Electric Current Prediction Based on Voltage and Frequency Using K-Nearest Neighbors (KNN) and Ridge Regression Algorithms
Keywords:
Information technology, KNN, Ridge regression, Machine learningAbstract
The development of information technology has had a significant impact on various aspects of life, including the field of electricity. Technology, derived from the words technologia and techno, means expertise and knowledge. As time progresses, many tasks that were previously performed manually can now be simplified and automated through the use of digital technologies, including in the calculation of electric current. Calculating current based on frequency and voltage values involves a high level of complexity when done manually. To address this challenge, this study designs a system for predicting electric current using a Machine Learning approach, specifically employing the K-Nearest Neighbors (KNN) and Ridge Regression algorithms. KNN is an instance-based learning method that classifies or predicts new data based on its proximity to existing data. Meanwhile, Ridge Regression is a form of linear regularization that is effective in handling multicollinearity and overfitting in regression modeling. The methodology includes several stages: data collection, normalization, splitting the data into training and testing sets, model training, and performance evaluation using metrics such as Mean Squared Error (MSE) and the coefficient of determination (R²). The model is developed using Python as the primary programming language due to its capabilities in supporting data analysis and implementing machine learning algorithms efficiently. The results of the study show that the designed system is capable of effectively predicting electric current based on frequency and voltage inputs. Additionally, a performance comparison between the KNN and Ridge Regression algorithms is obtained, which can serve as a consideration in selecting the most appropriate prediction method based on the characteristics of electrical data. These findings are expected to contribute to the utilization of intelligent technology for more efficient and effective technical analysis in the future.
Keywords: information technology, KNN, Ridge Regression, Machine Learning, research methodology.
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