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All Journal JREC (Journal of Electrical and Electronics) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Elementer (Elektro dan Mesin Terapan) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol EPIC : Journal of Electrical Power, Instrumentation and Control Aisyah Journal of Informatics and Electrical Engineering Jurnal Mekanova : Mekanikal, Inovasi dan Teknologi Jurnal Midpro JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Energy and Electrical Engineering (JEEE) Journal of Electrical Engineering and Computer (JEECOM) Jurnal Ilmiah Wahana Pendidikan JUTECH : Journal Education and Technology Jurnal Teknik Elektro Integrasi : Jurnal Ilmiah Teknik Industri Power Elektronik : Jurnal Orang Elektro
Reni Rahmadewi, Reni
Universitas andalas
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Other

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Implementation of YOLOv8 for Classifying Fertile and Infertile Eggs in the Chicken Hatching Process Arihta, Michael; Manullang, Maribeth Adventina; Hanafi, Zikri; Rahmadewi, Reni
Journal of Energy and Electrical Engineering Vol 7, No 1: October 2025
Publisher : Teknik Elektro Universitas Siliwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jeee.v7i1.15774

Abstract

This study aims to develop an embryo detection system in chicken eggs using the YOLOv8 algorithm based on computer vision. This approach is proposed as a solution to the manual candling method which is often inaccurate and time consuming. The dataset used amounted to 4,396 chicken egg images, consisting of fertile and infertile categories. The model was trained using Google Collaboratory with GPU support, where the model was trained for 100 epochs to maximize accuracy. The evaluation results show that the YOLOv8 model is able to detect embryos with a high level of accuracy, indicated by a precision value of 93.2%, mean average precision (mAP) of 98.5%, and recall of 87.2%. The fertile category was successfully detected with a precision of 100% and a recall of 94.2%, while the infertile category had a precision percentage of 86.4% and a recall of 100%. These findings prove that the YOLOv8 algorithm can be effectively implemented to automate the selection process of fertile and infertile eggs, thereby improving efficiency and accuracy in the livestock production process.
Metode Thresholding Otsu untuk Klasifikasi Awal Vitiligo dan Hyperpigmentation Berdasarkan Luas Piksel Putra Alvaro, Rayhan; Rahmadewi, Reni
EPIC Journal of Electrical Power Instrumentation and Control Vol 8 No 1 (2025): EPIC
Publisher : Universitas Pamulang, Prodi teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/epic.v8i1.58841

Abstract

Penelitian ini mengembangkan sistem deteksi kelainan pigmentasi kulit menggunakan metode Otsu Thresholding dan analisis jumlah piksel pada citra biner. Sistem dibuat dalam bentuk Graphical User Interface (GUI) MATLAB sehingga memudahkan pengguna dalam melakukan input citra, cropping, pra-pemrosesan, segmentasi, dan klasifikasi. Dataset terdiri dari 30 citra yang mencakup 15 citra vitiligo dan 15 citra hiperpigmentasi. Tahap pra-pemrosesan dilakukan melalui konversi grayscale dan Gaussian smoothing untuk menstabilkan intensitas sebelum segmentasi otomatis oleh metode Otsu. Hasil segmentasi dianalisis melalui perhitungan jumlah piksel putih dan hitam untuk menentukan jenis kelainan. Hasil pengujian menunjukkan akurasi 100% untuk vitiligo dan 80% untuk hiperpigmentasi. Secara keseluruhan sistem mencapai akurasi 90% dan dapat digunakan sebagai pendekatan awal deteksi kelainan pigmentasi kulit.
Co-Authors Abda Abda Abdul jalil Afta Wijaksana, Sevada Andika Pratama Arief, Akhmad Ikhsannul Arihta, Michael Ariyanto Pamungkas, Guntur Arnisa Stefanie Bangsa, Insani Abdi Budhi Santoso, Dian Cahyaputra, Hafizhiadi Rizki Diko Aprilyanto, Muhammad Dwi Wibowo, Muhammad Fauzan Al-Ghifary Dzaki, Ilham Fadhlurrahman, Rafi Fahla, Adam Fatah, Amirul Fathurrizqy, Muhammad Fawaz Fauzi, Rafly Febriyanto, Dionisius Firmasnyah, Naufal Haasyim Hadikusuma, Ridwan Satrio Hanafi, Zikri Haris Hermawan, Haris Hibban Sya'bana, Daffa Hidayat, Ahmad Hilman Ibnu Prabowo, Farhan Ibrahim Ibrahim Immanuel, Josya Marvin Irsabmalfi, Rangga Laili, Maria Bestarina Lela Nurpulaela Lindawati, Utari Luqman Ramadhani, Muhammad Malik Ibrahim, Muhamad Manullang, Maribeth Adventina Maulana, Enggal Meilany, Mauludy Naufal, Daffa Handika Nawawi, Haikal Engga Nugraha, Rifki Fajar Nurzamilah, Zulia Pramudia, Rendy Prapongga, Muhammad Krisna Purwita Sary, Indri Pusparina A, Rianda Puspitasari, Della Putra Alvaro, Rayhan Rachman, Faishal Rahmi, Hayatul Ramadhan, Andra Caesar Raya Ismail, Dzaguar Rismayanti, Azizah Santoso, Dian Budhi Septianti, Nurul Sihaloho, Arman Dwika Farlentinus Hasudungan Siswadi Siswadi Sukmawardani, Ramadhan Syahda, Rizky Oriza Yuliarman Saragih