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All Journal Jurnal Pengabdian Masyarakat Journal of Vocational, Informatics and Computer Education
Ayu Lestari
Universitas Negeri Makassar, Indonesia
Humanities Computer Science & IT Education Electrical & Electronics Engineering Social Sciences

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Implementasi Sistem Absensi Otomatis Berbasis QR-Code dan ESP32 dengan Notifikasi WhatsApp di SMK Negeri 5 Gowa Fathahillah Fathahillah; Abdul Muis Mappalotteng; Dyah Darma Andayani; Kurnia Prima Putra; Ayu Lestari
Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2026): Jurnal Pengabdian Masyarakat (Abdimas)
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/abdimas.v4i1.12846

Abstract

SMK Negeri 5 Gowa sebagai mitra saat ini menerapkan sistem absensi manual berupa tanda tangan yang direkapitulasi ke dalam buku, sehingga proses rekapitulasi lambat, rentan kesalahan, dan tidak mampu menyajikan data kehadiran secara real-time. Kondisi ini diperberat oleh masih adanya siswa yang membolos serta keterbatasan informasi waktu kehadiran dan kepulangan siswa bagi orang tua. Kegiatan pengabdian kepada masyarakat ini bertujuan mengimplementasikan sistem absensi otomatis berbasis QR Code (Quick Response Code) dan mikrokontroler ESP32 dengan basis data MySQL sebagai hasil penelitian yang diterapkan secara nyata kepada masyarakat. Kegiatan dilaksanakan pada Februari–Maret 2026 melalui tahapan koordinasi dan persiapan, pemasangan dan penyiapan sistem, pengujian alat, pelatihan dan pendampingan, serta evaluasi. Hasil kegiatan menunjukkan sebanyak 880 pengguna berhasil didaftarkan, terdiri atas 58 guru dan 822 siswa, dengan dua unit perangkat terpasang pada gerbang dan lobi sekolah. Modul GM67 mampu membaca kartu hingga jarak 10 cm, sedangkan notifikasi kehadiran siswa diterima orang tua melalui WhatsApp dalam waktu rata-rata 35 detik. Evaluasi memperoleh rata-rata tanggapan positif sebesar 99%. Dengan demikian, sistem yang diimplementasikan mempercepat rekapitulasi kehadiran secara real-time, mendukung pemantauan kehadiran siswa, dan meningkatkan transparansi informasi kepada orang tua.
AI Assistant Use and Perceived Cognitive Skills in Programming: Extending the Technology Acceptance Model Fathahillah; Ayu Lestari; Muhammad Yahya; Muhammad Fardan; Putri Nanda Sari
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.990

Abstract

Purpose - This study investigates the relationship between students’ use of AI assistants and their perceived cognitive abilities in programming by extending the Technology Acceptance Model (TAM) to incorporate perceived Computational Thinking (CT) and perceived Problem-Solving Skills (PSS).Methods - A quantitative survey was administered to 320 undergraduate students with prior experience using AI assistants for programming-related tasks. The data were gathered through a four-point Likert-scale questionnaire and subsequently analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4.Findings - The results showed that Perceived Usefulness was positively associated with Behavioral Intention to Use (β = 0.329, p < 0.001), while Perceived Ease of Use was positively associated with Perceived Usefulness (β = 0.680, p < 0.001) and Behavioral Intention to Use (β = 0.346, p < 0.001). Behavioral Intention to Use was positively associated with Actual Use (β = 0.741, p < 0.001). Actual Use was also positively associated with perceived CT (β = 0.672, p < 0.001) and perceived PSS (β = 0.754, p < 0.001).Research Implications - The findings suggest that AI assistants may support students’ perceived cognitive engagement in programming when used reflectively for debugging, comparing solutions, and understanding programming logic. However, the results should be interpreted as perceived cognitive support rather than objective evidence of cognitive skill improvement.Originality - This study extends TAM by linking AI assistant acceptance with perceived cognitive outcomes in AI-assisted programming.
Co-Authors Abdul Muis Mappalotteng Darma Andayani, Dyah Fathahillah Fathahillah Fathahillah Kurnia Prima Putra Muhammad Fardan Muhammad Yahya Putri Nanda Sari