<![CDATA[Rapid advancements in digital technology have made image-processing applications increasingly ac-cessible, leading to a rise in digital image manipulation cases and casting doubt on image authenticity in forensic investigations, criminal cases, and journalism. Manipulated images are difficult to distin-guish visually, while existing analysis approaches rely on disparate tools, making the identification process inefficient. This research aims to design a system that integrates Error Level Analysis (ELA) and EXIF metadata analysis to automatically detect signs of digital image manipulation. The system was developed using the Waterfall model, comprising analysis, design, implementation, testing, and maintenance phases. Testing involved black-box testing across nine functional scenarios and manual validation using nine photo samples from three different devices (SONY ILCE-6400, Apple iPhone 14, and Infinix X6725). Test results indicate that all functional scenarios operated according to specifica-tions and the system successfully detected all manipulated samples (9 out of 9) through the combina-tion of ELA visualization and EXIF metadata analysis. The integration of these two methods proved complementary, thereby enhancing the effectiveness of digital image authenticity verification. While the developed system serves as a tool for the preliminary detection of digital image manipulation, it is not intended to provide conclusive forensic evidence.]]>
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