Implementasi Metode YOLOv8 Mendeteksi Komputer Aktif dengan Subjek Layar Monitor
DOI:
https://doi.org/10.14421/jiska.2025.10.3.319-330Keywords:
Deep Learning, Computers, School Laboratory, YOLOv8, Monitor ScreenAbstract
Computers are one example of technological advances used in education. The use of computers that are not turned off can cause damage to computer components, and the use of electrical energy can increase. Student disobedience in turning off school laboratory computers when finished using them causes teachers to conduct manual checks by visiting each computer laboratory in the school. Deep learning is a machine learning algorithm that uses artificial neural networks. Deep learning is usually used for image recognition, voice identification, and data pattern analysis. Therefore, this study will apply the Deep Learning method, specifically YOLOv8, which aims to detect active computers based on the subject of the monitor screen and is expected to provide information about computers that are still active in the school laboratory. Based on the study's results, which detected 10 active computers, the 200-epoch model was selected with 100% accuracy at a speed of 2ms. Twenty active computers were selected, with 200 epoch models achieving 95% accuracy at a speed of 6ms per epoch. Thirty active computers were selected, with 100 epoch models achieving 96.67% accuracy at a speed of 3ms.
References
Alberto, J., & Hermanto, D. (2023). Bird Species Classification Using CNN Method and ResNet-50 Architecture. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 10(3), 34–36. https://doi.org/10.35957/jatisi.v10i3.4558
Alfarizi, D. N., Pangestu, R. A., Aditya, D., Setiawan, M. A., & Rosyani, P. (2023). Penggunaan Metode YOLO pada Deteksi Objek: Sebuah Tinjauan Literatur Sistematis. Jurnal Artificial Inteligent dan Sistem Penunjang Keputusan, 1(1), 54–63. https://jurnalmahasiswa.com/index.php/aidanspk/article/view/144
Ali, M. L., & Zhang, Z. (2024). The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection. Computers, 13(12), Article ID: 336. https://doi.org/10.3390/computers13120336
Amin, R. Al, & Obermaisser, R. (2025). Real-Time Object Detection and Classification Using YOLO for Edge FPGAs. ArXiv Preprint. http://arxiv.org/abs/2507.18174
Betti, A., & Tucci, M. (2023). YOLO-S: A Lightweight and Accurate YOLO-like Network for Small Target Detection in Aerial Imagery. Sensors, 23(4), Article ID: 1865. https://doi.org/10.3390/s23041865
Erbani, J., Portier, P.-É., Egyed-Zsigmond, E., & Nurbakova, D. (2024). Confusion Matrices: A Unified Theory. IEEE Access, 12, 181372–181419. https://doi.org/10.1109/ACCESS.2024.3507199
Gupta, C., Gill, N. S., Gulia, P., Kumar, A., Karamti, H., & Moges, D. M. (2025). An Optimized YOLO NAS Based Framework for Realtime Object Detection. Scientific Reports, 15(1), Article ID: 32903. https://doi.org/10.1038/s41598-025-17919-w
Hardi, N., & Sundari, J. (2023). Pengenalan Telapak Tangan Menggunakan Convolutionall Neural Network (CNN). Reputasi: Jurnal Rekayasa Perangkat Lunak, 4(1), 10–15. https://doi.org/10.31294/reputasi.v4i1.1951
Hutauruk, J. S. W., Matulatan, T., & Hayaty, N. (2020). Deteksi Kendaraan Secara Real Time Menggunakan Metode YOLO Berbasis Android. Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan, 9(1), 8–14. https://doi.org/10.31629/sustainable.v9i1.1401
Khairunnas, K., Yuniarno, E. M., & Zaini, A. (2021). Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot. Jurnal Teknik ITS, 10(1), A50–A55. https://doi.org/10.12962/j23373539.v10i1.61622
Laia, F. H., Rosnelly, R., Naswar, A., Buulolo, K., & Lase, M. C. (2023). Deteksi Pengenalan Wajah Orang Berbasis AI Computer Vision. Jurnal Teknologi Informasi Mura, 15(1), 62–72. https://jurnal.univbinainsan.ac.id/index.php/jti/article/view/2024
Luthfi, A. (2021). Pendeteksi Senjata Berbahaya pada Percobaan Tindakan Kriminal dengan Menggunakan Metode YOLO (You Only Look Once) [Universitas Islam Negeri Sultan Syarif Kasim Riau]. In Pekanbaru. https://repository.uin-suska.ac.id/41304/
Monalia, M., Asfiyanti, N. A., & Putri, S. E. (2022). Computers and Information Technology as a Source of Learning Media for Elementary School Teachers. International Journal of Natural Science and Engineering, 5(3), 96–103. https://doi.org/10.23887/ijnse.v5i3.41862
Mulyana, D. I., & Rofik, M. A. (2022). Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan di Indonesia Menggunakan Metode YOLOV5. Jurnal Pendidikan Tambusai, 6(3), 13971–13982. https://doi.org/10.31004/jptam.v6i3.4825
Rahayu, W. I., Prianto, C., & Novia, E. A. (2021). Perbandingan Algoritma K-Means dan Naïve Bayes untuk Memprediksi Prioritas Pembayaran Tagihan Rumah Sakit Berdasarkan Tingkat Kepentingan pada PT. Pertamina (Persero). Jurnal Teknik Informatika, 13(2), 1–8. https://ejurnal.ulbi.ac.id/index.php/informatika/article/view/1383
Raup, A., Ridwan, W., Khoeriyah, Y., Supiana, S., & Zaqiah, Q. Y. (2022). Deep Learning dan Penerapannya dalam Pembelajaran. JIIP - Jurnal Ilmiah Ilmu Pendidikan, 5(9), 3258–3267. https://doi.org/10.54371/jiip.v5i9.805
Romadloni, P., Adhi Kusuma, B. A., & Baihaqi, W. M. (2022). Komparasi Metode Pembelajaran Mesin untuk Implementasi Pengambilan Keputusan dalam Menentukan Promosi Jabatan Karyawan. JATI (Jurnal Mahasiswa Teknik Informatika), 6(2), 622–628. https://doi.org/10.36040/jati.v6i2.5238
Sadrawi, M., Fugaha, D. R., Heerlie, D. M., Lorell, J., Gautama, N. R. P., & Aminuddin, M. Z. (2023). Artificial Intelligence Based Brain Tumor Localization Using YOLOv5. Indonesian Journal of Life Sciences, 5(1), 1–9. https://doi.org/10.54250/ijls.v5i01.176
Saputra, D. H., Imran, B., & Juhartini. (2023). Object Detection untuk Mendeteksi Citra Buah-Buahan Menggunakan Metode YOLO. Jurnal Kecerdasan Buatan dan Teknologi Informasi, 2(2), 70–80. https://doi.org/10.69916/jkbti.v2i2.18
Yaseen, M. (2024). What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector. ArXiv Preprint. http://arxiv.org/abs/2408.15857
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