Evaluasi Kompresi Lossless Huffman Statis pada Sampel Teks dan Citra Komik
Abstract
Webtoon is a digital comic platform that presents stories as images and text on Android devices. Large files can increase storage requirements and slow access to content. This study aims to apply Huffman coding to reduce the size of text and image data obtained from comics on the Webtoon application. The method consists of counting symbol frequencies, creating a priority queue, constructing a Huffman tree, assigning binary codes, and replacing the original symbols with the resulting codes. Frequently occurring symbols receive shorter codes, whereas less frequent symbols receive longer codes. The algorithm was implemented in the Python programming language. Evaluation was performed by comparing data sizes before and after compression and calculating the percentage reduction. Text data decreased from 616 bits to 189 bits, while image data decreased from 512 bits to 380 bits. The manuscript reports compression percentages of 69.4% for text and 25.7% for images. These findings indicate that Huffman coding can reduce storage requirements for the data examined. The generated Huffman tree provides the basis for reconstructing the original data during decompression. Applying this method may support more efficient storage and transmission of digital comic content. Further research is recommended to evaluate combinations of Huffman coding with other compression methods and test performance across different user devices, particularly regarding image quality and application response time.
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