SISTEM PENGENALAN PELAKU PEMBUANG SAMPAH DENGAN METODE FACE RECOGNITION

Muhammad Fakhri Febriansyah

doi:10.23960/jitet.v14i3.10157

Muhammad Fakhri Febriansyah
Politeknik Negeri Sriwijaya

DOI: https://doi.org/10.23960/jitet.v14i3.10157

Keywords Face Recognition, Haar Cascade, YOLOv8, Confusion Matrix, Telegram, Pembuangan Sampah

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Abstract

Abstrak. Pembuangan sampah sembarangan di kawasan Sungai Sekanak masih menjadi permasalahan yang berdampak pada kualitas lingkungan. Penelitian ini bertujuan mengembangkan sistem pengenalan pelaku pembuangan sampah menggunakan metode face recognition berbasis Haar Cascade dan library face_recognition. Sistem memanfaatkan kamera untuk mendeteksi dan mengenali wajah pelaku secara real-time dengan membandingkannya terhadap database wajah yang berasal dari data KTP. Deteksi objek dilakukan menggunakan YOLOv8, sedangkan notifikasi identitas pelaku dan bukti gambar dikirim melalui Telegram. Kinerja sistem dievaluasi menggunakan confusion matrix. Hasil pengujian menunjukkan nilai precision sebesar 100%, recall sebesar 54,55%, dan accuracy sebesar 54,55%. Sistem yang dikembangkan mampu membantu meningkatkan efektivitas pengawasan terhadap aktivitas pembuangan sampah sembarangan.

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References

Kementerian Lingkungan Hidup dan Kehutanan, “Sistem Informasi Pengelolaan Sampah Nasional (SIPSN),” 2023.

Dinas Lingkungan Hidup Kota Palembang, “Laporan Pengelolaan Sampah Kota Palembang,” 2024.

Pemerintah Kota Palembang, “Laporan Kegiatan Pembersihan Sungai Sekanak,” 2023.

M. Bahit et al., “Validation of Haar Cascade Classification Method in Face Detection,” Journal of Information Technology and Engineering (JITE), 2023.

Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A Unified Embedding for Face Recognition and Clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Deng, J., Guo, J., Xue, N., & Zafeiriou, S., "ArcFace: Additive Angular Margin Loss for Deep Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 10, pp. 5962–5979, 2022.

Wang, M., & Deng, W., "Deep Face Recognition: A Survey," Neurocomputing, vol. 429, pp. 215–244, 2021.

Viola, P., & Jones, M. (2001). Rapid Object Detection Using a Boosted Cascade of Simple Features. Proceedings of CVPR.

Hashim, S., & McCullagh, P., "Face Detection by Using Haar Cascade Classifier," Wasit Journal of Computer and Mathematics Science, vol. 2, no. 1, pp. 1–8, 2023.

Nidom, M. S., "Haar Cascade Classifier and Adaboost Algorithm for Face Detection with the Viola-Jones Method," Transactions on Informatics and Data Science, vol. 2, no. 1, pp. 15–26, 2025.

Jocher, G., Chaurasia, A., & Qiu, J. (2024). Ultralytics YOLOv8. Ultralytics. Available: https://docs.ultralytics.com

Jocher, G., Chaurasia, A., & Qiu, J., Ultralytics YOLOv8 Documentation, Ultralytics, 2024.

Yisihak, H. M., & Li, L., "Advanced Face Detection with YOLOv8: Implementation and Integration into AI Modules," Open Access Library Journal, vol. 11, pp. 1–19, 2024.

A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd ed., Sebastopol, CA: O'Reilly Media, 2022.

R. Rinanto et al., “Deteksi dan Pengenalan Wajah Real-Time Menggunakan Metode Haar Cascade dan CNN,” Jurnal Riset Rekayasa Elektro, 2025.

Chicco, D., Tötsch, N., & Jurman, G., "The Matthews Correlation Coefficient (MCC) is More Reliable Than Balanced Accuracy, Bookmaker Informedness, and Markedness in Two-Class Confusion Matrix Evaluation," BioData Mining, vol. 14, no. 13, 2021.

Obi, J. C., "A Comparative Study of Several Classification Metrics and Their Performances on Data," World Journal of Advanced Engineering Technology and Sciences, vol. 8, no. 1, pp. 308–314, 2023.