DOI: https://doi.org/10.14710/jtsiskom.2022.14213

Sistem Penghitung Jumlah Orang Menggunakan Metode SSD-MobileNet dan Centroid Tracking

1Departement of Informatic Engineering, Politeknik Negeri Semarang, Indonesia

2Informatic Engineering Politeknik Negeri Semarang, Indonesia

3Teknologi Rekayasa Komputer Politeknik Negeri Semarang, Indonesia

Received: 14 May 2021; Published: 30 Apr 2022.
Open Access Copyright (c) 2022 Afandi Nur Aziz Thohari, Aisyatul Karima, Angga Wahyu Wibowo, Kuwat Santoso
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Salah satu penerapan kecerdasan buatan untuk mencegah penyebaran virus corona adalah dengan membuat sistem penghitung jumlah orang otomatis untuk mencegah kerumunan di dalam ruangan. Penelitian ini membahas mengenai pembuatan prototipe sistem penghitung jumlah orang menggunakan algoritma deep learning pada single board computer. Tujuan dari penelitian ini adalah untuk menghitung jumlah orang dalam suatu ruangan agar okupansi ruangan dapat ditekan. Kontribusi dari penelitian ini adalah mengkombinasikan dua metode visi komputer yaitu SSD-MobileNet untuk identifikasi objek orang dan centroid tracking untuk menghitung jumlah orang. Berdasarkan pengujian yang telah dilakukan menunjukan bahwa sistem telah dapat menghitung objek orang dengan akurasi 100% apabila jumlah orang yang memasuki ruangan berjumlah satu, dua, atau tiga secara bersama-sama. Kemudian sistem dapat mendeteksi objek dengan jarak maksimal 10 meter dan intensitas cahaya redup atau kurang dari 100 lux. Pada pengujian komputasi menunjukan bahwa sistem dapat memproses video dengan jumlah frame 30 fps dan kualitas video high definition (HD).
Fulltext View|Download Email colleagues
Keywords: deteksi objek, menghitung jumlah orang pelacakan objek, prototipe

