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All Journal Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Masyarakat Indonesia
Tuti Andriani
STMIK Mulia Darma
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education

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Pelatihan Pembuatan Media Pembelajaran Digital Menggunakan Canva bagi Guru Sekolah Dasar Pandi Barita Nauli Simangunsong; Tuti Andriani; Matias Julyus Fika Sirait; Apriwati Br Barus; Sorang Pakpahan
Jurnal Masyarakat Indonesia (Jumas) Vol. 5 No. 01 (2026): Jurnal Masyarakat Indonesia (Jumas)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jumas.v5i01.416

Abstract

Perkembangan teknologi digital menuntut guru memiliki kompetensi dalam mengembangkan media pembelajaran yang kreatif dan inovatif. Namun, pemanfaatan aplikasi desain digital, seperti Canva, oleh guru sekolah dasar masih belum optimal sehingga diperlukan kegiatan pelatihan yang mampu meningkatkan kompetensi digital guru. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk meningkatkan kompetensi guru sekolah dasar dalam merancang media pembelajaran digital menggunakan Canva. Metode yang digunakan adalah participatory training yang meliputi analisis kebutuhan, pre-test, sosialisasi dan pelatihan, praktik pembuatan media pembelajaran, pendampingan, presentasi hasil, post-test, dan evaluasi program. Evaluasi dilakukan melalui pre-test, post-test, observasi, dokumentasi, dan kuesioner kepuasan peserta. Hasil kegiatan menunjukkan bahwa guru mampu memahami dan memanfaatkan berbagai fitur Canva untuk menghasilkan media pembelajaran berupa presentasi, poster edukasi, infografis, dan lembar kerja peserta didik (LKPD) yang sesuai dengan kebutuhan pembelajaran. Selain itu, pelatihan memberikan peningkatan pengetahuan, keterampilan, dan kepercayaan diri guru dalam mengintegrasikan teknologi digital ke dalam proses pembelajaran. Dengan demikian, pelatihan Canva menjadi salah satu strategi yang efektif untuk meningkatkan kompetensi digital guru serta mendukung terciptanya pembelajaran yang lebih kreatif, interaktif, dan berkualitas di sekolah dasar.
Optimized MobileNetV2 for Tomato Leaf Disease Classification Using AutoAugment, AdamW, and Cosine Annealing Learning Rate Pandi Barita Nauli Simangunsong; Tuti Andriani; Matias Julyus Fika Sirait
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 07 (2026): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i07.2636

Abstract

Tomato leaf diseases pose a significant challenge to agricultural productivity, as delayed or inaccurate disease identification can substantially reduce crop yield and quality. Recent advances in deep learning have demonstrated considerable potential for automated plant disease classification; however, many existing approaches rely heavily on transfer learning and computationally intensive convolutional neural network (CNN) architectures. This study proposes an optimized MobileNetV2 framework trained entirely from scratch for tomato leaf disease classification by integrating AutoAugment for automated data augmentation, the AdamW optimizer for effective parameter optimization, and a Cosine Annealing Learning Rate scheduler to achieve stable model convergence. Experiments were conducted using the PlantVillage tomato leaf dataset comprising ten disease categories, including healthy leaves. The proposed model was trained for 50 epochs without employing pre-trained ImageNet weights. Experimental results demonstrated stable convergence, with the training loss decreasing from 1.5569 to 0.5543 and the validation loss decreasing from 2.5431 to 0.5754, indicating good generalization performance without significant overfitting. The proposed framework achieved a best validation accuracy of 92.89%, with 92.95% precision, 92.89% recall, and an F1-score of 92.87%. These findings indicate that the integration of AutoAugment, AdamW, and Cosine Annealing effectively enhances the learning capability of MobileNetV2 even without transfer learning. The proposed approach provides a lightweight and computationally efficient solution suitable for deployment on resource-constrained devices, making it a promising alternative for real-time tomato leaf disease diagnosis in smart agriculture applications.
Co-Authors Apriwati Br Barus Matias Julyus Fika Sirait Matias Julyus Fika Sirait Pandi Barita Nauli Simangunsong Sorang Pakpahan