Pengembangan Aplikasi Web untuk Resize Citra Digital dengan Fitur Batch Processing Menggunakan Next.Js dan Sharp

Abstract

The exponential growth of digital content has increased the demand for efficient and accessible image processing tools. This research aims to develop a web-based image resize application with batch processing features using Next.js and Sharp. The research method employs Research and Development (R&D) with a Software Development Life Cycle (SDLC) approach using the Waterfall model, encompassing requirements analysis, system design, implementation, testing, deployment, and maintenance phases. The application was developed by integrating Next.js 16 framework for full-stack development, Sharp library for high-performance image processing, and JSZip for archive handling. Implemented features include flexible upload (file, folder, ZIP), downsampling and upsampling options, pixel dimension input, JPEG/JPG/PNG format conversion, and batch processing with progress monitoring. Testing results demonstrated that 100% of features were successfully implemented with a functional testing success rate of 100%. The average response time achieved 1.76 seconds per image, 41% faster than the 3-second target. The quality of the test results shows that the quality of the resized images meets very good quality standards with high structural similarity to the original images for both downsampling and upsampling. This research has produced a web application for image resizing that is accessible without installation, efficient for batch processing, and produces optimal output quality by utilizing the Mitchell interpolation kernel for downsampling and Lanczos for upsampling