Perancangan Sistem Deteksi Wajah Real-Time Menggunakan Convolutional Neural Network pada Perangkat Komputer Desktop
Article Sidebar
📅
Published:
Sep 10, 2026
Main Article Content
Abstract
Article Summary
This research aims to design and implement a real-time face detection system use a Convolutional Neural Network (CNN) on a desktop computer. The research consisted of data preprocesing, collection, model training, and performance evaluation. The dataset contained 200 facial images, which were resized to 224 ×is 224 pixels, normalized, and divided into training and testing sets. The model was developed using TensorFlow, Keras, and OpenCV, and evaluated use confusion matrix based on precision, accuracy, recall, and F1-score. The research results is that the proposed model can detect and recognize faces effectively, as demonstrated by an accuracy value of 97.22%, along with recall, precision, and F1-score values of 97%. The implementation of Batch Normalization and Dropout improved training stability and enhanced the model's generalization capability. These findings indicate that the CNN -based approach is effective for real-time face detection on desktop computer systems.
Keywords
Article Keywords
Downloads
Download data is not yet available.
Article Details
How to Cite
Pratama, V. R., Sasmoko, D., & Putra, T. W. A. (2026). Perancangan Sistem Deteksi Wajah Real-Time Menggunakan Convolutional Neural Network pada Perangkat Komputer Desktop. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 7(3), 388-400. https://doi.org/10.63447/jimik.v7i3.1982
Section
Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
License: All articles are licensed under 
References
Adliansyah, F. N., Holilah, H., & Krisdianto, N. (2025). Sistem absensi berbasis face recognition dengan model Inception-ResNet. RIGGS: Journal of Artificial Intelligence and Digital Business, 4(4), 2111–2118. https://doi.org/10.31004/riggs.v4i4.3545.
Aminullah, M. (2021). Alat deteksi masker dengan metode convolutional neural network untuk tunanetra pada era new normal. SinarFe7, 1–10.
Anwar, K. (2025). Sistem deteksi wajah berbasis deep learning menggunakan convolutional neural network (CNN). Journal of Computer Science and Information Technology, 1(2), 46–52. https://doi.org/10.70716/jocsit.v1i2.258.
Ardiansyah, I., Huda, F. Al, & Yudistira, N. (2025). Pengembangan aplikasi mobile klasifikasi ekspresi wajah manusia pada platform Android menggunakan arsitektur MobileNet. Jurnal Pengembangan Teknologi dan Ilmu Komputer, 9(1), 1–10.
Christyanto, N. E., Jonemaro, E. M. A., & Yudistira, N. (2022). Pengembangan aplikasi Android presensi kehadiran real-time menggunakan pengenalan wajah dengan model Facenet. Jurnal Pengembangan Teknologi dan Ilmu Komputer, 6(10), 4839–4847.
Goyal, H., Sidana, K., Singh, C., Jain, A., & Jindal, S. (2022). A real-time face mask detection system using convolutional neural network. Multimedia Tools and Applications, 81(11), 14999–15015. https://doi.org/10.1007/s11042-022-12166-x.
Hangaragi, S., Singh, T., & N, N. (2023). Face detection and recognition using face mesh and deep neural network. Procedia Computer Science, 218, 741–749. https://doi.org/10.1016/j.procs.2023.01.054.
Heri, H., Hartomi, Z. H., Ordila, R., & Irawan, Y. (2025). Integration of machine learning models random forest and XGBoost for credit card fraud detection in a Python Flask-based application. Jurnal Teknologi dan Open Source, 8(2), 811–825. https://doi.org/10.36378/jtos.v8i2.4821.
Husna, I. N., Ulum, M., Saputro, A. K., Haryanto, D., Laksono, D. T., & Purnamasari, D. N. (2022). Rancang bangun sistem deteksi dan perhitungan jumlah orang menggunakan metode convolutional neural network (CNN). SinarFe7, 1–10.
Karseno, D., Yuhandri, & Ramadhanu, A. (2024). Penerapan algoritma Haar cascade classifier dan computer neural network sebagai presensi karyawan. Jurnal Kometika, 11(4), 398–408. https://doi.org/10.35134/komtekinfo.v12i1.565.
Khana, R., Saputra, A. E., & Sobirin, M. (2024). Implementasi sistem presensi pada kegiatan seminar dengan pengenalan wajah menggunakan metode YOLO v5. Jurnal Kajian Teknik Elektro, 9(1), 45–57. https://doi.org/10.52447/jkte.v9i1.7631.
Marinda, D. E., & Amin, I. H. Al. (2023). Implementasi metode convolutional neural network untuk deteksi penggunaan masker secara real-time. Jurnal Teknik Informatika UNIKA Santo Thomas, 8(1), 81–91.
Maulana, H. K. (2025). Penerapan arsitektur CNN-EfficientNetB2 dengan transfer learning pada klasifikasi gambar tokoh wayang kulit. Jurnal Informatika dan Teknik Elektro Terapan, 13(1), 1–12. https://doi.org/10.23960/jitet.v13i1.5626.
