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All Journal Infolitika Journal of Data Science
Nuzullah, Teuku Muhammad Faiz
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Forecasting Upwelling Phenomena in Lake Laut Tawar: A Semi-Supervised Learning Approach Ulhaq, Muhammad Zia; Farid, Muhammad; Aziza, Zahra Ifma; Nuzullah, Teuku Muhammad Faiz; Syakir, Fakhrus; Sasmita, Novi Reandy
Infolitika Journal of Data Science Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i2.211

Abstract

The current climate change is causing the upwelling phenomenon to occur frequently in lakes and reservoirs. As a result of this phenomenon, thousands of fish die, causing floating net cage fish farmers to suffer losses. From existing studies, temperature sensors are used to determine the current condition of a body of water experiencing upwelling or not. Therefore, this study applies clustering to historical climate data from 2017-2023 using a semi-supervised learning approach that produces two labels: "potential for upwelling" and "no potential for upwelling." In the clustering process, the data is divided into two clusters using K-Means Clustering, and Support Vector Machine (SVM) is chosen to classify them. The performance of the proposed algorithm is expressed with accuracy, precision, recall, and F1-score values of 0.99, 0.995, 0.970, and 0.985, respectively. The analysis results show that this model has excellent performance in identifying upwelling potential. By using this method, information about upwelling potential can be obtained more quickly and accurately, allowing fish farmers to take appropriate preventive measures. This study also shows that the combination of K-Means Clustering and Support Vector Machine (SVM) can be effectively used to analyze historical climate data and generate useful predictions.
Optimizing Energy Consumption Prediction Across the IMT-GT Region Through PCA-Based Modeling Farid, Muhammad; Nuzullah, Teuku Muhammad Faiz; Aklya, Zatul; Nazila, Syifa; Ulhaq , Muhammad Zia; Apriliansyah, Feby; Sasmita, Novi Reandy
Infolitika Journal of Data Science Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i1.286

Abstract

This study aims to improve the accuracy of energy consumption prediction in the Indonesia-Malaysia-Thailand Growth Triangle (IMT-GT) region by addressing multicollinearity among independent variables such as energy production (Mtoe), lignite coal production (million tons), crude oil production (million tons), refined oil production (million tons), natural gas production (billion cubic meters), and electricity production (terawatt-hours). By integrating Principal Component Analysis (PCA) with Random Forest (RF), six correlated variables were reduced into two uncorrelated principal components (PC1 and PC2), explaining 80.77% of the data variance. The PCA-RF hybrid model outperformed the standalone Random Forest (RF) model, with an increase in the coefficient of determination (R2) from 0.976 to 0.993. Additionally, it achieved significant reductions in error metrics, with the mean absolute error (MAE) decreasing from 5.811 to 4.169 and the root mean square error (RMSE) dropping from 9.278 to 4.786. These results demonstrate PCA’s effectiveness in isolating dominant drivers such as energy and lignite coal production while improving model stability. The framework provides policymakers with a reliable tool to forecast energy demand and align economic growth with sustainability in fossil fuel-dependent economies.
Co-Authors Aklya, Zatul Apriliansyah, Feby Aziza, Zahra Ifma Muhammad Farid Nazila, Syifa Sasmita, Novi Reandy Syakir, Fakhrus Ulhaq , Muhammad Zia Ulhaq, Muhammad Zia