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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Specta Journal of Technology Scientific Journal of Informatics ILTEK : Jurnal Teknologi Journal of System and Computer Engineering Jurnal Pengabdian Masyarakat Abdimas Madani Patria Artha Journal of Community Bulletin of Information Technology (BIT) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Teknologi dan Komputer ILTEK : Jurnal Teknologi Journal of Embedded Systems, Security and Intelligent Systems Jurnal Pengabdian Sosial JIKTIF
Nursuci Putri Husain
Universitas Islam Makassar
Religion Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences

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Analisis Diagnosis Penyakit Ikan Lele Berbasis Website Menggunakan Metode Forward Chaining Dan Certainty Factor Sukirman Sukirman; Fahri El Fazza; Nursuci Putri Husain
Jurnal Ilmiah Informatika Vol. 8 No. 1 (2023): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v8i1.37-53

Abstract

There are several new symptoms and new types of diseases in catfish farming. Through this website, catfish farmers can find out how to prevent and solve catfish diseases. The Expert System Development Life Cycle is the study methodology utilized an expert system with a forward chaining mechanism as a decision, while the certainty factor is a supporter of confidence for the diagnosis of catfish disease. Confidence from experts and users on the type of flatulence with a value of 74% while a little confidence in the type of intestinal rupture and the bacterium Flexibacter columnaris with a value of 30%.
A Multi-Branch EfficientNet-U-Net Hybrid Framework for Segmentation of Oyster Mushrooms in Cultivation Media Husain, Nursuci Putri; Kadir, Muh Ichwan
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.2607

Abstract

Purpose – This study aims to develop a semantic segmentation model for oyster mushrooms in cultivation media to support automated monitoring, growth analysis, and yield estimation in smart farming systems. Design/methods/approach – A Multi-Branch EfficientNet-U-Net hybrid architecture was proposed, using EfficientNet-B0 as the encoder and a multi-branch fusion strategy to integrate multi-scale features from three encoder levels. The dataset consisted of 150 manually annotated oyster mushroom images collected from two cultivation sites under varying illumination, mushroom cluster density, and background texture. The model was evaluated using Intersection over Union (IoU) and Dice Coefficient metrics on training, validation, and testing subsets. Findings – Experimental results show that the proposed model achieved high segmentation performance, with a median IoU of approximately 0.90 and a Dice coefficient of 0.93. Compared with the baseline U-Net, the proposed architecture produced cleaner segmentation boundaries and more consistent detection of mushroom regions under complex environmental conditions. Research implications/limitations – This study is limited by the relatively small dataset and evaluation on images from only two cultivation sites. Further studies should involve larger and more diverse datasets to assess robustness across broader cultivation environments. Originality/value – This study offers an effective semantic segmentation framework that combines EfficientNet-B0 encoding and multi-branch multi-scale feature fusion to improve oyster mushroom segmentation accuracy in real cultivation settings, with potential application in smart agriculture monitoring systems.
Co-Authors Agus Zainal Arifin Ahmad Hanafie Ainun Nur, Rikhzan Akhsa, Amaliah Chintami Darti Akmal, Maisyal Arwinsa, Arwinsa Atsari Hardiman, Ashriyanto Baco, Syarifudddin Burhanuddin, Nur Isnayanti Chastine Fatichah Darlis Herumurti Dedi Susanto El Fazza, Fahri Fahri El Fazza Faridah Faridah Fitri, Rahmadhany Futriani, Riski H. Halim, Nur Indah Sari Hamdan Arfandy Hanafie, Ahmad Hariati Kumkelo Herlinah B Herlinah B Herwinsyah Ichwan K, Muh Ichwan K, Muh. Jabar, Rahul Jainab, Zul efendi Junaedy Junaedy M Akbar Mardawiah MARIAM Martani, Ahmad Mirnawaty Sultan, Dian Mithahul Khairah Muh. Dzulkifli P Muh. Ichwan Kadir Musrawati Mutmainnah Mutmainnah Mutmainnah Mutmainnah Nurhidayah, Ulfa Nursanti Novi Arisa Nurseno Bayu Aji Nurseno Bayu Aji, Nurseno Bayu Putri Nur Rahayu Ramadhan, Fatwa Ramli, Ryan Midzar Wiradinata Rantau Jati, Tanto Dwi Ratu, Ratu Ameliah Ria Rahmi Putri Rosmiati Sajiah, Sajiah Sandre, Revas Siti Eti Tridamayanti Soasiu, Munawar Suharni Sukirman Sukirman Sukirman Sukirman Suradi Suradi Suradi Suradi Syarifuddin Baco Syarifuddin Baco Tridarmayanti, Siti Eti Tukmuly, Asyuni