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All Journal Jurnal Rekayasa Material, Manufaktur & Energi
Fikri, Thaharul
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Aerospace Engineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Analisis Perpindahan Panas Pada Peralatan Pengering Multi Tingkat Secara Numerik Fikri, Thaharul; Syuhada, Ahmad; Thaib, Razali; Bahri, Samsul
Rekayasa Material, Manufaktur dan Energi Vol 8, No 2: JULI 2025
Publisher : Fakultas Teknik UMSU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/rmme.v8i2.25979

Abstract

Drying equipment is a device commonly used to remove moisture from various materials, particularly food products, to extend their shelf life and usability. However, in multi-tier drying systems, a common issue is the non-uniform temperature distribution between levels. This problem arises due to the uneven distribution of hot gas from the fuel source, causing the temperature on racks closer to the heat source to be higher than those on upper levels. To address this issue, heat ducts and fins were added to each level. The fins serve to evenly distribute heat from the hot gas channels throughout the drying chamber. Due to the high costs associated with experimental testing, this study adopts a numerical approach. The numerical simulation is conducted using Computational Fluid Dynamics (CFD) via SOLIDWORKS 2022 to analyze fluid flow and heat transfer within the dryer. The results indicate that the nine-level dryer remains operationally efficient, with the lowest temperature difference (∆T) recorded at 1.6°C when operating at 60°C.
Machine Learning-Based Regression Model for Predicting Global Horizontal Radiation and Global Horizontal Irradiance: A Case Study in Banda Aceh Fajar Sabri, M Salamul; Muhammad, Ikramullah; Rizqullah, Akbar; Fikri, Thaharul; Fajri, Nural; Mizanus Sabri, Faris Ahmad
Rekayasa Material, Manufaktur dan Energi Vol 8, No 2: JULI 2025
Publisher : Fakultas Teknik UMSU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/rmme.v8i2.26011

Abstract

Global Horizontal Radiation (GHR) and Global Horizontal Illumination (GHI) are critical environmental parameters that play a vital role in solar energy development, precision agriculture, and sustainable urban planning. However, their prediction remains challenging due to the high variability caused by atmospheric conditions. This study evaluates the performance of various machine learning models in predicting GHR and GHI using a comprehensive dataset comprising 29 environmental features. The models tested include Linear Regression, Random Forest Regressor, XGBoost Regressor, LightGBM Regressor, Support Vector Regressor (SVR), and Artificial Neural Network (ANN). The results consistently show that ensemble-based models, particularly LightGBM Regressor, provide the best predictive performance for both target variables, achieving very high R-squared values (approaching 0.999). XGBoost and Random Forest also demonstrate highly competitive performance. ANN performs well, while Linear Regression and SVR show lower accuracy. These findings underscore the significant potential of advanced machine learning models in predicting environmental parameters with high accuracy, which has important implications for renewable energy optimization, smart agriculture, and sustainable urban planning.
Co-Authors Ahmad Syuhada Fajar Sabri, M Salamul Fajri, Nural Mizanus Sabri, Faris Ahmad Muhammad, Ikramullah Razali Thaib Rizqullah, Akbar Samsul Bahri