Deteksi Objek menggunakan Deep Learning untuk Mengetahui Tingkat Kerumunan Mahasiswa

Nur Wakhidah, Prind Triajeng Pungkasanti, Agusta Praba Ristadi Pinem

Abstract


Penyebaran Covid 19 varian Delta di Kota Semarang pada bulan Juni – November 2021 mengakibatkan terjadinya kembali lonjakan. Hal ini menjadi pertimbangan Dinas Kesehatan terkait pembelajaran tatap muka (PTM) terbatas. Universitas Semarang (USM) yang memiliki mahasiswa terbanyak di Kota Semarang dengan jumlah mahasiswa terdaftar sejumlah 21644. Banyaknya mahasiswa yang dimiliki USM akan sangat rentan terjadinya pelanggaran protokol kesehatan dilingkungan universitas seperti adanya kerumunan mahasiswa. Salah satu yang dapat dilakukan dalam pencegahan yaitu deteksi objek untuk menentukan kerumunan menggunakan teknologi deep learning. Penerapan deep learning pada model pendeteksi objek menggunakan metode Convolutional Neural Network (CNN) berfungsi untuk melakukan ekstraksi fitur ciri objek yang tertangkap kamera, lalu akan disimpan sebagai fitur ciri objek. Setelah fitur disimpan, model melakukan pendeteksian dan menghitung banyaknya objek pada citra yang ditangkap untuk menentukan tingkat kerumunan mahasiswa. Model yang dibangun secara keseluruhan memiliki F1-Score 0.91 yang berarti kegagalan False Negative maupun False Positive tidak berbeda jauh. Model deteksi ini mampu melakukan penghitungan obyek manusia dengan MAPE 17% dan RMSE 2.68.

Keywords


Covid; Kerumunan; Deep Learning; Convolutional Neural Network

Full Text:

PDF

References


K. Zhang, W. Wang, Z. Lv, Y. Fan, and Y. Song, “Computer vision detection of foreign objects in coal processing using attention CNN,” Engineering Applications of Artificial Intelligence, vol. 102, p. 104242, Jun. 2021, doi: 10.1016/j.engappai.2021.104242.

M. Segal-Rozenhaimer, A. Li, K. Das, and V. Chirayath, “Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN),” Remote Sensing of Environment, vol. 237, p. 111446, Feb. 2020, doi: 10.1016/j.rse.2019.111446.

H. Kim, W.-K. Jung, Y.-C. Park, J.-W. Lee, and S.-H. Ahn, “Broken stitch detection method for sewing operation using CNN feature map and image-processing techniques,” Expert Systems with Applications, vol. 188, p. 116014, Feb. 2022, doi: 10.1016/j.eswa.2021.116014.

P. Sharma, Y. P. S. Berwal, and W. Ghai, “Performance analysis of deep learning CNN models for disease detection in plants using image segmentation,” Information Processing in Agriculture, vol. 7, no. 4, pp. 566–574, Dec. 2020, doi: 10.1016/j.inpa.2019.11.001.

V. Gonzalez-Huitron, J. A. León-Borges, A. E. Rodriguez-Mata, L. E. Amabilis-Sosa, B. Ramírez-Pereda, and H. Rodriguez, “Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4,” Computers and Electronics in Agriculture, vol. 181, p. 105951, Feb. 2021, doi: 10.1016/j.compag.2020.105951.

P. J. Hennessy, T. J. Esau, A. W. Schumann, Q. U. Zaman, K. W. Corscadden, and A. A. Farooque, “Evaluation of cameras and image distance for CNN-based weed detection in wild blueberry,” Smart Agricultural Technology, vol. 2, p. 100030, Dec. 2022, doi: 10.1016/j.atech.2021.100030.

M. A. J. Asshiddiqie, B. Rahmat, and F. T. Anggraeny, “MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK,” 2020.

S. Parvathi and S. Tamil Selvi, “Detection of maturity stages of coconuts in complex background using Faster R-CNN model,” Biosystems Engineering, vol. 202, pp. 119–132, Feb. 2021, doi: 10.1016/j.biosystemseng.2020.12.002.

N. J. Hussein, “A quantitative segmentation and classification brain bleeding injury detection using CNN,” Materials Today: Proceedings, p. S2214785321043455, Jun. 2021, doi: 10.1016/j.matpr.2021.06.012.

W. Yang, Z. Li, C. Wang, and J. Li, “A multi-task Faster R-CNN method for 3D vehicle detection based on a single image,” Applied Soft Computing, vol. 95, p. 106533, Oct. 2020, doi: 10.1016/j.asoc.2020.106533.

F. Nawab, A. S. Abd Hamid, A. Alwaeli, M. Arif, M. F. Fauzan, and A. Ibrahim, “Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan,” Sustainability, vol. 14, no. 13, p. 7945, Jun. 2022, doi: 10.3390/su14137945.

S. I. Cho and S.-J. Kang, “Real-Time People Counting System for Customer Movement Analysis,” IEEE Access, vol. 6, pp. 55264–55272, 2018, doi: 10.1109/ACCESS.2018.2872684.

D. Putra, Pengolahan citra digital. Andi, 2010.

L. A. Mushawwir and I. Supriana, “Deteksi dan Tracking Objek untuk Sistem Pengawasan Citra Bergerak”.

Y. S. Park and S. Lek, “Artificial Neural Networks: Multilayer Perceptron for Ecological Modeling In,” Developments in environmental modelling, vol. 28, pp. 123–140, 2016.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539.

G. Kaur et al., “Face mask recognition system using CNN model,” Neuroscience Informatics, vol. 2, no. 3, p. 100035, Sep. 2022, doi: 10.1016/j.neuri.2021.100035.

A. Newell, K. Yang, and J. Deng, “Stacked Hourglass Networks for Human Pose Estimation.” arXiv, Jul. 26, 2016. Accessed: Nov. 28, 2022. [Online]. Available: http://arxiv.org/abs/1603.06937




DOI: http://dx.doi.org/10.26418/jp.v9i3.70132

Refbacks

  • There are currently no refbacks.