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The Impact of Generative Artificial Intelligence (GenAI) on the Quality and Ease of Teaching Material Preparation in Higher Education: Pengaruh Generative Artificial Intelligence (GenAI) terhadap Kualitas dan Kemudahan Penyusunan Materi Pembelajaran di Perguruan Tinggi Puspani, Nissa Syifa; Suyono, Annisa Maharani; Endiyansyah, Oktavian
JRST (Jurnal Riset Sains dan Teknologi) Volume 10 No. 1, March 2026: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v10i1.28531

Abstract

This study is motivated by the challenges faced by lecturers in Indonesian higher education institutions in developing high-quality teaching materials due to time constraints, limited resources, and the burden of the tri dharma duties. On the other hand, the Generative Artificial Intelligence (GenAI) offers a solution to enhance content efficiency and relevance through its key characteristics of relevance, content accuracy, and reference accuracy. Therefore, this research aims to evaluate the impact of GenAI usage on the quality and ease of preparing teaching materials in higher education settings. The study employs a quantitative approach using composite-based Structural Equation Modeling (C-SEM), based on data collected through surveys from lecturers at universities in Indonesia who have utilized GenAI in the teaching material design process. The analysis reveals that the main characteristics of GenAI, such as relevance, content accuracy, and reference accuracy, significantly contribute to improving lecturers' effectiveness and efficiency in designing teaching materials. The tested structural model demonstrates a significant positive influence of GenAI usage on the quality of teaching materials and the ease of their preparation, with large effect sizes. These findings underscore the importance of developing GenAI applications focused on enhancing content quality and relevance to support adaptive and innovative learning processes in higher education.   ABSTRAK  (Bahasa Indonesia) Penelitian ini dilatarbelakangi oleh tantangan dosen perguruan tinggi di Indonesia dalam menyusun materi pembelajaran berkualitas akibat keterbatasan waktu, sumber daya, dan beban tugas tridarma. Disisi lain, generative Artificial Intelligence (GenAI) dapat menjadi solusi untuk meningkatkan efisiensi dan relevansi konten melalui karakteristik relevansi, akurasi konten, serta akurasi referensi. Untuk itu, penelitian ini bertujuan mengevaluasi pengaruh penggunaan  GenAI terhadap kualitas dan kemudahan penyusunan materi pembelajaran di lingkungan pendidikan tinggi. Penelitian ini menggunakan pendekatan kuantitatif dengan metode composite-based Structural Equation Modeling (C-SEM), berdasarkan data yang dikumpulkan melalui survei terhadap dosen dari universitas di Indonesia yang telah memanfaatkan GenAI dalam proses perancangan materi ajar. Analisis menunjukkan bahwa karakteristik utama GenAI, seperti relevansi, akurasi konten, dan akurasi referensi, memberikan kontribusi signifikan dalam meningkatkan efektivitas dan efisiensi dosen dalam proses perancangan materi ajar. Model struktural yang diuji memperlihatkan pengaruh positif yang signifikan dari penggunaan GenAI terhadap kualitas materi pembelajaran dan kemudahan penyusunannya dengan ukuran efek yang besar. Hasil ini menegaskan pentingnya pengembangan aplikasi GenAI yang fokus pada peningkatan kualitas dan relevansi konten untuk mendukung proses pembelajaran yang adaptif dan inovatif di perguruan tinggi.
Perancangan Ulang Tata Letak Fasilitas Produksi Menggunakan Algoritma CORELAP dan CRAFT pada CV CTR Danuatmaja, Dony Sastra; Endiyansyah, Oktavian; Dermawan, Fadly Rizky; Ihsan, Tiaradia
SENTRI: Jurnal Riset Ilmiah Vol. 5 No. 2 (2026): SENTRI : Jurnal Riset Ilmiah, Februari 2026 (In Press)
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v5i2.5557

Abstract

In the Indonesian manufacturing industry, production efficiency is a key factor in improving company competitiveness, as a significant portion of operational costs originates from material handling activities. Inefficient facility layouts often result in long material flow distances, increased processing time, and high material handling costs. This study aims to optimize the facility layout of CV CTR, a manufacturing company producing ropes and threads, which currently experiences inefficient material flow due to improper departmental arrangement. The research applies a combination of the CORELAP (Computerized Relationship Layout Planning) algorithm and the CRAFT (Computerized Relative Allocation of Facilities Technique) method. CORELAP is used to construct an initial layout based on departmental closeness relationships, while CRAFT is employed to refine the layout through iterative department exchanges to minimize material handling costs. The results indicate that the proposed layout reduces the total Material Handling Cost (MHC) from Rp912,346.84 per day to Rp692,819.43 per day, achieving a 24% reduction. This confirms that the integration of CORELAP and CRAFT effectively improves material flow efficiency and reduces operational costs.