KOMBINASI REGRESI LINEAR DAN K-MEANS UNTUK ANALISIS TREN PROYEKSI PESERTA KB PADA DINAS DPPKB KABUPATEN LABUHAN BATU - Hasyim Ar Rasyid Hasyim; Aninda Muliani Harahap Aninda (LPPM Universitas Abdurrab, 2026)
The rapid development of information technology requires public institutions to transform their data management to enhance efficiency and objectivity in decision-making. The Department of Population Control and Family Planning (DPPKB) of Labuhanbatu Regency faces challenges in managing dynamic and growing Family Planning (KB) participant data. Conventional data processing triggers issues such as reporting delays, difficulties in mapping regional characteristics, and inaccurate future service projections. This study aims to develop a web-based information system capable of analyzing trends and projecting the number of KB participants by combining K-Means Clustering and Linear Regression methods. The K-Means method is utilized to group KB participant data based on similarities in regional characteristics, status, and age into three growth rate clusters (low, medium, high). Subsequently, Linear Regression is applied to each cluster to predict the participant trend volume for the next period based on historical data from December 2024 to December 2025. The system is designed using Unified Modeling Language (UML) and built utilizing the Laravel 10 framework, PHP, and MySQL database. The system provides two main access roles: Administrator for data management and algorithm execution, and Leader (Head of Department) for real-time dashboard trend visualization monitoring. The results of this study are expected to serve as an objective, accurate, and data-driven strategic decision-making tool for DPPKB Labuhanbatu Regency in determining service strategies and resource allocation.