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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System KOMPUTIKA - Jurnal Sistem Komputer Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Lembaga Pengabdian Kepada Masyarakat Undana RENATA Jurnal Pengabdian Masyarakat Kita Semua Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Journal of Applied Research In Computer Science and Information Systems
Silvester Tena
Teknik Elektro, Universitas Nusa Cendana, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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Analisis Perbandingan Kualitas Kompresi Citra Menggunakan Algoritma Shannon-Fano dan Huffman Coding Serta Pengujian Efek Noise AWGN Pada Citra Rekonstruksi Mahalalila Hayuning Sekarbayu; Beby H. A. Manafe; Silvester Tena
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.17674

Abstract

Advances in information technology currently play an important role in the rapid exchange of information. Information in the form of images requires adequate storage media due to the large size of images, thus requiring an efficient method, namely image compression. This study aimed to evaluate the performance of the Shannon-Fano and Huffman Coding algorithms in terms of compression ratio, processing time, and resistance to Additive White Gaussian Noise (AWGN). The solution in this study was the application and comparison of two compression algorithms to determine the more efficient compression algorithm. The algorithms used were Shannon-Fano and Huffman Coding for the compression of 256×256 and 512×512 BMP images. After the reconstruction process, the images were tested using the AWGN noise effect with noise variance ranging from 0.01 to 0.5 and filtered using a Gaussian filter to assess the image’s resistance to noise. The results showed that the Huffman Coding algorithm produced higher compression ratios of 5.60% and 5.65% compared to Shannon-Fano’s 5.51% and 5.58% for images measuring 256×256 and 512×512, respectively. However, the Huffman Coding algorithm required longer processing time than Shannon-Fano. AWGN noise testing showed that an increase in noise variance reduced image quality, but the application of a Gaussian filter could improve image quality. This study showed that Huffman Coding is more efficient in compression algorithms, while Shannon-Fano is superior in processing time, and the reconstructed image was able to maintain image quality after being affected by noise. The results can be used as a reference in selecting an efficient image compression algorithm to improve transmission channel performance
Preference-Driven Medical Image Retrieval using a Dual-Head DenseNet-121 and Multi-Objective Skyline Query for COVID-19 Detection Handoko, Slamet Handoko; Prayitno, Prayitno; Tena, Silvester; Putra, Karisma Trinanda; Sunardi, Sunardi; Prasetyo, Eko; Damarjati, Cahya
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5884

Abstract

This study addresses the limitation of single-objective content-based image retrieval in medical imaging, which fails to consider multiple clinical preferences such as image quality. The objective is to develop a preference-driven retrieval system for COVID-19 chest radiography images. A hybrid approach is proposed by integrating a Dual-Head DenseNet-121 model for feature extraction and quality regression with a multi-objective skyline query algorithm for retrieval optimization. The system evaluates multiple image quality dimensions, including sharpness, contrast, exposure, signal-to-noise ratio, and entropy. Experimental results demonstrate that the proposed method achieves 100% Pareto efficiency and improves diversity and hypervolume coverage compared to conventional methods. This approach provides a more flexible and effective multi-objective retrieval mechanism, contributing to the advancement of intelligent medical image retrieval systems in computer science.
Co-Authors Beby H. A. Manafe Beby H.A. Manafe Brian J. Mooy Cahya Damarjati Dominikus, Wara Sabon Dwiandiyanta, Bernadectus Yudi Eko Prasetyo Gregorius Klau Halla, Maria Arnoldia Hendrik J. Djahi Hendro FJ Lami Herry Zadrak Kotta I Wayan Sukarjita Igi Ardiyanto Johanis F.M Bowakh Kalvein Rantelobo Karisma Trinanda Putra, Karisma Trinanda Lejap, Marianus Yakobus Lili Mahalalila Hayuning Sekarbayu Manafe, Beby H. A. Melzando L. F Tari Muhammad Khoiruddin Mulki Indana Zulfa, Mulki Indana Ndoloe, Welmy M Nursalim Nursalim Nursalim Nursalim Pratama, Bima Gerry Prayitno Prayitno Rizkiono, Sampurna Dadi Rudy Hartanto Sampeallo, Agusthinus S Samy Y. Doo Sarlince O. Manu Seyk, Madeleine Nizara Slamet Handoko Stephanie I. Pella Sudirman Syam Sunardi Sunardi Syahrul Syahrul Tari, Melzando L. F Tulit Ina, Wenefrida Varly M Ndolu Wellem Fridz Galla Welmy M. Ndoloe Wenefrida T Ina Yoga Arya Setyadi