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All Journal Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Sosial
Ummah, Annisa Syifaul
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Religion Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Pengembangan Agroeduwisata Kopi Arabika dan Konservasi sebagai Strategi Pemberdayaan Masyarakat di Desa Petarangan Ummah, Annisa Syifaul; Wiratama, Ristiya Adi
Jurnal Pengabdian Sosial Vol. 2 No. 11 (2025): September
Publisher : PT. Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/g64ktv79

Abstract

Program Kuliah Kerja Nyata (KKN) Tematik Universitas Sebelas Maret 2025 di Desa Petarangan dilaksanakan untuk mengoptimalkan potensi kopi arabika melalui pendekatan agroeduwisata dan konservasi lingkungan. Kegiatan meliputi sosialisasi budidaya kopi berkelanjutan, pelatihan pengolahan limbah kopi menjadi produk bernilai ekonomi, promosi digital wisata kopi, pemeriksaan kesehatan gratis, edukasi konsumsi kopi sehat, serta pembinaan minat bakat anak-anak melalui Ruang Ceria. Puncaknya, Festival Kopi Desa menjadi ajang pameran produk dan penampilan minat bakat dari program kerja Ruang Ceria. Hasil kegiatan menunjukkan peningkatan pemahaman petani terhadap teknik budidaya, lahirnya produk inovatif berbasis kopi, meningkatnya kesadaran kesehatan, serta terbangunnya citra Desa Petarangan sebagai destinasi agroeduwisata. Program ini terbukti berkontribusi pada pemberdayaan masyarakat dan mendukung pembangunan berkelanjutan di bidang ekonomi, sosial, dan lingkungan.
Enhancing Flood Area Segmentation in Remote Sensing Images Using Hybrid Attention Mechanism on DeepLabV3+ with ResNet-50 Backbone Ummah, Annisa Syifaul; Suryani, Esti; Dewangkoro , Herdito Ibnu
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5523

Abstract

Flooding is caused by climate change and urbanization, so rapid and accurate monitoring is essential in supporting emergency response. However, flood segmentation still faces challenges in dense vegetation. This study aims to improve and analyze the performance of the Hybrid Attention Mechanism in the form of Point-wise spatial attention (PSA) and Squeeze-and-Excitation Block (SE Block) in the DeepLabV3+ architecture with the ResNet-50 backbone. The methods used include collecting a dataset of 600 training and 63 validation, data augmentation, model development and Hybrid Attention Mechanism design, hyperparameter optimization, ablation study, and performance evaluation. The ablation results obtained show the best performance with accuracy of 0.9624, F1-score of 0.9618, IoU (Non-Flood) of 0.9323, IoU (Flood) of 0.9208, and mIoU of 0.9265, surpassing previous studies that used Modified U-Net in detecting floods in dense vegetation. This research contributes to the development of a flood segmentation model based on a hybrid attention mechanism, which is more effective in detecting flooded areas in densely vegetated regions.
Co-Authors Dewangkoro , Herdito Ibnu Esti Suryani Wiratama, Ristiya Adi