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All Journal ILMU KELAUTAN: Indonesian Journal of Marine Sciences Civil Engineering Dimension Indonesian Review of Physics (IRiP)
Hassan, Rohana
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Astronomy Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Education Materials Science & Nanotechnology

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Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio Alfaris, Lulut; Firdaus, Anas Noor; Nyuswantoro, Ukta Indra; Siagian, Ruben Cornelius; Muhammad, Aldi Cahya; Hassan, Rohana; Aunzo, Jr., Rodulfo T.; Ariefka, Reza
ILMU KELAUTAN: Indonesian Journal of Marine Sciences Vol 29, No 2 (2024): Ilmu Kelautan
Publisher : Marine Science Department Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ik.ijms.29.2.273-284

Abstract

This research investigates the comparative predictive efficacy of two leading machine learning methodologies, specifically the XGBoost and Random Forest models, in estimating ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions along the east coast of North America. Using annual temperature datasets and relevant oceanographic parameters, the data is carefully processed, cleaned and sorted into training and test subsets via the RStudio Platform. The performance evaluation model is carried out using predetermined machine learning assessment criteria, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared. The results show the superiority of the XGBoost model compared to Random Forest in terms of prediction accuracy and minimizing prediction errors. The XGBoost model shows lower MSE values and higher R-squared values than the Random Forest model, indicating its better capacity in explaining data variations. XGBoost consistently provides more accurate predictions and shows higher sensitivity in identifying important factors influencing ocean temperature fluctuations than Random Forest. This research significantly improves understanding and prognostic capabilities regarding ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions. Empirical evidence underlines the efficacy of the XGBoost model in predicting ocean temperatures in the studied region. Continuous model evaluation and parameter refinement for both methodologies is critical to establishing standards for optimal prediction performance. The findings of this research have implications for the fields of oceanography and climate science, and offer potential pathways to comprehensively understand and mitigate the impacts of climate change on marine ecosystems.
Assessing the Efficacy of the UV Index in Predicting Surface UV Radiation: A Comprehensive Analysis Using Statistical and Machine Learning Methods Ervianto, Edy; Marpaung, Noveri Lysbetti; Raisal, Abu Yazid; Hutabarat, Sakti; Hassan, Rohana; Siagian , Ruben Cornelius; Nurhalim, Nurhalim; Amri, Rahyul
Indonesian Review of Physics Vol. 6 No. 2 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/irip.v6i2.8216

Abstract

The study investigated the relationship between the UV Index and measurements of ultraviolet A (UVA) and ultraviolet B (UVB) radiation to evaluate the effectiveness of the UV Index in predicting and understanding UV radiation at the surface. The implications of this study are significant for public health policies and UV protection strategies. This study used a variety of statistical analyses and modelling techniques, including ANOVA, Naive Bayes classification, decision trees, artificial neural networks, support vector machines (SVM), and k-means clustering, to examine relationships and predict UV Index values. ANOVA analysis showed a significant relationship between the UV Index and UVA and UVB measurements. Prediction models such as Naive Bayes classification, decision trees, and artificial neural networks showed variability in their accuracy. Notably, SVM showed a high degree of accuracy in predicting UV Index values, while k-means clustering effectively clustered the data based on similarities in UV Index and UV measurements. These findings confirm that the UV Index is a reliable indicator for predicting and understanding UV radiation levels at the Earth's surface. This research underscores the importance of developing more accurate and precise UV Index prediction models. Further investigation is essential to understand the temporal variations and environmental impacts on the UV Index, as well as the broader implications of UV exposure on public health. This study lays a strong foundation for the development of early warning systems and more effective UV protection strategies, ultimately improving public health outcomes and safety measures against UV radiation.
The Axial Compression Capacity of Finger-Jointed Laminated Board Made from Rubber Wood Species Awaludin, Ali; Sulhan, Muhammad Afif; Effendi, Mahmud Kori; Hassan, Rohana
Civil Engineering Dimension Vol. 27 No. 2 (2025): SEPTEMBER 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/ced.27.2.113-122

Abstract

The utilisation of Rubberwood was an effort to provide an alternative to low-cost housing in Indonesia. This study investigated the use of Rubberwood Finger-Jointed Laminated Board (FJLB) under compression loading parallel to the grain. The investigation included laboratory experiments and numerical analysis. The experiments were conducted using two specimens of FJLB members, each with a length of 2000 mm and a cross-sectional dimension of 100 mm × 100 mm². Finite element analysis (FEA) was employed to predict the axial capacity, considering non-linearity, contact boundary conditions, and buckling analysis of the material. The study found an average axial capacity of 150.9 kN for the two specimens, which was 3.2% higher than the FEA and 5.4% higher compared to the Euler formula. Laboratory measurements revealed that initially, the stress distribution in the cross-section was uniform, then suddenly changed to a combination of tension and compression during the final loading stage.
Co-Authors Abu Yazid Raisal Aldi Cahya Muhammad Ali Awaludin Ariefka, Reza Aunzo, Jr., Rodulfo T. Edy Ervianto Firdaus, Anas Noor Lulut Alfaris Mahmud Kori Effendi Noveri Lysbetti M Nurhalim Nurhalim, Nurhalim Nyuswantoro, Ukta Indra Rahyul Amri Sakti Hutabarat Siagian , Ruben Cornelius Siagian, Ruben Cornelius Sulhan, Muhammad Afif