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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Information Technology and Computer Science (JOINTECS) Network Engineering Research Operation [NERO] Jurnal Sains dan Informatika Generation Journal Joutica : Journal of Informatic Unisla Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) NERO (Networking Engineering Research Operation) Jurnal Pengabdian Masyarakat Nusantara JISCOMP (Journal of Information System and Computer)
Julian Sahertian
Universitas Nusantara PGRI Kediri
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Transportation

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Journal : nero networking engineering research operation

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Deteksi dan Klasifikasi Kue Tradisional Indonesia Menggunakan YOLOv8 Mustofa, Arin Ayu Silvyani; Wulanningrum, Resty; Sahertian, Julian
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.30177

Abstract

Indonesian traditional cakes are part of the cultural heritage, characterized by their rich flavors, unique forms, and significant historical value. However, the lack of recognition among younger generations necessitates a new approach to preservation efforts. This study aims to develop an image processing-based detection system for traditional cake types using the YOLOv8 algorithm. The five types of cakes identified in this research are lumpur cake, lapis cake, wingko cake, dadar gulung cake, and putu ayu cake. The image dataset was obtained through a combination of direct image capture and public datasets, and was manually annotated using the Roboflow platform. The model was trained using the PyTorch framework and evaluated based on precision, recall, F1-score, and mean Average Precision (mAP) metrics. Experimental results show that the system achieved an average mAP of 89.9% and an F1-score of 86.5%, with a relatively low classification error rate. These findings indicate that the YOLOv8 algorithm is effective in detecting visually similar objects and holds significant potential for application in the digital preservation of culinary heritage. The system can also be further developed as a technology-based educational medium to support the conservation of Indonesia’s local culinary wealth.Keywords: YOLOv8, Object Detection, Cake Traditional, Image Processing, Computer Vision
Deteksi dan Klasifikasi Kue Tradisional Indonesia Menggunakan YOLOv8 Mustofa, Arin Ayu Silvyani; Wulanningrum, Resty; Sahertian, Julian
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.30177

Abstract

Indonesian traditional cakes are part of the cultural heritage, characterized by their rich flavors, unique forms, and significant historical value. However, the lack of recognition among younger generations necessitates a new approach to preservation efforts. This study aims to develop an image processing-based detection system for traditional cake types using the YOLOv8 algorithm. The five types of cakes identified in this research are lumpur cake, lapis cake, wingko cake, dadar gulung cake, and putu ayu cake. The image dataset was obtained through a combination of direct image capture and public datasets, and was manually annotated using the Roboflow platform. The model was trained using the PyTorch framework and evaluated based on precision, recall, F1-score, and mean Average Precision (mAP) metrics. Experimental results show that the system achieved an average mAP of 89.9% and an F1-score of 86.5%, with a relatively low classification error rate. These findings indicate that the YOLOv8 algorithm is effective in detecting visually similar objects and holds significant potential for application in the digital preservation of culinary heritage. The system can also be further developed as a technology-based educational medium to support the conservation of Indonesia’s local culinary wealth.Keywords: YOLOv8, Object Detection, Cake Traditional, Image Processing, Computer Vision
Co-Authors Achmad Iqbal Maulana Adhitia, Riswandha Ahmad Bagus Setiawan Ahmad Robet Nailul Author Alexander, Kevin Rio Alif Rama Nugroho Amanda, Novia Anandra, Yayan Anardha, Danuar Aditya Anifiatiningrum Anwar, Muhammad Choirul Anwarruddin, Muhammad Tri Aqharabah, Bhisri Hafi Ardi Sanjaya Astutik, Eka Yulia Sri Ayu Meudea, Prita Azmi, Muhamad Ulul Baehaqi BIMA SETIAWAN Cholid Ilham Isniawan Christofel Wicaksono, John Danang Wahyu Widodo Daniel Swanjaya Darmawan, Reza Depi, Alisa Sintiya Diansyah, Alex Rahma Dipa Perwira, Mohammad Askar Doni Abdul Fatah Dusea Widyadara, Made Ayu Erlina Nasrinatun Ni’mah Erwanto, Elga Asfa Fery Setiawan Frans Rega Agista Hari Setiawan Ibnu Al Ikrom Ilham Firmansyah Indra Septiawan Intan Nur Farida Irawan, Rony Hery Juli Sulaksono Khotmuniza, Muzan Ihda Kumalasari, Ratih Kurniawan, Candra Mega Adi Kurniawati, Desi Dwi Luluk Indah Safitri M. Rizal Umami Made Ayu Dusea Widyadara - Universitas Nusantara Kediri, Made Ayu Dusea Widyadara Mahdiyah, Umi Mahmudi, Aris Majid, Moh. Lukky Abdul Marjuni, Mohamad Marzuki, Moh. Ismail Maulana, Arfan Moh Imam Yusuf Mustofa Muhammad Vicko Putra Ardiansyah Mulya, Leon Prasetya Mustofa, Arin Ayu Silvyani Muzan Ihda Khotmuniza Natasha, Sonya Niska Shofia, Niska Niswatin , Ratih Kumalasari Novianto, Alfian Dwi Nugroho, Alindro Septo Nur Farida Nurarinda, Terry Anda Putra Nurul Mahpiroh Odhianto, Yosan Pandie, Risky Vridel Eduard Patmi Kasih Pramudita, Yosua Yonnas Prasetyo, Mochammad Bima Pratama Putra, Septiandy Adibya Pratama, Tutus Lusni Raharjo, Yulianto Dwi Ramadhanu, Ilham Khefi Ratih Kumalasari Niswatin Resty Wulanningrum Risa Helilintar Risky Aswi R, Risky Rizakatama, Moh. Rohman Rizal, Muhamad Helmi Khoirur Rohman, Ahmad Andi Fatkhur Rony Heri Irawan Rony Hery Irawan Saiful Akbar Salsabila, Adinda Meylia Santoso, Christa Witta Putra Saputro, Aryo Widodo Satria Bijaksana Satrio Damara, Moch. Deifa Sholahuddin, Muhammad Resandi Subiyantoko, Rizki Suraju, Ghovin Triosaputra, Johan Rizky Umami, M. Rizal Utama, Yoga Putra WAHYU FIRMANSYAH Wahyuniar , Lilia Sinta Wulaningrum, Resty Yahya, Moh. Zakariya Yahya, Nisaa’ Husnia Yan Yusril Muzaky Yuprastiwi, Yessy Zuhri, Mohamad Farkhan Fahmi