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Fajar Zulkarnain, Andry
Universitas Lambung Mangkurat
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Peningkatan Kompetensi Siswa SMK di Bidang Computer Vision dengan Implementasi YOLO dan Raspberry Pi 4 Arief Trisno Eko Suryo; Akhmad Ghiffary Budianto; Andry Fajar Zulkarnain; Gunawan Rudi Cahyono; Rusilawati Rusilawati; Bayu Setyo Wibowo; Marcfiliadi Ezra Nugroho; Fridho Ery Dwi Atmadja; Feby Zulviana Efendi
Indonesian Journal for Social Responsibility Vol. 8 No. 01 (2026): June 2026
Publisher : LPkM Universitas Bakrie

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36782/ijsr.v8i01.543

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.
Co-Authors Abdul Aziz Akbar, Jamaluddin Muhammad Akhmad Ghiffary Budianto Andry Alamsyah Atmadja, Fridho Ery Dwi Awaliyah, Aisyah Az-Zahra, Siti Fathimah Az-zahra, Siti Fatimah Bayu Setyo Wibowo Fakhri, Gibrael Muhammad Feby Zulviana Efendi Fridho Ery Dwi Atmadja Griffani Megiyanto Rahmatullah Gunawan Rudi Cahyono Heryani, Hesty Husnul Khatimi Iqbal Rizqi, Muhammad M. Reza Hidayat Mahrudin Mahrudin Marcfiliadi Ezra Nugroho Martunus Martunus Maulida, Muti'a Muhammad Alkaff Muhammad Nizar Ramadhan Muhammad Rizqy Alfarisi Mutia Maulida Nadiya Hijriana Najua, Siti Nur Nandang Eko Yulianto Noor Ridha Yanti Nurul Fathanah Mustamin Rohim, Muhammad Nabil Rusilawati Rusilawati Shafriani, Karimal Arum Suryo, Arief Trisno Eko Triana Rifkah Utomo Utomo Wibowo, Bayu Setyo Wijaya, Eka Setya Yanti, Noor Ridha Yanuary Yulistian Putra