Sistem Identifikasi Cerdas: Integrasi IOT dengan YOLOv8 Untuk Identifikasi Visual Kerusakan Dinding Bangunan
-
Kamdan Kamdan
*
Universitas Nusa Putra, Sukabumi,
Indonesia
- Somantri Somantri Universitas Nusa Putra, Sukabumi, Indonesia
- Satria Rizki Rohmat Universitas Nusa Putra, Sukabumi, Indonesia
- Agung Gumelar Universitas Nusa Putra, Sukabumi, Indonesia
- Ivana Lucia Kharisma Universitas Nusa Putra, Sukabumi, Indonesia (*) Corresponding Author
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.
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References
M. Putra, D. Mabui, and M. Wantoro, “Evaluasi Kinerja Bangunan Gedung Rektorat Universitas Yapis Papua Akibat Pengaruh Gempa Di Jayapura,” Cyclops J. Tek. Sipil dan Perenc., vol. 1, no. 2, pp. 63–74, 2023, doi: 10.55098/jtsp.v1i2.492.
D. G. Gutama and R. L. Rahayu, “Ketahanan Bangunan Rumah Sakit Terhadap Bencana Gempa Bumi Di Bantul Daerah Istimewa Yogyakarta,” Lakar J. Arsit., vol. 4, no. 2, p. 150, 2021, doi: 10.30998/lja.v4i2.10958.
S. S. Kelty Joria , Sheera Xaviera, Radha P. , Lidia B. Manalu , Erwin , Allan Jali, “Perumahan Marina Park,” JUTEKS - J. Tek. SIPIL, vol. V, no. I, pp. 11–19, 2020, doi:10.32511/juteks.v5i1.625
A. P. Wibowo, A. Adha, I. F. Kurniawan, and I. Laory, “Wall Crack Multiclass Classification: Expertise-Based Dataset Construction and Learning Algorithms Performance Comparison,” Buildings, vol. 12, no. 12, 2022, doi: 10.3390/buildings12122135.
Z. Jiang, J. I. Messner, and E. Matts, “Computer Vision Applications in Construction and Asset Management Phases: a Literature Review,” J. Inf. Technol. Constr., vol. 28, no. November 2022, pp. 176–199, 2023, doi: 10.36680/J.ITCON.2023.009.
Yudistira Bagus Pratama and Nurzaidah Putri Dalimunthe, “Implementasi Teknik Computer Vision Untuk Deteksi Viridiplantae Pada Lahan Pasca Tambang,” Bull. Comput. Sci. Res., vol. 3, no. 1, pp. 64–72, 2022, doi: 10.47065/bulletincsr.v3i1.193.
M. Sarosa and N. Muna, “Implementasi Algoritma You Only Look Once (YOLO) Untuk Deteksi Korban Bencana Alam,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 4, pp. 787–792, 2021, doi: 10.25126/jtiik.202184407.
S. Somantri, G. P. Insany, S. Olis, and K. Kamdan, “Perancangan Sistem Otomatisasi Pemberi Pakan Ikan Lele Berdasarkan Suhu Air Menggunakan Logika Fuzzy Sugeno,” J. Edukasi dan Penelit. Inform., vol. 9, no. 2, p. 289, 2023, doi: 10.26418/jp.v9i2.65823.
M. Q. Jannah, Z. Amalia, C. N. Asyifa, and M. Afifuddin, “Implementasi Algoritma Convolutional Neural Network Untuk Identifikasi Tingkat Kerusakan Struktur Bangunan Pasca Gempa Bumi,” Journal of The Civil Engineering Student, vol. 6, no. 4, pp. 212–221, 2024, doi: 10.24815/journalces.v6i4.30359
R. Soekarta, S. Aras, and A. M. Fitrah Pasaribu, “Deteksi Keretakan Permukaan Gedung Menggunakan Algoritma YOLO Berbasis Web,” Insect (Informatics Secur. J. Tek. Inform., vol. 11, no. 1, pp. 116–126, 2025, doi: 10.33506/insect.v11i1.4339.
S. Shomal Zadeh, S. Aalipour birgani, M. Khorshidi, and F. Kooban, “Concrete Surface Crack Detection with Convolutional-based Deep Learning Models,” SSRN Electron. J., vol. 10, no. 3, pp. 25–35, 2024, doi: 10.2139/ssrn.4661249.
W. S. N. Hassanah, Y. P. Lestari, and R. A. Saputra, “Digital Image Processing to Detect Cracks in Buildings Using Naïve Bayes Algorithm (Case Study: Faculty of Engineering, Halu Oleo University),” Telematika, vol. 20, no. 1, p. 1, 2023, doi: 10.31315/telematika.v20i1.8925.
D. S. Charismana, H. Retnawati, and H. N. S. Dhewantoro, “Motivasi Belajar Dan Prestasi Belajar Pada Mata Pelajaran Ppkn Di Indonesia: Kajian Analisis Meta,” Bhineka Tunggal Ika Kaji. Teor. dan Prakt. Pendidik. PKn, vol. 9, no. 2, pp. 99–113, 2022, doi: 10.36706/jbti.v9i2.18333.
A. Raimundo, “Crack Detection Computer Vision Project.” Access Date April 2025, Available: https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg
Tim BR, “Damage-detection Computer Vision Project.” Access Date April 2025, Available: https://universe.roboflow.com/tim-4ijf0/damage-detection-0otvb
Pexels, “No Defect Wall.” Access Date April 2025, Available: https://www.pexels.com/id-id/pencarian/Foto Dinding/
M. I. Hatta, F. Yanto, I. Afrianty, and L. Afriyanti, “Pengaruh Image Enhancement Contrast Stretching dalam Klasifikasi CT-Scan Tumor Ginjal Menggunakan Deep Learning,”Inovtek Polbeng-Seri Informatika vol. 9, no. 1, 2024 , pp. 408–419, 2024, doi:10.35314/isi.v9i1.4233
B. Khalili and A. W. Smyth, “SOD-YOLOv8 - Enhancing YOLOv8 for Small Object Detection in Traffic Scenes,” Sensors, vol. 24, no. 19, 2024. doi: 10.3390/s24196209.
Evidently AI Team, “Mean Average Precision (MAP) in ranking and recommendations.” [Online]. Available: https://www.evidentlyai.com/ranking-metrics/mean-average-precision-map
A. Efendi Noor and P. Irfan, “Implementasi Progressive Web Apps (PWA) Menggunakan (Implementation of Progressive Web Apps (PWA) Using Laravel and Vue.Js in Making Freelance Service Provider Applications),” JTIM : Jurnal Teknologi Informasi dan Multimedia, vol. 2, no. 3, pp. 174–180, 2020, doi:10.35746/jtim.v2i3.109
H. Saputra, K. Muchtar, N. Chitraningrum, A. Andria, and A. Febriana, “Performance evaluation of hyper-parameter tuning automation in YOLOV8 and YOLO-NAS for corn leaf disease detection,” Sinergi (Indonesia), vol. 29, no. 1, pp. 197–206, 2025, doi: 10.22441/sinergi.2025.1.018.
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Copyright (c) 2025 Kamdan Kamdan, Somantri Somantri, Satria Rizki Rohmat, Agung Gumelar, Ivana Lucia Kharisma
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