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All Journal bit-Tech PANDALUNGAN : Jurnal Pengabdian kepada Masyarakat
Rifqi, Mohammad Habim Hazidan
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Religion Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics

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PEMANFAATAN LAHAN KOSONG UNTUK MENUNJANG KETAHANAN PANGAN MELALUI PEMBIBITAN SAYURAN DI DESA KASIYAN, JEMBER Rifqi, Mohammad Habim Hazidan; Stephanie, Netanya Adel; Setiawan, Rozak Budi; A.Ibrahim, Hilmi; Atasa, Dita; Megasari, Dita
Pandalungan: Jurnal Pengabdian kepada Masyarakat Vol 3 No 1 (2024): Oktober
Publisher : Universitas Al-Falah As-Sunniyah Kencong Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62097/pandalungan.v3i1.1919

Abstract

Ketahanan pangan merupakan aspek vital dalam mencapai tujuan pembangunan berkelanjutan, yang mencakup ketersediaan, aksesibilitas, keterjangkauan, dan kualitas pangan. Desa Kasiyan, Kabupaten Jember, dengan potensi alam yang mendukung pertanian, masih menghadapi tantangan dalam pemanfaatan lahan kosong. Untuk itu, program KKN Kolaboratif #3 tahun 2024 memperkenalkan inisiatif pembibitan sayur mayur seperti bayam, kangkung, dan cabai untuk meningkatkan ketahanan pangan dan ekonomi lokal. Program ini mencakup persiapan media tanam, penyemaian, distribusi bibit, dan sosialisasi perawatan tanaman. Hasil evaluasi menunjukkan peningkatan pengetahuan masyarakat dalam bercocok tanam, serta potensi pemanfaatan lahan pekarangan sebagai upaya mencapai kemandirian pangan dan kesejahteraan sosial. Program ini diharapkan dapat menurunkan ketergantungan terhadap pasar dan menciptakan peluang ekonomi baru melalui pertanian berkelanjutan.
Analisis Perbandingan Deteksi Penyakit Daun Jagung Menggunakan YOLO dan CNN Rifqi, Mohammad Habim Hazidan; Haromainy, Muhammad Muharrom Al; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3392

Abstract

This study compares the performance of two deep learning methods, You Only Look Once version 8 (YOLOv8) and the Convolutional Neural Network (CNN) EfficientNetB0, in detecting and classifying maize leaf diseases. The background of this research stems from the importance of early plant disease identification to support food security, as well as the limitations of manual inspection methods, which are slow, subjective, and inefficient. The study combines primary and secondary data, totaling 2,000 images that underwent undersampling, augmentation, resizing, and bounding box annotation for YOLO training needs. Both models were trained on the same dataset with an 80% training and 20% testing split. YOLOv8n was trained using a transfer learning approach for 30 epochs, while the CNN was trained using EfficientNetB0 with similar training parameters. The results show that YOLOv8 achieved high detection performance with an mAP@0.5 of 0.985 and the highest class accuracy in the Healthy category (0.99). Meanwhile, the CNN demonstrated more stable classification performance, achieving the highest accuracy in the Grey Leaf Spot class (0.99) and a validation accuracy of 0.96. The comparison indicates that YOLO excels in object detection and disease localization in field images, whereas the CNN is more consistent in classifying segmented leaf images. These findings provide practical implications for real world deployment: YOLOv8 is suitable for real time detection in field conditions, including potential integration into mobile based early warning systems for farmers, while EfficientNetB0 is more appropriate for offline or laboratory based classification of static leaf samples.
Co-Authors A.Ibrahim, Hilmi Dita Atasa Dita Megasari Muhammad Muharrom Al Haromainy Nurlaili, Afina Lina Setiawan, Rozak Budi Stephanie, Netanya Adel