abstract

Indonesia faces persistent challenges in achieving sustainable development, particularly in harmonizing economic growth with environmental sustainability. The imbalance among economic, social, and environmental dimensions necessitates a comprehensive and reliable measurement tool to assess progress toward a green economy. The Green Economy Index (GEI), developed by the Ministry of National Development Planning (BAPPENAS), serves this function. However, limited data availability at the provincial level, such as in East Java, hampers accurate evaluation and informed policy formulation. This study aims to develop a machine learning-based predictive model for the GEI using a stacking ensemble approach that combines three powerful algorithms: XGBoost, LightGBM, and CatBoost. The model was built using relevant economic, social, and environmental indicators and evaluated on a holdout dataset to assess its predictive accuracy and generalizability. The results show that the stacking ensemble model achieved superior performance compared to the individual models, recording an RMSE of 0.0298, MAE of 0.0225, and the R² score of 0.9774. In comparison, CatBoost, XGBoost, and LightGBM individually performed with slightly lower accuracy. These findings confirm that the stacking ensemble approach is highly effective for predicting GEI values and offers a practical, data-driven solution for supporting sustainable development strategies at the regional level. The study concludes that such predictive tools can significantly enhance policy planning and monitoring of green economic growth, although further research is recommended to validate the model across other provinces.
Co-Authors Basuki Rahmat Masdi Siduppa Eva Yulia Puspaningrum Hanafi, Achmad Arbi Nurilhaq, Muhammad Sabili Wira, Putra Dwi