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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT) Abdimasku : Jurnal Pengabdian Masyarakat
Nurul Anisa Sri Winarsih
Universitas Dian Nuswantoro, Semarang, Indonesia
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Pelatihan Dasar Komputasi (Computational Thinking) untuk Pembuatan Game pada Siswa SMA/SMK Kabupaten dan Kota Kediri Zudha Pratama; Filmada Ocky Saputra; Didik Hermanto; Nurul Anisa Sri Winarsih; Galuh Wilujeng Saraswati
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3225

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman siswa SMA/SMK di Kota dan Kabupaten Kediri terhadap konsep Dasar Komputasi (Computational Thinking) sebagai fondasi dalam pemrograman dan pembuatan game. Computational Thinking meliputi kemampuan dekomposisi, pengenalan pola, abstraksi, dan perancangan algoritma yang sangat penting untuk menyelesaikan permasalahan secara terstruktur. Metode pelaksanaan kegiatan dilakukan melalui pelatihan interaktif yang mencakup penyampaian materi, simulasi logika game menggunakan studi kasus mini game Fruit Catcher, serta evaluasi pembelajaran melalui pre-test dan post-test. Hasil evaluasi menunjukkan adanya peningkatan pemahaman yang signifikan pada seluruh kategori materi, dengan rata-rata peningkatan persentase jawaban benar sebesar 35%–40%. Hasil ini menunjukkan bahwa pendekatan pembelajaran berbasis game efektif dalam menanamkan pola berpikir komputasional pada siswa SMA/SMK.
Lightweight Deep Learning Approach for Sugarcane Leaf Disease Classification Using MobileNetV2 Cinantya Paramita; Rifky Bintang Pradana; Nurul Anisa Sri Winarsih; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.456

Abstract

Sugarcane is one of Indonesia’s strategic crops, yet its productivity is frequently disrupted by leaf diseases such as yellow leaf, rust, and red rot. Previous studies have shown that deep learning models are promising for plant disease detection, but many of them rely on heavy architectures that limit deployment in real-world agricultural settings. To address this gap, this study applies MobileNetV2, a lightweight Convolutional Neural Network, for the classification of sugarcane leaf diseases. Using the publicly available Kaggle dataset, the model was trained and evaluated on four classes: healthy, yellow leaf, rust, and red rot. The results demonstrate that MobileNetV2 achieved 97.0% test accuracy, with strong precision, recall, and F1-scores across all categories. These findings highlight that efficient deep learning architectures can deliver reliable disease classification while remaining practical for implementation on mobile or edge devices. Compared with previous approaches, this study contributes by demonstrating that lightweight model like MobileNetV2 can provide a balance of accuracy and efficiency, making them suitable for supporting precision agriculture practices in resource-limited environments
Co-Authors Cinantya Paramita Didik Hermanto Filmada Ocky Saputra Galuh Wilujeng Saraswati Ricardus Anggi Pramunendar Rifky Bintang Pradana Zudha Pratama