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All Journal Jurnal ICT : Information and Communication Technologies
Sari, Deassy Ratna Juwita
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering

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Use of Differential Evolution Algorithm for Parameter Optimization in Weather Prediction Models Permana, Nana Yudi; Sari, Deassy Ratna Juwita
Jurnal ICT : Information and Communication Technologies Vol. 14 No. 2 (2023): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v14i2.137

Abstract

This research aims to optimize the parameters in a weather prediction model using the Differential Evolution (DE) algorithm, with a focus on improving the accuracy of more reliable weather predictions. The main problems faced in developing weather prediction models are model complexity and uncertainty in parameterization. The DE method is used to adjust the complex parameters in the model, resulting in a significant improvement in weather prediction accuracy based on evaluation using observational data. The implications of this research are that it makes a valuable contribution to our understanding of parameter optimization in weather prediction, as well as improving our ability to predict atmospheric conditions more accurately and reliably.
Optimization of K-Means Algorithm for Big Data Clustering Using Computational Distribution Approach Sari, Deassy Ratna Juwita; Permana, Nana Yudi
Jurnal ICT : Information and Communication Technologies Vol. 14 No. 2 (2023): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v14i2.138

Abstract

In the growing digital era, big data clustering becomes a major challenge in data analysis, especially with the well-known K-Means Algorithm that has limitations in dealing with large-scale data. This study aims to optimize the K-Means Algorithm for big data clustering with a computational distribution approach, to improve clustering efficiency and accuracy. We use the computational distribution approach to process data in parallel across multiple computing nodes, optimize memory usage, develop an intelligent cluster center selection algorithm, and optimize communication between nodes. The implementation of this optimization method successfully improves the efficiency and accuracy of big data clustering, reduces execution time and memory consumption. The practical implications include better business decision making and more effective marketing strategies based on more precise customer data analysis.
Co-Authors Permana, Nana Yudi