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All Journal JIWAKERTA: Jurnal Ilmiah Wawasan Kuliah Kerja Nyata Bulletin of Network Engineer and Informatics (BUFNETS)
Danuarta, Bachtiar
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Public Health Social Sciences

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Peran Mahasiswa Kuliah Kerja Nyata Dalam Pemberdayaan Dan Pengembangan Visual Umkm Di Desa Rojopolo Dewi, Sofia Rhosma; Ayyubi, Sholahuddin Al; S, Hanif Dzaky; Danuarta, Bachtiar; Darmawan, Hafid; Tilla, Siti Rohma
JIWAKERTA: Jurnal Ilmiah Wawasan Kuliah Kerja Nyata Vol 5, No 2 (2024): Desember
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/jiwakerta.v5i2.23768

Abstract

Usaha kecil mikro menengah  adalah  salah  satu  bidang  yang memberikan sumbangsih   yang   signifikan   dalam   memacu   pertumbuhan   ekonomi  di Indonesia. Hal   ini dikarenakan  daya  serapnya  terhadap  tenaga  kerja  yang  sangat  besar  dan  dekat  dengan masyarakat. Pertanian merupakan mayoritas mata pecaharian masyarakat desa Rojopolo namun tidak menutup kemungkinan masyarakatnya juga mempunyai usaha yang berskala kecil menengah (UMKM) seperti usaha ritel, produksi tahu, budidaya ikan lele, air minum galon, jual parfum, konter HP, dan lainnya. Banyaknya macam usaha yang ada di desa, diperlukan adanya Nomer Induk Berusaha (NIB) yang nantinya akan menunjang Identitas Visual Usaha. Dengan berwirausaha  selain mendapatkan legalitas usaha, pengajuan permodalan, pendanaan, juga mampu meningkatkan branding usaha. Pelaku  usaha  yang  ingin usahanya  berkembang  pesat  diharapkan mengetahui  pentingnya  legalitas  usaha dan  branding  terhadap  produknya.  Pengabdian mahasiswa KKN ini menjadi hal penting dalam membantu kepengurusan NIB yang nantinya akan membuka akses mudah bagi pelaku UMKM dalam perizinan, permodalan, serta pengurusan lainnya. Adanya NIB juga akan memudahkan dalam meningkatkan branding atau identitas visual suatu usaha. 
Classification of Papaya Leaf Disease Using Ensemble Learning Danuarta, Bachtiar; Nilogiri, Agung; Al Faruq, Habibatul Azizah
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April - September 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/asexz453

Abstract

Papaya leaf diseases, such as Papaya Ringspot Virus and Begomovirus, have a significant impact on papaya production and quality. Manual identification is often ineffective due to the visual similarity of symptoms. This study proposes a system for classifying papaya leaf diseases using a Convolutional Neural Network (CNN) with an ensemble learning method that aims to improve accuracy and provide accurate and stable predictions on new data that has not been seen during training. Three CNN architectures, namely VGG16, ResNet-34, and MobileNetV2, were trained individually with hyperparameter optimization using grid search. After training the individual models, the three models were combined (ensemble) using the soft voting method. The results showed that the best individual model was MobileNetV2, achieving 98% on all accuracy, precision, recall, and f1-score metrics, but the model with the most optimal performance was achieved by the ensemble model. The ensemble model achieved 99% accuracy, 98% precision, 99% recall, and 99% f1-score, these results demonstrate a significant improvement over the three individual trained models and outperform the ResNet-152v2 architecture reported in previous research
Co-Authors Agung Nilogiri Al Faruq, Habibatul Azizah Ayyubi, Sholahuddin Al Dewi, Sofia Rhosma Hafid Darmawan S, Hanif Dzaky Tilla, Siti Rohma