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Syahroni Hidayat
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Jurnal Teknologi Informasi dan Multimedia
Published by Puslitbang Sekawan Institute Nusa Tenggara
ISSN : 27152529     EISSN : 26849151     DOI : https://doi.org/10.35746/jtim.v2i1
Core Subject : Science,
Computer Science & IT
Cakupan dan ruang lingkup JTIM terdiri dari Databases System, Data Mining/Web Mining, Datawarehouse, Artificial Integelence, Business Integelence, Cloud & Grid Computing, Decision Support System, Human Computer & Interaction, Mobile Computing & Application, E-System, Machine Learning, Deep Learning, Information Retrievel (IR), Computer Network & Security, Multimedia System, Sistem Informasi, Sistem Informasi Geografis (GIS), Sistem Informasi Akuntansi, Database Security, Network Security, Fuzzy Logic, Expert System, Image Processing, Computer Graphic, Computer Vision, Semantic Web, Animation dan lainnya yang serumpun dengan Teknologi Informasi dan Multimedia.
Arjuna Subject : -
Articles 342 Documents
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Rancang Bangun Sistem Deteksi Manipulasi Citra Digital Berbasis Error Level Analysis (ELA) dan Analisis Metadata Exif Windi Prasetya Wibowo; Sigit Sugiyanto; Harjono Harjono; Ermadi Satria Wijaya
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 4 (2026): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i4.1142

Abstract

Rapid advancements in digital technology have made image-processing applications increasingly ac-cessible, leading to a rise in digital image manipulation cases and casting doubt on image authenticity in forensic investigations, criminal cases, and journalism. Manipulated images are difficult to distin-guish visually, while existing analysis approaches rely on disparate tools, making the identification process inefficient. This research aims to design a system that integrates Error Level Analysis (ELA) and EXIF metadata analysis to automatically detect signs of digital image manipulation. The system was developed using the Waterfall model, comprising analysis, design, implementation, testing, and maintenance phases. Testing involved black-box testing across nine functional scenarios and manual validation using nine photo samples from three different devices (SONY ILCE-6400, Apple iPhone 14, and Infinix X6725). Test results indicate that all functional scenarios operated according to specifica-tions and the system successfully detected all manipulated samples (9 out of 9) through the combina-tion of ELA visualization and EXIF metadata analysis. The integration of these two methods proved complementary, thereby enhancing the effectiveness of digital image authenticity verification. While the developed system serves as a tool for the preliminary detection of digital image manipulation, it is not intended to provide conclusive forensic evidence.
Pengenalan Instrumen Bedah Menggunakan Deskriptor Geometris Berbasis Kontur Fica Aida Nadhifatul Aini; Siti Duratun Nasiqiati Rosady; Talifatim Machfuroh; Zakiyah Amalia; Tri Luhur Indayanti Sugata
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 3 (2026): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i3.1182

Abstract

Surgical instrument recognition is a critical component in robot-assisted surgery for precisely identifying the types of instruments being utilized. This study investigates the application of contour-based geometric descriptors to recognize surgical instruments without requiring a model training process (training-free). The proposed approach extracts instrument contours and calculates three geometric descriptors: Aspect Ratio, Solidity, and Contour Ratio. The descriptor values of the input objects are compared against an offline reference descriptor matrix using the Euclidean distance measure. The main contributions of this study lie in proposing a lightweight and training-free surgical instrument recognition scheme utilizing a combination of three contour-based geometric descriptors, as well as providing a transparent and traceable (explainable) classification decision mechanism based on Euclidean distance similarity comparison across classes. Evaluation was performed using a primary laboratory dataset acquired with a Logitech Brio 4K camera (1080p, 60 fps) at a distance of 30 cm, an illumination of 300 lux, and a plain green cloth background. The testing dataset comprised 10 images containing 30 controlled instrument instances (10 instances each for Tissue Forceps, Dressing Forceps, and Mayo-Hegar Needle Holder). On this specific testing dataset, the proposed method achieved a recognition accuracy of 100% with an average processing time of 59.31 ms per image. Although obtaining perfect accuracy under controlled laboratory conditions, these results are interpreted strictly within the limits of the experimental environment and do not yet reflect generalization to complex real-world surgical images.

Page 35 of 35 | Total Record : 342

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