Face detection in low-light conditions remains challenging due to underexposure, noise, and unstable contrast, which significantly degrade the performance of convolutional-based models. Conventional enhancement techniques, such as Histogram Equalization, Contrast-Limited Adaptive Histogram Equalization (CLAHE), and Low-Light Image Enhancement (LIME), often improve brightness but introduce visual artifacts and lack robustness for face detection. This study proposes Low-light Unified Multi-stage Enhancement Network (LUMEN), a lightweight multi-stage image enhancement pipeline designed as a preprocessing module to improve RetinaFace-based face detection under low-light conditions. LUMEN integrates Multiscale Retinex with Color Restoration, adaptive gamma correction, CLAHE-based local contrast enhancement, controlled image fusion, and Non-Local Means denoising to jointly stabilize illumination, preserve texture, and maintain visual naturalness. The method is evaluated on a low-light subset of the Human Faces Object Detection Dataset using RetinaFace. Detection performance is assessed using detection rate and confidence score, while visual quality is evaluated using no-reference metrics such as the Natural Image Quality Evaluator (NIQE) and the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Experimental results show that LUMEN achieves a face detection rate of 91% with a high confidence score (0.9545), rep-resenting an improvement of 33 percentage points over raw low-light images (58%), while maintaining detection performance comparable to CLAHE, which ranks as the second-best method, LUMEN delivers superior perceptual quality, evidenced by the lowest BRISQUE score, as well as more stable visual appearance with reduced noise amplification and fewer contrast artifacts in low-light facial regions. LUMEN also achieves the lowest BRISQUE score, indicating superior texture preservation and perceptual stability. Ablation studies confirm that Retinex and CLAHE are the most critical components for detection robustness, while gamma correction, fusion, and denoising mainly contribute to visual naturalness. These results demonstrate that LUMEN provides an effective and practical preprocessing solution for low-light face detection.

Personnel Leave Application Information System Application: The Personnel Leave Information System at the Batu Bara Regency Regional General Hospital is an application built for the web-based leave application process. This research was conducted on the Staff Leave Information System at the Regional General Hospital of Batu Bara Regency using research methods, namely the Observation method, Library Research, Field Research, needs analysis, system design, and system implementation. Use Case Diagram, Activity Diagram, Data Flow Diagram ( DFD), Sequence Diagram, Class Diagram. This application was built using software, namely the PHP programming language, HTML, CodeIgniter, Composer database used by MySQL. The application built operates on the Windows Operating System. The WEB-based Application for the Information System for Applying for Employee Leave at the Batu Bara Regency Regional General Hospital has helped the Batu Bara Regency Regional General Hospital in registering and informing employee leave applications quickly and accurately, and can also provide information to employees to find out whether the leave process has been approved or not. approved.