Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Teknik Elektro Jurnal EECCIS Syntax Literate: Jurnal Ilmiah Indonesia Journal Zetroem SinarFe7 Jurnal Teknik Elektro: Ilmu dan Teknologi
Daniel Rohi
Adi Buana University Surabaya
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Improving Temperature Prediction Accuracy Using Stacking Ensemble Methods in Meteorological Data Nugroho, Aryo; Rohi, Daniel
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v10i12.62972

Abstract

Accurate temperature prediction is crucial for weather forecasting, influencing sectors such as agriculture, energy, and disaster management. This study aimed to improve daily temperature prediction using stacking ensemble methods on weather data from Pontianak (2021-2024). The research compared the performance of individual models, Linear Regression and Random Forest, and demonstrated how stacking—which combines multiple models—could enhance prediction accuracy. Stacking was implemented with Linear Regression and Random Forest as base learners, while a Linear Regression model served as the meta-learner to integrate the predictions from the base models. The results showed that the stacking model outperformed both individual models in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE), leading to more accurate predictions. While stacking improved accuracy, it introduced greater computational overhead compared to single-model approaches. This trade-off between accuracy and computational efficiency must be considered for real-world applications. The study demonstrated the effectiveness of stacking in enhancing temperature prediction accuracy, offering insights into how ensemble methods can improve weather forecasting.
Comparing Machine Learning and Neuro-Fuzzy Models for Hydropower Prediction Under Seasonal Operating Conditions Daniel Rohi; Akhmad Solikin
ZETROEM Vol 8 No 2 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i2.8441

Abstract

Accurate prediction of hydropower energy production is essential for operational planning and decision-making under varying hydrological conditions. However, comparative evaluations of high-accuracy data-driven models and interpretable intelligent models using real operational data remain limited. This study aims to compare the performance of Machine Learning and Neuro-Fuzzy approaches for predicting energy production at the Sengguruh Hydropower Plant, Indonesia. One year of engineering-validated operational data consisting of discharge, reservoir and tailrace water levels, operation time, turbine efficiency, and derived hydraulic head were used for model development and evaluation. Three Machine Learning models, namely Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Regression (SVR), were compared with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results indicate that Machine Learning models achieved superior predictive performance, with Random Forest providing the highest accuracy (RMSE = 8.42 MWh/day, MAE = 6.35 MWh/day, and R² = 0.982). In contrast, the ANFIS model produced lower prediction accuracy but offered a more interpretable representation of hydropower operating behavior. Correlation analysis further indicates that operation time and inflow discharge are the most influential operational variables associated with energy production. The findings demonstrate that Machine Learning approaches are more suitable for high-accuracy forecasting and operational optimization, whereas Neuro-Fuzzy models provide advantages in interpretability and operational representation. These results contribute to the development of intelligent hydropower forecasting systems that balance predictive performance and engineering understanding.
Co-Authors Adikurniawan, Nadia Akhmad Solikin Albert Richardo Bing Arbi Darmawan Gontani Aryo Nugroho Dion Dwipayana Utomo Hanny Hosiana Tumbelaka Lesmana, Romario Louviano Lukas Lukas Nadia Adikurniawan Ontoseno Penangsang Radita Sonixtus Arauna Romario Louviano Lesmana Setiadji, Julius Sentosa Tumbelaka, Hanny Hosiana