| dc.contributor.advisor | Widodo, Bayu | |
| dc.contributor.author | Ridan, T. Fikri Nabil | |
| dc.date.accessioned | 2026-09-16T08:46:59Z | |
| dc.date.available | 2026-09-16T08:46:59Z | |
| dc.date.issued | 2026 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/179950 | |
| dc.description.abstract | Sistem pelaporan dan penanganan kerusakan fasilitas sering terkendala keterlambatan penyampaian informasi, ketidaktepatan dokumentasi, dan proses monitoring yang kurang efektif. Mekanisme komunikasi manual antara pengguna dan petugas menyebabkan respons menjadi lambat dan mengurangi efisiensi perbaikan. Untuk mengatasi masalah tersebut, penelitian ini mengusulkan pengembangan sistem pelaporan kerusakan berbasis Internet of Things (IoT) menggunakan ESP32, platform web, dan AI Assistant untuk analisis gambar kerusakan. Laporan dan foto kerusakan diunggah melalui aplikasi web dan ditampilkan sebagai notifikasi real-time pada perangkat. Saat notifikasi dibuka, sistem menampilkan detail laporan dan melakukan analisis visual menggunakan model vision AI untuk memberikan rekomendasi perbaikan awal. Evaluasi dilakukan melalui pengujian fungsi perangkat, validasi hasil analisis AI, dan penilaian
efektivitas notifikasi. Pengembangan sistem ini diharapkan mampu mempercepat waktu respons, meningkatkan akurasi dokumentasi, dan mendukung pengambilan keputusan teknis secara lebih optimal. | |
| dc.description.abstract | Facility damage reporting and handling systems often face issues such as delayed information delivery, inaccurate documentation, and ineffective monitoring processes. Manual communication between users and technicians slows response time and reduces repair efficiency. To address these challenges, this research proposes the development of an IoT-based facility damage reporting system using
the ESP32, a web platform, and an AI Assistant for image-based damage analysis. Damage reports and photos are submitted through a web application and displayed as real-time notifications on the device. When opened, the system presents report
details and performs automated visual analysis using a vision-based AI model to provide preliminary repair recommendations. System evaluation includes functional device testing, validation of AI analysis results, and assessment of notification effectiveness. The system is expected to improve response speed, enhance documentation accuracy, and support more effective technical decision-making. | |
| dc.description.sponsorship | | |
| dc.language.iso | id | |
| dc.publisher | IPB University | id |
| dc.title | Implementasi Smart Real-time Notification Assistant Terintegrasi Cloud Vision AI untuk Reduksi Response Time | id |
| dc.title.alternative | Implementation of a Smart Real-time Notification Assistant Integrated with Cloud Vision AI for Response Time Reduction | |
| dc.type | Tugas Akhir | |
| dc.subject.keyword | AI | id |
| dc.subject.keyword | ESP32 | id |
| dc.subject.keyword | IoT | id |
| dc.subject.keyword | model vision | id |
| dc.subject.keyword | sistem pelaporan | id |
| dc.subject.keyword | reporting system | id |
| dc.subtype | Undergraduate Theses | |
