Aplikasi Penghitung Kapasitas Ruang Parkir pada Lahan Parkir Kosong Menggunakan Library OpenCV pada Bahasa Pemrograman Python
In recent years, there have been many studies conducted with the aim of making it easier to determine vacant parking lots in a parking area. In this study, the authors discuss the creation of an application to calculate the availability of vacant parking lots using the Image Processing method. OpenCV is a library in the Python programming language that is needed during the Image Processing process. OpenCV will help to create applications related to images such as images or photos and videos. The methodology of the Image Processing and OpenCV processes, along with their descriptions, will be provided in this research. In this application, the technology used is video image processing through video recording taken from an overhead angle using a drone camera. The data needed by this application is a video recording of the car when entering or leaving the parking lot. Then the data will be processed through an engine, then the output generated from the application is in the form of an image file of a parking lot that is entering or leaving the parking lot in file.png format. The program will give the value of each object that is in the available parking space. If the Count < 900, it means that there are no large objects, namely cars, in the parking lot. If the count > 900, it means that there is a large object, namely a car, in the parking lot. In the trial phase using a sample in the form of a video with 10 respondents stating that 92.6% of this application could function according to its purpose and it was found that there were no problems in calculating the application and the application could run properly and as the purpose of making it, namely as a calculator for the availability of parking areas on a land open parking.
Afriliana, N., Rosalina, & Valeria, R. (2018). PENDETEKSIAN RUANG KOSONG PARKIR DI DALAM RUANGAN. Ultima Computing, 34-40.
Ashqer, M. I., & Bikdash, M. (2019). PARKING LOT SPACE DETECTION BASED ON IMAGE PROCESSING. Conference Proceeding IEEE SouthEastCon.
Firdaus, A., & Imelda. (2018). PENERAPAN METODE GAUSSIAN BLUR DAN ABSOLUTE DIFFERENCE PADA JUMLAH DAN KECEPATAN KENDARAAN. Skanika, 1003-1011.
Habibah, M. U., & Kurniawan, M. (2021). SEGMENTASI CITRA WAJAH DENGAN IMPLEMENTASI ADAPTIVE THRESHOLD-INTEGRAL IMAGE. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 919-928.
Hattale, P., Hangam, V., Khilare, S., Ratnaparkhi, a., & Kasture, P. (2021). PARKING SPACE DETECTION USING IMAGE PROCESSING. International Journal of Science and Research (JSR), 1440-1442.
Jupiyandi, S., Saniputra, F. R., Pratama, Y., & Dharmawan, M. R. (2019). PENGEMBANGAN DETEKSI CITRA MOBIL UNTUK MENGETAHUI JUMLAH TEMPAT PARKIR MENGGUNAKAN CUDA DAN MODIFIED YOLO. Jurnal Teknologi Informasi dan Ilmu Komputer (KTIIK), 413 - 419.
Kadir, A. (2019). LANGKAH MUDAH PEMROGRAMAN OPENCV DAN PYTHON. Jakarta: PT. ElexMedia Komputindo.
Kurniawan, S., & Sriharyani, L. (2018). ANALISIS PENGARUH PARKIR DI BADAN JALAN TERHADAP KINERJA JALAN JENDERAL AHMAD YANI KOTA METRO. Lampung: Tapak.
Listartha, I. M., Indrawan, G., & Pramartha, I. G. (2020). IOT - PARKING LOT DETECTION BASED ON IMAGE PROCESSING. Jurnal Sistem dan Informatika (JSI), 168-176.
Lopez, M., Griffin, T., Ellis, K., Enem, A., & Duhan, C. (2019). PARKING LOT OCCUPANCY TRACKING THROUGH IMAGE PROCESSING. Proceeding of 34th International Conference on Computers and Their Applications (pp. 265-270). Hawaii, USA: Epic Computing.
Nguyen, Q. (2019). HANDS-ON APPLICATION DEVELOPMENT WITH PYCHARM: ACCELERATE YOUR PYTHON APPLICATION USING PRACTICAL CODING TECHNIQUES IN PYCHARM. Birmingham: Packt Publishing.
Pratt, W. K. (2001). DIGITAL IMAGE PROCESSING: PIKS INSIDE, THIRD EDITION. California: John Wiley and Son, Inc.
Rizkatama, G. N., Nugroho, A., & Suni, A. F. (2021). SISTEM CERDAS PENGHITUNG JUMLAH MOBIL UNTUK MENGETAHUI KETERSEDIAAN LAHAN PARKIR BERBASIS PYTHON DAN YOLO v4. Edu Komputika Journal, Unnes, 91-99.
Rosebrock, A. (2016). PRACTICAL PYTHON AND OPENCV (3rd EDITION). PyImageSearch.
Solomon, C., & Breckon, T. (2011). FUNDAMENTAL OF DIGITAL IMAGE PROCESSING: A PRACTICAL APPROACH WITH EXAMPLE IN MATLAB. West Sussex: John Wily and Sons, Inc.
Sonka, M., Hlavac, V., & Boyle, R. (2015). IMAGE PROCESSING, ANALYSIS, AND MACHINE VISION FOURTH EDITION. USA: Cengage Learning.