<![CDATA[As the volume of digital data continues to escalate across sectors such as education, business, and government, the demand for efficient data storage and transmission methods grows increasingly critical. Data compression algorithms offer a prevalent solution to this challenge. This study undertakes an evaluation of the Deflate algorithm's performance in compressing digital document files, specifically examining its efficacy in reducing file size and its efficiency in processing time. Employing a comparative analysis methodology, the research involves measuring file sizes before and after compression, recording compression and decompression durations on a machine with an Intel Core i5 CPU, 8 GB RAM, running Windows 10 64-bit, and calculating compression ratios. The implementation utilizes Python and the Zlib library, which directly supports the Deflate algorithm. Tests were conducted on diverse document types, including plain text files, mixed-content files, and files rich in visual elements like images. The findings indicate that the Deflate algorithm achieves a significant compression ratio, reducing file sizes by over 90% and reaching a maximum ratio of 99.60% for text files. Compression and decompression operations were most rapid for text files, averaging 0.01 seconds. However, for documents containing images, the compression ratio was considerably lower and less impactful. Notwithstanding this, the compression and decompression times remained relatively swift and consistent across all document types. These results underscore the importance of aligning compression algorithm selection with the specific content characteristics of a document to attain optimal efficiency.]]>
