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All Journal JURNAL INTEGRASI PROSES RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Informatika Kaputama (JIK) Industrial Engineering Journal (IEJ) Jurnal Informasi dan Teknologi Jurnal Tekinkom (Teknik Informasi dan Komputer) International Journal of Engineering, Science and Information Technology Journal of Childhood Development TECHSI - Jurnal Teknik Informatika Jurnal MathEducation Nusantara Internasional Journal of Data Science, Computer Science and Informatics Technology (InJODACSIT) Jurnal Pendidikan Matematika Malikussaleh Jurnal Teknologi Terapan and Sains 4.0 Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Jurnal Solusi Masyarakat Dikara Jurnal Ilmiah Kesehatan Keperawatan Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Maryana Maryana
Universitas Malikussaleh, Indonesia
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Journal : journal of applied informatics and computing

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Real-Time Detection of Coffee Cherry Ripeness Using YOLOv11 Ilyana, Anis; Nurdin, Nurdin; Maryana, Maryana
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9735

Abstract

This study aims to develop a real-time coffee fruit ripeness detection system using the YOLOv11 algorithm to assist farmers in determining the optimal harvest time. The dataset comprises 302 images categorized into three ripeness levels: ripe, semi-ripe, and unripe. Model training was conducted on Google Colab with data augmentation to enhance dataset variability and prevent overfitting. After 20 epochs, the model demonstrated strong performance in the ripe category (mAP50: 0.774, Precision: 0.645, Recall: 0.812) and satisfactory results for semi-ripe fruits (mAP50: 0.695, Precision: 0.624, Recall: 0.679). However, detection performance for unripe fruits was lower (mAP50: 0.4). The system achieved an inference time of 183.4 ms per image, with fast preprocessing and postprocessing (0.5 ms each), indicating its suitability for real-time applications. While the model performs well overall, further improvement is needed in detecting unripe coffee fruits for enhanced system effectiveness.
Co-Authors Ahmad Fauzi Abdillah Aklimawati Aklimawati, Aklimawati Angga Pratama Annisa Afrilia Zahra Annisa Anya Regina Putri Arief Rahman Aryandi, Aryandi Ayuni, Novita Cut Ita Erliana Dahlan Abdullah darnila eva Daud, Eva Darnila Defi Irwansyah Dema Yulianto E. Elwina EDI YUSUF, EDI Effan Fahrizal Eka Yuliana Fatimah Erna Isfayani Eva Darnila Ezwarsyah Ezwarsyah Ezwasyah, Ezwasyah Fadlisyah Fadlisyah Fadlisyah Fajriana, Fajriana fatimah Fatimah Faturrahman, Puja Fauzi, Alfi Fuadi, Wahyu Haves Qausar Hayatun Nufus Ilyana, Anis Ismiaton Fitria Iwan Pahendra Jasiah Khairunnisa Khairunnisa Khalid Mawardi Lis Ayu Widari, Lis Ayu Listiana, Yeni Marhami, Marhami Martunis Maulida, Leni Meriatna Meriatna Muhammad Arrayyan Muhammad Azmi, Muhammad Muhammad Daud Muhammad Fadhil Muhammad Muhammad Muhammad Ridhwan Muhammad Zakaria muli ani Munawarah Munawarah, Munawarah Mursalin . Muthmainnah Muthmainnah mutiara mutiara Nadya Raudathul Sofa Ndari, Wulan Novi Sylvia Nur Elisyah Nurdin Nurdin Nurdin Nurdin Olanda, T.M.Riski Raihan Putri Rini Meiyanti Rohantizani, Rohantizani Rozi Kesuma Dinata Rozzi Kesuma Dinata Safwandi Safwandi Sahputra, Ilham Said Fadlan Anshari salamah salamah Sari, Riska Kumala Sinambela Marzuki Siti Hajar Sofyan, Diana Khairani Sri Meutia Sugeng - Sujacka Retno Sumarni Rumfot Syibral Malasyi, Syibral Syukriah Syukriah, Syukriah Taufiq Taufiq Ulfi Zahara Ulia, Rahmatul Wahyu Fuadi Wahyu Fuadi Wanrice, Mabrur Wulandari Wulandari Yazid Bindar Yovi Chandra Zalfie Ardian Zarkasyi Zarkasyi Zuraida Zuraida