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All Journal Jurnas Nasional Teknologi dan Sistem Informasi INOVTEK Polbeng - Seri Informatika Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science bit-Tech Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics MEANS (Media Informasi Analisa dan Sistem) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) Indonesian Journal of Community Empowerment Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Kharisma, Ivana Lucia
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Energy Engineering Environmental Science Public Health

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Journal : Building of Informatics, Technology and Science

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Sistem Identifikasi Cerdas: Integrasi IOT dengan YOLOv8 Untuk Identifikasi Visual Kerusakan Dinding Bangunan Kamdan, Kamdan; Somantri, Somantri; Rohmat, Satria Rizki; Gumelar, Agung; Kharisma, Ivana Lucia
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7882

Abstract

Damage to non-structural building elements, particularly walls, can serve as an early indicator of more serious structural issues. Manual crack identification is often time-consuming, subjective, and lacks consistency. This study develops an automated identification system based on computer vision using the YOLOv8 architecture, integrated with Internet of Things (IoT) technology through the ESP32-CAM device. The system is designed to visually detect and classify wall damage into light, moderate, or severe categories based on field-captured images. The model was trained and evaluated using the confusion matrix metric to assess its classification performance. The test results show that the system achieved a solid performance with an mAP@50 score of 0.822 and a stricter mAP@50-95 score of 0.522, indicating the system’s strong capability in detecting damage objects with a good level of precision. The implementation of this system is expected to support building inspection processes in a more standardized, objective, and sustainable manner, and assist in decision-making regarding building maintenance and repair.
Co-Authors Agung Gumelar Al-Basori, Muhamad Cepnur Alhidamkara, Salman Alun Sujjada Angelina, Nadila Apriliyanti, Silvi Ayu Lestari, Melani Bila, Nida Aulia Salsa Dewi, Dhita Diana Fatalifi, Amerjid Ghulamson Fergina, Anggun Firdaus, Asep Rizki Gunawan, Muhamad Sahrul Hafidzh Al Muhiddin, Muhammad Ibnu Sina, Rivaldi Insany, Gina Purnama Kamdan Khairiyah Ivanka, Najwa Kustandi, Agus Mamun, Yuliana Maulana Yusuf Muhammad Dzulqarnain, Fajar Munandar, Rifki Arief Natanael, Brillian Aji Natasya, Sandra Pauzi, Yoviar Perkasa, M. Juang Pajri Prayoga , Rizki Haddi Purnama Insany, Gina Ramdan, Hari Muhammad Rohmat, Satria Rizki Rosmawati Rosmawati Sebayang, Alvin Christoper Septiani, Dhea Ayu Serliani, Siska Meyisa Amelia Somantri, S Somantri, Somantri Sukmawan, Dede Sulaeman, Asep Surahman Tresna Ati, Mutiara Annisa Yasin, Fakhriyal Riyandi Yustiana, Indra Zahra, Falya Amrina Zaki Hibatullah, Muhammad