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All Journal Elkom: Jurnal Elektronika dan Komputer International Journal of Advances in Intelligent Informatics International Journal of Computing and Informatics (IJCANDI) Jurnal Pengabdian Pada Masyarakat ILKOM Jurnal Ilmiah Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Informasi dan Teknologi Indonesian Journal of Electrical Engineering and Computer Science G-Tech : Jurnal Teknologi Terapan Jurnal Abdidas Indonesian Journal of Data and Science Jurnal Teknik Informatika (JUTIF) Journal La Multiapp Jurnal Minfo Polgan (JMP) Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat Bulletin of Social Informatics Theory and Application Engineering: Journal of Mechatronics and Education Prosiding SeNTIK STI&K JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Linier: Literatur Informatika dan Komputer
Siska Anraeni
Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive 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 Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Journal : Bulletin of Social Informatics Theory and Application

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Innovative CNN approach for reliable chicken meat classification in the poultry industry Anraeni, Siska; Mustari, Muhid; Ramdaniah, Ramdaniah; Kurniati, Nia; Mubarak, Syahrul
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.686

Abstract

In response to the burgeoning need for advanced object recognition and classification, this research embarks on a journey harnessing the formidable capabilities of Convolutional Neural Networks (CNNs). The central aim of this study revolves around the precise identification and categorization of objects, with a specific focus on the critical task of distinguishing between fresh and spoiled chicken meat. This study's overarching objective is to craft a robust CNN-based classification model that excels in discriminating between objects. In the context of our research, we set out to create a model adept at distinguishing between fresh and rotten chicken meat. This endeavor holds immense potential in augmenting food safety and elevating quality control standards within the poultry industry. Our research methodology entails meticulous data collection, which includes acquiring high-resolution images of chicken meat. This meticulously curated dataset serves as the bedrock for both training and testing our CNN model. To optimize the model, we employ the 'adam' optimizer, while critical performance metrics, such as accuracy, precision, recall, and the F1-score, are methodically computed to evaluate the model's effectiveness. Our experimental findings unveil the remarkable success of our CNN model, with consistent accuracy, precision, and recall metrics all reaching an impressive pinnacle of 94%. These metrics underscore the model's excellence in the realm of object classification, with a particular emphasis on its proficiency in distinguishing between fresh and rotten chicken meat. In summation, our research concludes that the CNN model has exhibited exceptional prowess in the domains of object recognition and classification. The model's high accuracy signifies its precision in furnishing accurate predictions, while its elevated precision and recall values accentuate its effectiveness in differentiating between object classes. Consequently, the CNN model stands as a robust foundation for future strides in object classification technology. As we peer into the horizon of future research, myriad opportunities beckon. Our CNN model's applicability extends beyond chicken meat classification, inviting exploration across diverse domains. Furthermore, the model's refinement and adaptation for specific challenges represent an exciting avenue for future work, promising heightened performance across a broader spectrum of object recognition tasks.
Co-Authors Ainul Yaqin Anjasani Aisyah Aisyah Alfian Putra Ramadhan Amalia, Andi Cici Amaliah, Tazkirah Andi Alfian Pratama Putra Andi Nurul Dzulhijjah Darwis, Herdianti Dewi, Nabila Vita Erick Irawadi Alwi Erick Irawadi Alwi Erick Irawadi Alwi, Erick Irawadi Erika Riski Melani Fadhylah Nur Rezkyqah Fitriani Hasbullah Fitriyani Umar Furqaan Ismail Gaffar, Andi Widya Mufila Halim, Andi Ainun Dzariah Harlinda Lahuddin Hasnita Hasnita Hendrial Hendrial Herdiansya Herdiansya Herdianti Darwis Herdianti Darwis Herdianti Herdianti Herman Herman Herman Hidzrullah Ash Syuhrawardi Hilma Aszahrah Huda, Besse Nurul Ihwana As’ad Imada, Anugerah Indrabayu Indrabayu Ingrid Nurtanio Iqbal, Iwi Kurnia Irawati Irawati Januaril Aditya Samudra La Ode Abdurrahman Wahid Pattawari Lahuddin, Harlinda Lokapitasari Belluano, Poetri Lestari Lutfi Budi Ilmawan, Lutfi Budi Lutfi Budiman Ilmuwan M. Dimas Taufiqurahman M. Fiqry Septiawan Manga, Abdul Rachman Mansyur, St. Hajrah Mardiyyah Hasnawi Melani, Erika Riski Mubarak, Syahrul Muh Dasriyanto Saleh Muh. Aliyazid Mude Muhammad Arfah Asis Muhammad Fadhiel Muhammad Rifqi Fauzan Muhammad Salman Al Markas Muhsina Muhsina, Muhsina Muliyadi B Mursyid Mursyid Mustari, Muhid Nia Kurniati Nia Kurniati Nugraha Wanaspati Nur Amanah Nur Hikmah Amir Nursafi'at Nursafi'at Pahendra, Muhammad Agung Maugi Pomalingo, Suwito Ramdan Satra Ramdaniah Ramdaniah Ramdaniah Ramdaniah Ramdaniah, Ramdaniah Rifqatul Mukarramah Rina Junita Basri St. Hajrah Mansyur Sugiarti Sugiarti Sugiarti, Sugiarti Syafie, Lukman Syahrul Mubarak Abdullah Takdir Zulhaq Dessiaming Tasrif Hasanuddin Veithzal Rivai Zainal Wanaspati, Nugraha Wandi Anggara Yudha Nugraha Syailendra Yusrina Mukhlis