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All Journal Dinamisia: Jurnal Pengabdian Kepada Masyarakat AL-UMRON: Jurnal Pengabdian kepada Masyarakat
Faiqurrahman, Mahar
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Humanities Economics, Econometrics & Finance Education Public Health Social Sciences Other

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PENDAMPINGAN PEMBANGUNAN WEBSITE DAN KONTEN DIGITAL KREATIF DI ERA 5.0 BERBASIS GENERATIVE ARTIFICIAL INTELLIGENCE (GEN-AI) Basuki, Setio; Faiqurrahman, Mahar; Putri, Valencia Sefiana; Nugraha, Muhammad Daffa; Shafiyah, Rahajeng Febri
Al-Umron : Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2025): AL-UMRON : Jurnal Pengabdian kepada Masyarakat
Publisher : LEMBAGA PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT (LPPM) UNIVERSITAS NAHDLATUL ULAMA SUNAN GIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/alumron.v6i1.4191

Abstract

The use of Generative Artificial Intelligence (Gen-AI) in digital content development and website development is a new approach to digital marketing in the 5.0 era. The community service program spearheaded by Universitas Muhammadiyah Malang (UMM) aims to help schools improve branding, visibility, and promotional effectiveness with AI technology that generates automated content, so that they can create websites and creative content without coding using Gen-AI. The methods used include (i) observation, (ii) digital marketing strategy development, (iii) training module development, (iv) mentoring implementation, and (v) evaluation of results. The program invited 20 teachers from three Secondary Schools. The effectiveness of this program was evaluated through questionnaires before and after mentoring, related to several aspects, namely (i) Understanding of AI, (ii) Utilization of AI to Create Text Teaching Materials (Textbooks), (iii) Utilization of AI to Create Learning Videos, (iv) Utilization of AI to Create Websites. The results showed a significant increase in the aspect of understanding of AI increased from 62% (pre-test) to 83% (post-test), utilization of AI for text teaching materials increased from 64% (pre-test) to 85% (post-test), and utilization of AI to create learning videos and websites increased from 57% (pre-test) to 82% (post-test).
Assistance in Preparing Engineering Prompts for Muhammadiyah School Teachers to Optimize the Use of ChatGPT in the World of Education: Pendampingan Penyusunan Prompt Engineering Bagi Guru Sekolah Muhammadiyah Untuk Mengoptimalkan Pemanfaatan ChatGPT Di Dunia Pendidikan Basuki, Setio; Faiqurrahman, Mahar; Marthasari, Gita Indah; Indrabayu, Rizky; Zachra, Fatimatus; Effendy, Nico Ardia
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 3 (2024): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v8i3.19522

Abstract

ChatGPT, a widely used Large Language Model (LLM), enhances productivity in various sectors, including education. However, its extensive usage often lacks proficiency in writing effective prompts, resulting in less optimal, biased, and hallucinated outputs. This community service initiative by Universitas Muhammadiyah Malang (UMM) aims to educate teachers on prompt engineering, enabling them to (i) write effective prompts to utilize ChatGPT's potential, (ii) educate about potential biases and hallucinations of ChatGPT, and (iii) integrate ChatGPT into educational practices with integrity. Partnering with three Muhammadiyah Schools, the program trains 7-8 teachers from each institution. The training covers five key areas: (a) prompt engineering introduction, (b) building optimal prompts, (c) leveraging ChatGPT in teaching and learning, (d) prompt engineering for educational material creation, and (e) ethics of LLM usage in professional and academic settings. The effectiveness of this program is evaluated through pre-test and post-test questionnaires. Results indicate a significant improvement in prompt engineering proficiency, rising from 35.3% (pre-test) to 87.5% (post-test), and in the utilization of ChatGPT for learning support, increasing from 23.5% (pre-test) to 81.3% (post-test).
Co-Authors Basuki, Setio Effendy, Nico Ardia Gita Indah Marthasari Indrabayu, Rizky Nugraha, Muhammad Daffa Putri, Valencia Sefiana Shafiyah, Rahajeng Febri Zachra, Fatimatus