Implementasi Metode Learning Vector Quantization (LVQ) untuk Klasifikasi Jumlah Penduduk Menurut Jenis Kelamin dan Kabupaten di Sumatera Utara - Nur Saida; Muhammad Yasin (Lembaga Pengembangan Kinerja Dosen, 2025)
North Sumatra Province has a large population and is spread across various districts, so an effective system is needed to manage and analyze population data. This research aims to implement the Learning Vector Quantization (LVQ) method in classifying population based on gender and district in North Sumatra. The LVQ method was chosen because of its ability to perform classification based on supervised learning that utilizes vector prototypes. The data used is sourced from the Central Bureau of Statistics (BPS) of North Sumatra in 2022 and analyzed using customized parameters in RapidMiner software. This research involves several stages, starting from data collection, UML-based system design, variable selection, to the application and testing of classification models. The results showed that the LVQ method was able to classify the population based on gender and district accurately and efficiently. It is expected that this classification system can be the basis for decision-making in regional development planning and accelerate government programs related to population distribution.