Article Per Year (5 Year)
p-Index From 2020 - 2025
3.703
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ILMU KELAUTAN: Indonesian Journal of Marine Sciences Jurnal Keteknikan Pertanian IPTEK Journal of Proceedings Series PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmu Komputer dan Agri-Informatika International Journal of Advances in Intelligent Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Building of Informatics, Technology and Science Journal of Robotics and Control (JRC) Jurnal Pusat Inovasi Masyarakat Cylinder : Jurnal Ilmiah Teknik Mesin
Karlisa Priandana
Computer Science Department Faculty Of Mathematics And Natural Sciences IPB University (Bogor Agricultural University)
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

Published : 27 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : International Journal of Advances in Intelligent Informatics

 1 
Analysis and review of the possibility of using the generative model as a compression technique in DNA data storage: review and future research agenda Muhammad Rafi Muttaqin; Yeni Herdiyeni; Agus Buono; Karlisa Priandana; Iskandar Zulkarnaen Siregar
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.1063

Abstract

The amount of data in this world is getting higher, and overwriting technology also has severe challenges. Data growth is expected to grow to 175 ZB by 2025. Data storage technology in DNA is an alternative technology with potential in information storage, mainly digital data. One of the stages of storing information on DNA is synthesis. This synthesis process costs very high, so it is necessary to integrate compression techniques for digital data to minimize the costs incurred. One of the models used in compression techniques is the generative model. This paper aims to see if compression using this generative model allows it to be integrated into data storage methods on DNA. To this end, we have conducted a Systematic Literature Review using the PRISMA method in selecting papers. We took the source of the papers from four leading databases and other additional databases. Out of 2440 papers, we finally decided on 34 primary papers for detailed analysis. This systematic literature review (SLR) presents and categorizes based on research questions, namely discussing machine learning methods applied in DNA storage, identifying compression techniques for DNA storage, knowing the role of deep learning in the compression process for DNA storage, knowing how generative models are associated with deep learning, knowing how generative models are applied in the compression process, and knowing latent space can be formed. The study highlights open problems that need to be solved and provides an identified research direction.
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 Ahmad Zulfikar S Alex Coccia Annisa Utami Seminar Arsy Novita Syahada Arsy Novita Syahada Auzi Asfarian Bib Paruhum Silalahi Bonang Waspadadi Ligar Doli Aryo Dani Sinaga Doli Aryo Dani Sinaga Elisabeth Sri Hendrastuti Evy Damayanthi Farohaji Kurniawan Handoko, Dadang Harry Imantho Hasan, Rozaimi Che Henry Munandar Manik Herianto Herianto Hestirianoto, Totok Hidayat Hidayat Hidayat Husin Alatas Iis Rodiah Indra Jaya Irene Pricella Situmorang Irene Pricella Situmorang Irman Hermadi Ishak, Sahrial Ihsani ISKANDAR ZULKARNAEN SIREGAR Kahfi Gunardi Karina Eku Dwinanda Gunawan Kudang Boro Seminar Laksono Trisnantoro Leo P. Ligthart M Alwan Farhandany M. Cholid Mawardi Medria Kusuma Dewi Hardhienata Michael Julyus Christopher Manullang Mohamad Solahudin Muhammad Hazim Muhammad Rafi Muttaqin Mukhlis Mukhlis Muttaqin, Muhammad Rafi Nazala Hafidhullah Nugraha Edhi Suyatma Peter John Morley Puspita Kartika Sari Puspita Kartika Sari RR. Ella Evrita Hestiandari Santoso, Syifa Afnani Sarah Arisanti Siregar, Ardinsyah Sri Nurdiati Sri Wahjuni Sukarman Sumanto, Sumanto Try Kusuma Wardana Wisnu Ananta Kusuma WULANDARI Yandra Arkeman