Nabila, M., Idmayanti, R., & Rahmayuni, I. (2021). Deteksi wajah bermasker menggunakan webcam dan AWS EC2 berbasis Raspberry Pi. JITSI: Jurnal Ilmiah Teknologi Sistem Informasi, 2(4), 124–133. https://doi.org/10.62527/jitsi.2.4.54.
Satwikayana, S., Wibowo, S. A., & Vendyansyah, N. (2021). Sistem presensi mahasiswa otomatis pada Zoom meeting menggunakan face recognition dengan metode convolutional neural network berbasis web. JATI (Jurnal Mahasiswa Teknik Informatika), 5(2), 785–793.
Ulhaq, M. R. D., Zaidan, M. A., & Firdaus, D. (2023). Pengenalan ekspresi wajah secara real-time menggunakan metode SSD MobileNet berbasis Android. Journal of Technology and Informatics (JoTI), 5(1), 48–52. https://doi.org/10.37802/joti.v5i1.387.
Aminullah, M. (2021). Alat deteksi masker dengan metode convolutional neural network untuk tunanetra pada era new normal. SinarFe7, 1–10.
Anwar, K. (2025). Sistem deteksi wajah berbasis deep learning menggunakan convolutional neural network (CNN). Journal of Computer Science and Information Technology, 1(2), 46–52. https://doi.org/10.70716/jocsit.v1i2.258.
Ardiansyah, I., Huda, F. Al, & Yudistira, N. (2025). Pengembangan aplikasi mobile klasifikasi ekspresi wajah manusia pada platform Android menggunakan arsitektur MobileNet. Jurnal Pengembangan Teknologi dan Ilmu Komputer, 9(1), 1–10.
Christyanto, N. E., Jonemaro, E. M. A., & Yudistira, N. (2022). Pengembangan aplikasi Android presensi kehadiran real-time menggunakan pengenalan wajah dengan model Facenet. Jurnal Pengembangan Teknologi dan Ilmu Komputer, 6(10), 4839–4847.
Goyal, H., Sidana, K., Singh, C., Jain, A., & Jindal, S. (2022). A real-time face mask detection system using convolutional neural network. Multimedia Tools and Applications, 81(11), 14999–15015. https://doi.org/10.1007/s11042-022-12166-x.
Hangaragi, S., Singh, T., & N, N. (2023). Face detection and recognition using face mesh and deep neural network. Procedia Computer Science, 218, 741–749. https://doi.org/10.1016/j.procs.2023.01.054.
Heri, H., Hartomi, Z. H., Ordila, R., & Irawan, Y. (2025). Integration of machine learning models random forest and XGBoost for credit card fraud detection in a Python Flask-based application. Jurnal Teknologi dan Open Source, 8(2), 811–825. https://doi.org/10.36378/jtos.v8i2.4821.
Husna, I. N., Ulum, M., Saputro, A. K., Haryanto, D., Laksono, D. T., & Purnamasari, D. N. (2022). Rancang bangun sistem deteksi dan perhitungan jumlah orang menggunakan metode convolutional neural network (CNN). SinarFe7, 1–10.
Karseno, D., Yuhandri, & Ramadhanu, A. (2024). Penerapan algoritma Haar cascade classifier dan computer neural network sebagai presensi karyawan. Jurnal Kometika, 11(4), 398–408. https://doi.org/10.35134/komtekinfo.v12i1.565.
Khana, R., Saputra, A. E., & Sobirin, M. (2024). Implementasi sistem presensi pada kegiatan seminar dengan pengenalan wajah menggunakan metode YOLO v5. Jurnal Kajian Teknik Elektro, 9(1), 45–57. https://doi.org/10.52447/jkte.v9i1.7631.
Marinda, D. E., & Amin, I. H. Al. (2023). Implementasi metode convolutional neural network untuk deteksi penggunaan masker secara real-time. Jurnal Teknik Informatika UNIKA Santo Thomas, 8(1), 81–91.
Maulana, H. K. (2025). Penerapan arsitektur CNN-EfficientNetB2 dengan transfer learning pada klasifikasi gambar tokoh wayang kulit. Jurnal Informatika dan Teknik Elektro Terapan, 13(1), 1–12. https://doi.org/10.23960/jitet.v13i1.5626.
Nabila, M., Idmayanti, R., & Rahmayuni, I. (2021). Deteksi wajah bermasker menggunakan webcam dan AWS EC2 berbasis Raspberry Pi. JITSI: Jurnal Ilmiah Teknologi Sistem Informasi, 2(4), 124–133. https://doi.org/10.62527/jitsi.2.4.54.
Satwikayana, S., Wibowo, S. A., & Vendyansyah, N. (2021). Sistem presensi mahasiswa otomatis pada Zoom meeting menggunakan face recognition dengan metode convolutional neural network berbasis web. JATI (Jurnal Mahasiswa Teknik Informatika), 5(2), 785–793.
Ulhaq, M. R. D., Zaidan, M. A., & Firdaus, D. (2023). Pengenalan ekspresi wajah secara real-time menggunakan metode SSD MobileNet berbasis Android. Journal of Technology and Informatics (JoTI), 5(1), 48–52. https://doi.org/10.37802/joti.v5i1.387.