Penerapan Normalisasi Data pada Angkatan Kerja Indonesia Bulan Februari 2025 Berdasarkan Kelompok Umur - Anastasya Jesica Sidauruk; Juan Sebastian Sirait; Sardo Sipayung (JDMIS: Journal of Data Mining and Information Systems, 2026)
Data normalization is a crucial initial step in the data mining process, aiming to reduce scale differences in numerical attributes, allowing for more objective and accurate analysis. This study aims to implement and evaluate data normalization techniques on the Indonesian workforce in February 2025 based on age category. The data used is secondary data obtained from the Central Bureau of Statistics (BPS) thru the National Labor Force Survey (SAKERNAS), which includes numerical attributes such as the number of employed people, the number of unemployed, the size of the labor force, and the percentage of the working population. The normalization methods used in this study consist of Min-Max Normalization, Z-Score Normalization, and Decimal Scaling Normalization. The research process includes data collection, selection of data from the period February 2025, data cleaning, application of normalization techniques, and analysis of the normalization results. The research findings indicate that all three normalization methods successfully leveled the value scales across attributes that previously showed significant differences in their value ranges. Min-Max normalization is effective in converting data to a specific range, Z-Score can identify deviations from the mean value, while Decimal Scaling facilitates proportional comparisons between age categories. Empirically, this study confirms that the 25-44 age group will be the most dominant in the structure of the Indonesian workforce in February 2025. Implementing data normalization has proven to improve data quality and support more accurate labor analysis.