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All Journal Jurnal Jaringan Telekomunikasi
Wijayanti , Rieke Adriati
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Painting Security System using TensorFlow Based Object Detection Method PRASETYA, MUHAMMAD YOGA AKBAR; Anshori , M. Abdullah; Wijayanti , Rieke Adriati
Jurnal Jaringan Telekomunikasi Vol 14 No 1 (2024): Vol. 14 No. 01 (2024) : Maret 2024
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jartel.v14i1.783

Abstract

Painting is an art form that should be preserved. Raden Saleh, Affandi, Hendra Gunawan, and Nyoman Gunarsa's paintings are among those of outstanding artistic worth. The existence of paintings may be jeopardized if the painting protection system is poor. Several well-known examples of art theft include the theft of Affandi's paintings in 1973 and Raden Saleh's paintings in 2007. Apart from famous paintings, personal paintings, which are equally expensive, are frequently stolen. This, of course, concerns the general public, particularly private art collectors with minimal security. Based on these problems, specific management is needed through the "SI LUKAS" Painting Security System. Using the TensorFlow-Based Object Detection Method, a system innovation that can continuously monitor the whereabouts of artworks and send notifications via Telegram, can be accessed from anywhere and at any time using TensorFlow. Based on evaluating the system's delay in reading items at a distance, it is discovered that the system can detect all objects, specifically hands and paintings, within a distance of 50cm to 550cm, however the latency varies. The average accuracy of object detection from various angles was found to be 89.6% for hand objects and 96.4% for painting objects, placing them in the very high category. In other words, the SI LUKAS system is implementable.
Co-Authors Moh. Abdullah Anshori PRASETYA, MUHAMMAD YOGA AKBAR