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Most viewed articles
Decision Support System for Thesis Graduation Recommendation Using AHP-TOPSIS Method Perancangan Game Math Adventure Sebagai Media Pembelajaran Matematika Berbasis Android Perancangan Sistem Informasi Akademik Berbasis Mobile Web Studi Kasus di Program Studi Sistem Komputer Universitas Diponegoro Pembuatan Aplikasi Antar-Jemput Laundry Berbasis Web Service pada Platform Android Pengembangan Sistem Pakar Untuk Diagnosis Penyakit Hepatitis Berbasis Web Menggunakan Metode Certainty Factor More articles
Most cited articles
Pembuatan Aplikasi Panduan Gizi Seimbang Berbasis Android Dengan Menggunakan Metode Backward Chaining Identification of Herbal Medicinal Plants Based on Leaf Image Using Gray Level Co-occurence Matrix and K-Nearest Neighbor Algorithms Pengembangan Sistem Pakar Untuk Diagnosis Penyakit Hepatitis Berbasis Web Menggunakan Metode Certainty Factor Pengembangan Sistem Informasi Rekam Medis untuk Dinas Kabupaten Grobogan Desain dan Implementasi Sistem Pembelajaran Jarak Jauh Di Program Studi Sistem Komputer More cited articles
  1. R. Girasa, “AI as a Disruptive Technology,” in Artificial Intelligence as a Disruptive Technology, Palgrave Macmillan, Cham, 2020, pp. 3–21
  2. Kemendagri, Penegakan Protokol Kesehatan Untuk Pengendalian Penyebaran Corona Virus Disease 2019. Indonesia: Kementerian Dalam Negeri Republik Indonesia, 2020, pp. 1–5
  3. F. D. Adhinata, M. Ikhsan, and W. Wahyono, “People counter on CCTV video using histogram of oriented gradient and Kalman filter methods,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 3, pp. 222–227, 2020, doi: 10.14710/jtsiskom.2020.13660
  4. D. Y. Setiawan, H. Fitriyah, and I. Arwani, “Sistem Penghitung Jumlah Orang Melewati Pintu Menggunakan Metode Background Subtraction Berbasis Raspberry Pi,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 2, pp. 2105–2113, 2019
  5. A. K. Mahamad, S. Saon, H. Hashim, M. A. Ahmadon, and S. Yamaguchi, “Cloud-based people counter,” Bulletin Electrical Engineering and Informatics, vol. 9, no. 1, pp. 284–291, 2020, doi: 10.11591/eei.v9i1.1849
  6. T. Parthornratt, N. Burapanonte, and W. Gunjarueg, “People identification and counting system using raspberry Pi (AU-PiCC: Raspberry Pi customer counter),” in International Conference on Electronics, Information, and Communication (ICEIC), Da Nang, Vietman, Jan. 2016 doi: 10.1109/ELINFOCOM.2016.7563020
  7. T. Y. Chen, C. H. Chen, D. J. Wang, and Y. L. Kuo, “A people counting system based on face-detection,” in 4th International Conference on Genetic and Evolutionary Computing (ICGEC), Dec. 2010, pp. 699–702, doi: 10.1109/ICGEC.2010.178
  8. D. W. Nugraha, Y. Anshori, and N. K. Candriasih, “Rancang Bangun Sistem Penghitung Jumlah Pengunjung Perpustakaan Menggunakan Metode Haar like Features (Studi Kasus Pada Perpustakaan Universitas Tadulako),” ScientiCO : Computer Science and Informatics Journal., vol. 1, no. 1, p. 57, 2019, doi: 10.22487/j26204118.2018.v1.i1.11902
  9. W. Liu et al., “SSD: Single shot multibox detector,” Lecturer Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence Lecture Notes in Bioinformatics), vol. 9905 LNCS, pp. 21–37, 2016, doi: 10.1007/978-3-319-46448-0_2
  10. J. C. Nascimento, A. J. Abrantes, and J. S. Marques, “Algorithm for centroid-based tracking of moving objects,” in International Conference on Acoustics, Speech and Signal Processing (ICASSP), August. 1999, vol. 6, no. 1, pp. 3305–3308, doi: 10.1109/icassp.1999.757548
  11. A. A. Suzen, B. Duman, and B. Sen, “Benchmark Analysis of Jetson TX2, Jetson Nano and Raspberry PI using Deep-CNN,” in 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Jun. 2020, pp. 3–7, doi: 10.1109/HORA49412.2020.9152915
  12. J. A. Kim, J. Y. Sung, and S. H. Park, “Comparison of Faster-RCNN, YOLO, and SSD for Real-Time Vehicle Type Recognition,” in International Conference on Consumer Electronics (ICCE), Sep. 2020, pp. 8–11, doi: 10.1109/ICCE-Asia49877.2020.9277040
  13. E. Cengil, A. Çinar, and Z. Güler, “A GPU-based convolutional neural network approach for image classification,” in International Artificial Intelligence and Data Processing Symposium (IDAP), Sep. 2017, doi: 10.1109/IDAP.2017.8090194
  14. A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” in International Conference on Learning Representations, April. 2017, pp. 1–9, [Online]. Available: http://arxiv.org/abs/1704.04861
  15. D. Sinha and M. El-Sharkawy, “Thin MobileNet: An Enhanced MobileNet Architecture,” in 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), Oct. 2019, pp. 0280–0285, doi: 10.1109/UEMCON47517.2019.8993089
  16. H.-T. Kim, Gyu-yeong; Kim, Jae-Ho; Park, Jang-Sik ; Yu, Yun-Sik ; Kim, “Vehicle Tracking using Euclidean Distance,” Journal Korea Institute Electronic Communication Sciences, vol. 7, no. 6, pp. 1293–1299, 2012, doi : https://doi.org/10.13067/JKIECS.2012.7.6.1293
  17. S. N. Jyothi and K. V. Vardhan, “Design and implementation of real time security surveillance system using IoT,” in International Conference on Communication and Electronics Systems (ICCES), Oct. 2016, doi: 10.1109/CESYS.2016.7890003
  18. D. Bader, “Schedule,” readthedocs.io, 2020. https://schedule.readthedocs.io/en/stable/ (accessed May 12, 2021)
  19. Y. Li, H. Huang, Q. Xie, L. Yao, and Q. Chen, “Research on a surface defect detection algorithm based on MobileNet-SSD,” Applied Sciences Journal, vol. 8, no. 9, 2018, doi: 10.3390/app8091678
  20. David L. DiLaura, An Introduction to The IES Lighting Handbook 10 th. Forgotten Books, 2017
  21. N. N. Putri, “Aplikasi Pendeteksi Objek bergerak pada Image Sequence Dengan Metode Background Substraction,” Jurnal Teknologi Rekayasa., vol. 21, no. 3, pp. 162–172, 2016
  22. R. D. Ramadhani, A. N. A. Thohari, and N. A. Nugraha, “Sistem Keamanan Ruangan Berbasis Internet of Things Menggunakan Single Board Computer,” Jurnal Nasional Informatika dan Teknologi Jaringan, vol. 1, no. 2, pp. 0–5, 2020, doi: https://doi.org/10.30743/infotekjar.v4i2.2338
  23. E. Rohadi et al., “Internet of Things: CCTV Monitoring by Using Raspberry Pi,” in the first International Conference on Applied Science and Technology (iCAST), 2018, pp. 454–457, doi: 10.1109/iCAST1.2018.8751612

Last update:

No citation recorded.

Last update: 2024-11-02 22:32:48

No citation recorded.