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All Journal Nusantara Hasana Journal Journal of Applied Informatics Science
Fahri Hamdani
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Nursing

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INTEGRASI TEKNOLOGI PENDIDIKAN DALAM PEMBELAJARAN TATAP MUKA TERBATAS: STUDI PENGEMBANGAN MODEL HYBRID LEARNING DI SMAN 1 TALIWANG Fahri Hamdani; Dinola, Dinola; Anwar, Anwar
Nusantara Hasana Journal Vol. 5 No. 7 (2025): Nusantara Hasana Journal, December 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v5i7.1772

Abstract

This study examines the implementation of a hybrid learning model in limited face-to-face instruction at SMAN 1 Taliwang, developed as an adaptive response to post-pandemic learning conditions. Using a Research and Development (R&D) approach based on the ADDIE model, data were collected through classroom observations, questionnaires for teachers and students, and analysis of pre-test and post-test learning outcomes. The findings indicate that integrating digital tools—such as Google Classroom, video-based materials, and digital modules—helped maintain learning continuity despite reduced classroom hours. Most students reported that digital media facilitated their understanding of the material, although some still faced internet access challenges, particularly those living in peripheral areas of Taliwang. The results show significant improvements in student learning processes and outcomes. The average post-test scores increased compared to the pre-test, and student participation in submitting digital assignments exceeded 85%. Teachers also stated that the hybrid learning model provided greater flexibility in delivering materials and conducting assessments. Although the model did not fully resolve all technical issues and variations in students’ digital literacy, it proved feasible and effective as an adaptive learning strategy at SMAN 1 Taliwang. These findings support the continued refinement and long-term integration of educational technology within the school’s instructional practices.
CornLeafNet: Disease-Area-Based Corn Leaf Disease Classification Using Convolutional Neural Networks Herfandi Herfandi; Eri Sasmita Susanto; Fahri Hamdani; Jonathan Afriliansyah
Journal of Applied Informatics Science Volume 2 Issue 2 (2026)
Publisher : GWS Tech Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65897/jais.v2.i2.96

Abstract

Corn leaf diseases can reduce crop productivity by disrupting photosynthesis and plant growth. Manual identification in large-scale fields remains limited due to its dependence on observer expertise, visual similarity among disease symptoms, and variations in field conditions. This study proposes CornLeafNet, a Custom Convolutional Neural Network model for disease-area-based corn leaf disease classification. The dataset consists of XML-annotated corn leaf images, from which disease-affected regions were extracted through annotation parsing and bounding box-based cropping to focus the model on symptomatic leaf areas. CornLeafNet was developed to classify three disease categories: Grey Leaf Spot, Corn Rust, and Leaf Blight. The model achieved a validation accuracy of 97.66% and a testing accuracy of 98.60%, with precision, recall, and F1-score values of 0.9860, respectively. The best-performing model was converted into ONNX format and deployed in a web-based prototype for image- and video-based classification. The testing results showed that all core system functions operated as expected, indicating that CornLeafNet has potential as an automatic and practical support model for early corn leaf disease identification.
Co-Authors Anwar Anwar Dinola, Dinola Eri Sasmita Susanto Herfandi Herfandi Jonathan Afriliansyah