Penerapan Logistic Regression Untuk Klarifikasi Pendapatan Berdasarkan Variabel Sosial Ekonomi
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Abstract
This study aims to analyze the effectiveness of the Logistic Regression algorithm in classifying individual income levels based on socio-economic variables. The research employs a quantitative experimental approach using the Adult Census Income dataset, which consists of demographic and employment-related attributes. Data preprocessing stages include handling missing values, encoding categorical attributes, and splitting the dataset into training and testing subsets with an 80:20 ratio. Model development and evaluation were conducted using RapidMiner Studio to ensure a structured and reproducible workflow. The performance of the proposed model was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results show that the Logistic Regression model achieved an accuracy of 80.91%, indicating a reliable capability in distinguishing income categories ≤50K and >50K. The model demonstrates strong performance in identifying low-income individuals, while challenges remain in improving precision for the high-income class due to class imbalance. Overall, the findings confirm that Logistic Regression remains a relevant and interpretable baseline model for income classification tasks, particularly in socio-economic analysis and policy-oriented studies where transparency and interpretability are essential.
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