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All Journal Jurnal Pengabdian kepada Masyarakat Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Computing Innovations and Emerging Technologies
Muhammad Akbar Syahbana Pane
Universitas Negeri Medan
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Classification of Plant Pests Using the Real-Time Detection Transformer (RT-DETR) Algorithm in Oil Palm Plants Bayu Ath Thariq Syams; Ratu Mutiara Siregar; Muhammad Akbar Syahbana Pane
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27029

Abstract

Plant Pests (PP) are one of the main factors causing a decline in oil palm productivity. This study focuses on the classification of PP, limited to the three main pest species: the rhinoceros beetle (Oryctes rhinoceros), the fireworm (Setora nitens), and the Tioman rat (Rattus tiomanicus). The dataset consists of digital images representing these three pest types. The method used is the Real-Time Detection Transformer (RT-DETR), which is capable of real-time, end-to-end object detection. The research stages include data collection, preprocessing, model training, and evaluation using confusion matrix, precision, recall, and F1-score metrics. The research results are expected to produce an accurate and efficient crop pest classification system to support decision-making in pest management in oil palm plantations.
EMPOWERING PRIMARY EDUCATORS IN THE AI ERA: AN EVALUATION OF GEMINI AI TRAINING SATISFACTION AT AN-NIZAM PRIMARY SCHOOL Ariatna Ariatna; Ricky Drimarcha Barus; Adi Widarma; Muhammad Akbar Syahbana Pane; Ayu Lestari
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 6 No. 1 (2026): Juni 2026
Publisher : Bajang Institute

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Abstract

The rapid development of Generative Artificial Intelligence (GenAI) has transformed primary education by offering solutions to administrative burdens and creating new opportunities for innovative instructional design. This study aimed to measure the professional satisfaction and perceived usefulness of an intensive Gemini AI prompt engineering training and mentoring program among 23 homeroom teachers at SD An-Nizam. The program focused on utilizing structured prompting techniques to create Deep Learning materials based on the Task-Based Language Teaching (TBLT) approach, specifically embodying Mindful, Meaningful, and Joyful (MMJ) principles. By applying specialized tools such as Nano Banana for educational illustrations, Gemini Canvas for task-based classroom games, and Deep Research for robust lesson planning (RPP), the training guided teachers in building a sustainable Prompt Library. Using a descriptive survey design with a 5-point Likert scale and open-ended questions, the results showed a highly positive reception toward the training content and instructor quality. A large majority of teachers (19 respondents) reported feeling significantly more confident in applying Generative AI-driven instructional design. Qualitative feedback highlighted that specific tool like Nano Banana effectively simplified complex concepts for younger students by combining text and vibrant images. The study concludes that this training successfully improved educators' prompt engineering skills and instructional efficiency, providing a useful blueprint for future professional development that incorporates peer-mentoring and localized AI integration in primary schools. While these results are promising, the study is limited by its small, single-site sample size and a focus on immediate outcomes rather than long-term pedagogical shifts. Nevertheless, these findings establish a foundation for larger-scale implementations, emphasizing that sustainable AI adoption in early education depends on human-centered support and context-specific curriculum design.
The Relationship Between Blended Learning Experience and Students' Problem-Solving Skills: A Quantitative Correlational Study Novialdi Ashari; Muhammad Akbar Syahbana Pane; Ulfah Oktarida Sihaloho
Journal of Computing Innovations and Emerging Technologies Vol. 2 No. 1 (2026): Volume 2 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v2i1.29

Abstract

   
Co-Authors Adi Widarma Ariatna Ariatna Ayu Lestari Bayu Ath Thariq Syams Novialdi Ashari Ratu Mutiara Siregar Ricky Drimarcha Barus Ulfah Oktarida Sihaloho