MUNTHE, BERTRAND RIZKY (2026) IMPLEMENTASI COMPUTER VISION UNTUK SISTEM SMART PARKING BERBASIS RASPBERRY AUTOMATIC NUMBER PLATE RECOGNITION. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The increasing number of vehicles in urban areas has led to various parking management problems, such as long queues, manual recording errors, and inefficient use of parking spaces. This research aims to design and implement a smart parking system based on computer vision using Automatic Number Plate Recognition (ANPR) with Raspberry Pi as the main control unit. The proposed system integrates vehicle license plate recognition, parking slot availability detection, automatic gate control, and cashless payment using QRIS. This study employs an experimental research method with a prototype development approach. A hybrid ANPR method is applied, combining Haar Cascade for local license plate detection and the Plate Recognizer API as a cloud-based OCR solution. Parking slot detection is performed using HC-SR04 ultrasonic sensors, while vehicle data are stored in a local SQLite database. System testing is conducted in an indoor environment under various scenarios to evaluate accuracy and response time. The experimental results indicate that the system achieves a character-level recognition accuracy of 93.8% with an average system response time of 7.81 seconds. The parking slot detection system also demonstrates a high accuracy of 94%. It is expected that this research can provide an efficient, cost-effective smart parking solution and serve as a foundation for further development in larger-scale parking environments. Keywords: Smart Parking, ANPR, Computer Vision, Raspberry Pi, QRIS. Seiring meningkatnya jumlah kendaraan di kawasan perkotaan, sistem parkir konvensional sering menimbulkan permasalahan berupa antrean panjang, kesalahan pencatatan, serta rendahnya efisiensi pemanfaatan lahan parkir. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem smart parking berbasis computer vision dengan teknologi Automatic Number Plate Recognition (ANPR) menggunakan Raspberry Pi sebagai pengendali utama. Sistem ini mengintegrasikan deteksi plat nomor kendaraan, pendeteksian ketersediaan slot parkir, kontrol palang parkir otomatis, serta pembayaran non-tunai berbasis QRIS. Metode penelitian yang digunakan adalah metode eksperimental dengan pendekatan pengembangan prototipe. Sistem ANPR diterapkan menggunakan pendekatan hibrida, yaitu Haar Cascade untuk deteksi plat nomor secara lokal dan Plate Recognizer API sebagai OCR berbasis cloud. Deteksi slot parkir dilakukan menggunakan sensor ultrasonik HC-SR04, sedangkan data kendaraan disimpan pada basis data lokal SQLite. Pengujian dilakukan pada lingkungan indoor dengan berbagai skenario pengujian akurasi dan waktu respons. Hasil penelitian menunjukkan bahwa sistem mampu mengenali karakter plat nomor dengan akurasi sebesar 93,8% dan memiliki waktu respons rata-rata sebesar 7,81 detik. Sistem juga berhasil mendeteksi ketersediaan slot parkir dengan akurasi 94%. Diharapkan penelitian ini dapat menjadi solusi parkir otomatis yang efisien, ekonomis, dan dapat dikembangkan untuk skala parkir yang lebih besar di masa mendatang. Kata kunci: Smart Parking, ANPR, Computer Vision, Raspberry Pi, QRIS.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41421110029 |
| Uncontrolled Keywords: | Smart Parking, ANPR, Computer Vision, Raspberry Pi, QRIS. |
| Subjects: | 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 621 Applied Physics/Fisika terapan > 621.3 Electrical Engineering, Lighting, Superconductivity, Magnetic Engineering, Applied Optics, Paraphotic Technology, Electronics Communications Engineering, Computers/Teknik Elektro, Pencahayaan, Superkonduktivitas, Teknik Magnetik, Optik Terapan, Tekn 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 629 Other Branches of Engineering/Cabang Teknik Lainnya > 629.8 Automatic Control Engineering/Teknik Kontrol Otomatis |
| Divisions: | Fakultas Teknik > Teknik Mesin |
| Depositing User: | khalimah |
| Date Deposited: | 27 Feb 2026 07:36 |
| Last Modified: | 27 Feb 2026 07:36 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/101235 |
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