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Pemanfaatan Teknologi Internet of Things untuk Penunjang Model Pembelajaran Science, Technology, Engineering and Mathematics Budianto, Akhmad Ghiffary; Zulkarnain, Andry Fajar; Suryo, Arief Trisno Eko; Cahyono, Gunawan Rudi; Rusilawati, Rusilawati; Wibowo, Bayu Setyo; Az-Zahra, Siti Fathimah; Atmadja, Fridho Ery Dwi; Najua, Siti Nur
Indonesian Journal for Social Responsibility Vol. 7 No. 01 (2025): June 2025
Publisher : LPkM Universitas Bakrie

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36782/ijsr.v7i01.412

Abstract

The development of outstanding human resources can be achieved through the application of Science and Technology (S&T) in education. The focus of applying S&T is on the Science, Technology, Engineering, and Mathematics (STEM) learning approach. The STEM learning approach equips students with the ability to think creatively, critically, and problem-solving oriented, as well as to work collaboratively in teams, adapt to technological advancements, and continue to innovate. A lack of examples regarding the methods and applications of the latest technology in STEM fields poses a problem for students in schools. Students often struggle with understanding theories due to the absence of engaging demonstrative examples. The use of the Internet of Things (IoT) in various fields and daily life serves as an example of STEM application. Introducing IoT in several science subjects such as Physics, Chemistry, Biology, and Mathematics can provide engaging and relevant learning experiences. The goal of this Community Service activity is to provide an introduction and examples of IoT application as a supporting model for STEM learning at SMAN 1 Banjarbaru. The method used involves socializing IoT technology and including practical applications of IoT in STEM fields, such as assembling IoT kit components with WeMos D1, ESP32 NodeMCU, and various sensors for measuring temperature and pH. It is expected that students will understand the application of the latest IoT technology in STEM fields. Additionally, the school will be able to provide engaging, up-to-date learning experiences in technology that are relevant to the latest curriculum. As the result, three groups of students, each consisting of five members, successfully developed an IoT system for measuring liquid temperature and pH.
Peningkatan Kompetensi Siswa SMK di Bidang Computer Vision dengan Implementasi YOLO dan Raspberry Pi 4 Suryo, Arief Trisno Eko; Budianto, Akhmad Ghiffary; Zulkarnain, Andry Fajar; Cahyono, Gunawan Rudi; Rusilawati, Rusilawati; Wibowo, Bayu Setyo; Nugroho, Marcfiliadi Ezra; Atmadja, Fridho Ery Dwi; Efendi, Feby Zulviana
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.