Peningkatan Kompetensi Siswa SMK di Bidang Computer Vision dengan Implementasi YOLO dan Raspberry Pi 4

Authors

  • Arief Trisno Eko Suryo Universitas Lambung Mangkurat
  • Akhmad Ghiffary Budianto Universitas Lambung Mangkurat
  • Andry Fajar Zulkarnain Universitas Lambung Mangkurat
  • Gunawan Rudi Cahyono Universitas Lambung Mangkurat
  • Rusilawati Universitas Lambung Mangkurat
  • Bayu Setyo Wibowo Universitas Lambung Mangkurat
  • Marcfiliadi Ezra Nugroho Universitas Lambung Mangkurat
  • Fridho Ery Dwi Atmadja Universitas Lambung Mangkurat
  • Feby Zulviana Efendi Universitas Lambung Mangkurat

DOI:

https://doi.org/10.36782/ijsr.v8i01.543

Keywords:

Computer Vision, Digital Literacy, Object Detection, OpenCV, Raspberry Pi 4

Abstract

The rapid development of Artificial Intelligence (AI) technology up to 2025 has positioned Computer Vision (CV) as a crucial field in industrial applications, increasing the demand for competent graduates. Vocational High Schools (SMKs) are intended to prepare students for high employability; however, a situational analysis conducted at SMK Telkom Banjarbaru, South Kalimantan, Indonesia, revealed a gap in students’ understanding and practical application of CV technologies caused by limited learning resources and inadequate curriculum integration. The Community Service Program (Pengabdian kepada Masyarakat, PkM) of the Electrical Engineering Department aimed to introduce fundamental CV concepts to enhance students’ competencies and support digital literacy initiatives. The program employed a project-based training approach, combining theoretical sessions with practical demonstrations of a real-time face detection system using Raspberry Pi 4, OpenCV, and YOLO. The effectiveness of the program was evaluated through pre- and post-assessment surveys involving 30 participants (28 students and 2 supervising teachers). The results demonstrated successful implementation of an object detection system capable of detecting single and multiple faces with accuracy approaching 1.00 (100%). Survey findings indicated an increase in participants’ understanding of CV and digital literacy from 57% to 85%. Students’ comprehension of the difference between object classification and object detection improved from 64% to 89%, while their understanding of machine learning principles increased from 60% to 89%. Overall satisfaction with the program reached 89%. In conclusion, this community service program effectively bridged the competency gap and serves as a collaborative model between higher education institutions and vocational schools.

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Author Biographies

Arief Trisno Eko Suryo, Universitas Lambung Mangkurat

Program Studi Rekayasa Elektro

Akhmad Ghiffary Budianto, Universitas Lambung Mangkurat

Program Studi Rekayasa Elektro

Andry Fajar Zulkarnain, Universitas Lambung Mangkurat

Program Studi Rekayasa Elektro

Gunawan Rudi Cahyono, Universitas Lambung Mangkurat

Program Studi Rekayasa Elektro

Rusilawati, Universitas Lambung Mangkurat

Program Studi Rekayasa Elektro

Bayu Setyo Wibowo, Universitas Lambung Mangkurat

Program Studi Rekayasa Elektro

Marcfiliadi Ezra Nugroho, Universitas Lambung Mangkurat

Program Studi Rekayasa Elektro

Fridho Ery Dwi Atmadja, Universitas Lambung Mangkurat

Program Studi Rekayasa Elektro

Feby Zulviana Efendi, Universitas Lambung Mangkurat

Program Studi Rekayasa Elektro

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Published

2026-01-20

How to Cite

Suryo, A. T. E., Budianto, A. G., Zulkarnain, A. F., Cahyono, G. R., Rusilawati, R., Wibowo, B. S., … Efendi, F. Z. (2026). Peningkatan Kompetensi Siswa SMK di Bidang Computer Vision dengan Implementasi YOLO dan Raspberry Pi 4. Indonesian Journal for Social Responsibility, 8(01), 73–87. https://doi.org/10.36782/ijsr.v8i01.543

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