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All Journal International Journal of Advances in Intelligent Informatics ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Teknologi Sistem Informasi J-Icon : Jurnal Komputer dan Informatika
Muttaqin, Muhammad Rafi
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Journal : International Journal of Advances in Intelligent Informatics

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Biological constraint in digital data encoding: A DNA based approach for image representation Muttaqin, Muhammad Rafi; Herdiyeni, Yeni; Buono, Agus; Priandana, Karlisa; Siregar, Iskandar Zulkarnaen; Kusuma, Wisnu Ananta
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1747

Abstract

Digital data encoding is crucial for communication and data storage, but conventional techniques, such as ASCII and binary coding, have drawbacks in terms of processing speed and storage capacity. A potential substitute with parallel processing and high-capacity storage is DNA-based data encoding. The goal of this research is to develop a digital data encoding technique based on DNA, while considering biological constraints such as homopolymer and GC-content. The process involves converting image pixel values into binary format, followed by encoding into DNA sequences, ensuring they meet biological constraints. The validity of the resulting DNA sequences is assessed through transcription and translation processes. Additionally, Multiple Sequence Alignment analysis is conducted to compare the similarities between the encoded DNA sequences. The results indicate that the DNA sequences from MNIST images share similar characteristics, reflected in the phylogenetic tree's close clustering. Multiple Sequence Alignment analysis shows that biological constraints successfully preserved the core visual features, allowing accurate clustering. However, this method also faces drawbacks, particularly in the reduction of visual information and sensitivity to changes in image intensity. Despite these challenges, DNA-based encoding shows potential for digital image representation. Further development, particularly the integration of deep learning, could lead to more efficient, secure, and sustainable data storage systems, especially for image data.
Co-Authors Agus Buono Bayu, Bayu Defriani, Meriska ISKANDAR ZULKARNAEN SIREGAR Karlisa Priandana Muchtar, Fira Rizkya Muhyidin, Yusuf Pujadi, Octavia Saramitha Rohmat rohmat Sunandar, Muhammad Agus Wisnu Ananta Kusuma