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All Journal JURNAL ILMIAH PETERNAKAN TERPADU CITIZEN: Jurnal Ilmiah Mulitidisiplin Indonesia
Ni'am, Muhammad Nidhomun
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Religion Humanities Earth & Planetary Sciences Economics, Econometrics & Finance Education Social Sciences Other

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The Effect of Adding Cricket Flour (Gryllus bimaculatus) as a Substitute for Soybean Meal in Finisher Feed on Broiler Chicken Welfare Lazuardi, Pahlawan Bintang; Ni'am, Muhammad Nidhomun; Widiastuti, Sri
JURNAL ILMIAH PETERNAKAN TERPADU Vol. 13 No. 3 (2025)
Publisher : DEPARTMENT OF ANIMAL HUSBANDRY, FACULTY OF AGRICULTURE, UNIVERSITY OF LAMPUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jipt.v13i3.p631-645

Abstract

The issue of limited soybean meal availability and dependence on imports in Indonesia necessitates the search for alternative protein sources for animal feed. This study aimed to evaluate the impact of partially replacing soybean meal with cricket flour in the finisher feed of broiler chickens on breast dirtiness, footpad dermatitis (FPD), and hock burn. A total of 100 male Indian River broiler chickens were raised for 5 weeks in a closed-house system, divided into two treatment groups with 5 replications, each consisting of 10 chickens. The birds were housed in pens measuring 1.5 m x 0.75 m. The feeding treatments began during the finisher phase (age 21–35 days), with P0 containing 28.5% soybean meal and P1 containing 18.5% soybean meal + 10% cricket flour. Data on breast dirtiness, FPD, hock burn, and behavior were collected from days 33 to 35 and subsequently scored. The data were analyzed statistically using the Mann-Whitney test with the aid of the Statistical Package for Social Science (SPSS). The results showed that substituting 10% of soybean meal with cricket flour significantly reduced footpad dermatitis (P=0.02), while no significant differences were found in hock burn and breast dirtiness (P>0.05). Based on the findings of this study, it can be inferred that partially substituting soybean meal with cricket meal has the potential as an alternative protein source without causing welfare issues in broiler chickens.
Deep Learning Based Detection Of Laying Hen Health Status From Excreta Images Using MobileNetV2: Deteksi Status Kesehatan Ayam Petelur Berbasis Deep Learning Dari Citra Ekskreta Menggunakan MobileNetV2 Ni'am, Muhammad Nidhomun; Widiastuti, Sri; Fahrezi, Wildan Deni; Al-Huda, Thoriqul Irfah; Muqofi, Abdul Karim; Mustofa, Rizal Aji; Muafi, Arib Zainul
Citizen : Jurnal Ilmiah Multidisiplin Indonesia Vol. 6 No. 2 (2026): CITIZEN: Jurnal Ilmiah Multidisiplin Indonesia (On Progress)
Publisher : DAS Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53866/jimi.v6i2.1285

Abstract

Early and accurate disease detection is a critical challenge in modern poultry farming. This study aimed to develop and evaluate a deep learning-based classification system using MobileNetV2 Convolutional Neural Network (CNN) architecture for automated detection of poultry diseases from excreta images only, and to validate model predictions against laboratory microbiological analyses. A total dataset of 8,087 labeled excreta images was compiled across four health categories: Healthy, Salmonella, Coccidiosis, and Newcastle Disease, and subsequently split into training (6,471) and validation (1,616) subsets at an 80:20 ratio. The MobileNetV2 model was trained over eight epochs with data augmentation strategies and evaluated using precision, recall, F1-score, accuracy, and confusion matrix analysis. The model achieved an overall accuracy of 91%, with the highest per-class F1-score for Coccidiosis (0.97) and the lowest for Newcastle Disease (0.75). The CNN MobileNetV2 architecture demonstrates strong potential for real-time, non-invasive poultry disease monitoring.
Co-Authors Al-Huda, Thoriqul Irfah Fahrezi, Wildan Deni Lazuardi, Pahlawan Bintang Muafi, Arib Zainul Muqofi, Abdul Karim Mustofa, Rizal Aji Sri Widiastuti, Sri