PENDAMPINGAN PENINGKATAN KOMPETENSI PROFESIONAL GURU MELALUI UKG BERBASIS DEEP LEARNING TERINTEGRASI - Yohanes Mariano Dangku; Maximus Tamur; Henrikus Midun (Universitas Muhammadiyah Mataram, 2026)
Abstrak: Penguatan kompetensi profesional guru menjadi kebutuhan mendesak dalam menghadapi tuntutan pembelajaran abad ke-21 yang menekankan integrasi deep learning dan pemanfaatan teknologi digital. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan meningkatkan kompetensi profesional guru melalui UKG berbasis deep learning terintegrasi pada 40 guru Sekolah Menengah Atas. Metode yang digunakan adalah metode campuran yang memadukan pelatihan, pendampingan reflektif, dan diskusi kolaboratif. Program dirancang dengan pendekatan deskriptif kuantitatif melalui empat tahap, yaitu persiapan, pelaksanaan UKG, pendampingan reflektif, dan evaluasi. UKG digunakan sebagai instrumen pemetaan awal kompetensi, sedangkan pendampingan difokuskan pada penguatan praktik pembelajaran berbasis refleksi dan kolaborasi. Sistem evaluasi kegiatan dilakukan melalui analisis skor UKG dan angket respon peserta menggunakan skala Likert lima tingkat untuk mengukur persepsi terhadap kebermanfaatan program, peningkatan kompetensi, dan kesiapan implementasi deep learning. Indikator keberhasilan kegiatan ditetapkan berdasarkan capaian rerata skor minimal kategori baik (≥3,50) pada setiap dimensi kompetensi serta tingkat kepuasan peserta minimal 80%. Hasil evaluasi menunjukkan bahwa dimensi perencanaan pembelajaran (4,04), pelaksanaan pembelajaran (3,85), asesmen (4,15), dan refleksi (4,25) berada pada kategori baik. Sementara itu, dimensi adaptasi ICT memperoleh skor 3,05 dengan kategori cukup, yang menunjukkan perlunya penguatan integrasi teknologi dalam pembelajaran. Temuan ini mengindikasikan bahwa kompetensi pedagogis guru relatif kuat, namun pemanfaatan ICT belum optimal dalam mendukung pembelajaran berbasis deep learning. Kegiatan ini efektif sebagai model pendampingan berbasis UKG untuk pengembangan profesional guru secara berkelanjutan.Abstract: Strengthening teachers’ professional competence has become an urgent priority in responding to 21st-century learning demands, particularly those emphasizing the integration of deep learning and digital technology. This community service program aimed to enhance teachers’ professional competence through an integrated deep learning–based Teacher Competency Test (UKG) involving 40 senior high school teachers. The program employed a mixed-method approach combining training sessions, reflective mentoring, and collaborative discussions. It was designed using a descriptive quantitative framework implemented in four stages: preparation, UKG administration, reflective mentoring, and evaluation. The UKG functioned as an initial competency-mapping instrument, while mentoring activities focused on reinforcing reflective and collaborative instructional practices aligned with deep learning principles. The evaluation system consisted of UKG score analysis and a participant-response questionnaire using a five-point Likert scale to measure perceived program effectiveness, competency improvement, and readiness to implement deep learning. The indicators of success were defined as achieving a minimum average score in the “good” category (≥3.50) across competency dimensions and at least 80% participant satisfaction. The results revealed that lesson planning (4.04), instructional implementation (3.85), assessment practices (4.15), and reflective practice (4.25) were categorized as good. However, ICT adaptation scored 3.05, indicating a moderate level and the need for further strengthening in technology integration. These findings suggest that teachers’ pedagogical competence is relatively strong, yet ICT utilization remains suboptimal in supporting deep learning–based instruction. The program demonstrates effectiveness as a UKG-based mentoring model for sustainable professional development.