Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Teknik Elektro dan Komputer TRIAC Indonesian Journal for Social Responsibility
Bayu Setyo Wibowo
Universitas Lambung Mangkurat
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
Electrical Power Quality Analysis In Department Education Mechanical Engineering FT UNY Bayu Setyo Wibowo; Bagus Tri Kuncoro; Ridha Ulfia Rahmah
Jurnal Teknik Elektro dan Komputer TRIAC Vol 13, No 1 (2026): Mei 2026
Publisher : Jurusan Teknik Elektro Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The purpose of this research is to determine the electrical load profile and power quality of the power distribution system in the Department of Mechanical Engineering Education, Faculty of Engineering, Yogyakarta State University. The power quality study will provide recommendations for improving one of the electrical parameters, namely the power factor, and compare the measured electrical quantities with existing standards. This electrical power quality analysis was carried out starting with measurements on the electrical panels in the Department of Mechanical Engineering Education, the panels include Fabrication SDP, CNC and lathe machine SDP, welding machine SDP, lighting load and AC load using a power quality analyzer (A3Q) measuring instrument. The measurement data includes apparent power, active power, reactive power, current, voltage, frequency, Current THD, voltage THD, and power factor. The measurement data is then processed into data in the form of a comma-separated values file (csv) which will then be converted into excel data (xls). This conversion data will then be analyzed and then compared with the existing power quality standards. Based on the results of the measurements and analysis carried out, it shows that the overall power factor value on the SDP panel and all loads in the machine direction is still below 0.86 and there is a neutral current caused by an unbalanced load. The largest neutral current value is on the CNC panel, which is 21.46 amperes
Co-Authors Akhmad Ghiffary Budianto Bagus Tri Kuncoro Fajar Zulkarnain, Andry Feby Zulviana Efendi Fridho Ery Dwi Atmadja Gunawan Rudi Cahyono Marcfiliadi Ezra Nugroho Ridha Ulfia Rahmah Rusilawati Rusilawati Suryo, Arief Trisno Eko