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All Journal Jurnal Riset Informatika Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi
Fadhel Akhmad Hizham
Universitas Jember
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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CLASSIFICATION OF COFFEE LEAF SPOT DISEASES USING THE RESIDUAL NEURAL NETWORKS Stanislaus Jiwandana Pinasthika; Fadhel Akhmad Hizham; Annisa Fitri Maghfiroh Harvyanti
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1646.353 KB) | DOI: 10.34288/jri.v8i2.425

Abstract

Coffee is one of the competitive commodities that requires detailed quality control. The common diseases that attack coffee plants are miner, rust, and phoma. Despite their visual similarity, the diseases differ in symptoms and treatments, requiring precise identification aided by computer vision. Miner and phoma have similar image features that are challenging in this study. Avoiding treatment error, several deep learning approach is needed to help classify the diseases. One of the robust methods is the Residual Network. Considering the number of datasets and alignment with the state-of-the-art, this study picked ResNet50 and ResNet101 to be observed. This study employed ResNet50 and ResNet101 in two scenarios. The first scenario was training the models on datasets without preprocessing, while the second scenario trained models on processed datasets. The preprocessing involved converting the color model to HSV and taking the range of leaf spot color from light red to dark brown for color segmentation. This study successfully achieved accuracy, precision, and F1-score at 89,16%, 89,42%, and 89,15% respectively, for the ResNet50 model trained on preprocessed data, slightly higher than the metrics of ResNet101. The ResNet101 achieved 87.95% of accuracy, 88.05% of precision, and 87.98% of F1-Score. These results indicate that ResNet50 is more robust for classifying the leaf spot, and the color segmentation helped the model to optimize the performance
Pengembangan Website Profil sebagai Media Promosi Digital pada UMKM Sanggar Rias Sylvie Annisa Fitri Maghfiroh Harvyanti; Fadhel Akhmad Hizham; Shynta Ayu Dwi Darmawan; Vina Dewi Ramadhanty Ramadhanty; Ifrina Nuritha
Pengabdian kepada Masyarakat Bidang Teknologi dan Sistem Informasi (PETISI) Vol. 4 No. 1 (2026): Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/petisi.v4i1.4750

Abstract

Sanggar Rias Sylvie merupakan salah satu Usaha Mikro, Kecil, dan Menengah (UMKM) di Jember yang bergerak di bidang jasa rias dan kecantikan. Permasalahan utama yang dihadapi mitra adalah keterbatasan jangkauan promosi karena masih mengandalkan metode konvensional seperti dari mulut ke mulut dan media sosial yang tidak terkelola secara optimal. Kondisi ini menyebabkan rendahnya visibilitas usaha di tengah persaingan digital yang semakin ketat. Kegiatan ini bertujuan untuk mengembangkan website profil sebagai media promosi digital yang dapat meningkatkan jangkauan pemasaran Sanggar Rias Sylvie secara lebih luas dan terstruktur. Metode yang digunakan dalam kegiatan ini meliputi survei dan identifikasi kebutuhan mitra, perancangan dan pengembangan website menggunakan Content Management System (CMS) WordPress, pelatihan pengelolaan website kepada pemilik usaha, serta evaluasi dan monitoring pasca implementasi. Hasil kegiatan menunjukkan bahwa website profil berhasil dikembangkan dengan fitur-fitur utama meliputi halaman profil usaha, portofolio layanan, galeri foto, informasi kontak, dan formulir pemesanan online. Website profil ini diharapkan dapat menjadi sarana promosi digital yang berkelanjutan bagi Sanggar Rias Sylvie dalam menghadapi era digitalisasi UMKM.  
Co-Authors Annisa Fitri Maghfiroh Harvyanti Ifrina Nuritha Shynta Ayu Dwi Darmawan Stanislaus Jiwandana Pinasthika Vina Dewi Ramadhanty Ramadhanty