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All Journal International Journal of Electrical and Computer Engineering Jurnal Gamma TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi CommIT (Communication & Information Technology) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal of Artificial Intelligence and Data Mining Jiko (Jurnal Informatika dan komputer) Technologia: Jurnal Ilmiah Jurnal Abdimas Madani dan Lestari (JAMALI) ILKOMNIKA: Journal of Computer Science and Applied Informatics Automata TIN: TERAPAN INFORMATIKA NUSANTARA JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Journal Research of Social Science, Economics, and Management Jurnal Informatika: Jurnal Pengembangan IT
Arrie Kurniawardhani
Universitas Islam Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Implementation of Semi-Supervised Learning with YOLOv11 for On-Shelf Availability Detection of Retail Avilba, Pandu; Kurniawardhani, Arrie; Fudholi, Dhomas Hatta
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10881

Abstract

On-Shelf Availability (OSA) is a critical aspect of retail operations that affects customer satisfaction and potential sales. Computer vision–based systems have emerged as a promising solution to monitor product availability on store shelves. However, their implementation faces the challenge of limited labeled data, which requires time-consuming manual annotation with precise bounding boxes. This study proposes a semi-supervised learning approach based on pseudo-labeling using the YOLOv11n architecture to address the scarcity of labeled data. We utilized a dataset of 918 retail product images with 174 classes, divided into four proportions of labeled data (20%, 40%, 60%, and 80%). The research stages included training a teacher model, generating pseudo-labels with a confidence threshold of 0.5, and training a student model using a combination of labeled and pseudo-labeled data. Experimental results show that this approach effectively improves detection performance. With 60% labeled data, the model achieved an mAP50 of 0.931 and an mAP50-95 of 0.864, along with high-quality pseudo-labels (F1-Score 0.727; IoU 0.819). This significant improvement indicates that pseudo-labels can enrich data variation without introducing excessive noise. The study demonstrates that semi-supervised learning can reduce dependence on large labeled datasets while offering a practical and efficient solution for OSA detection systems in retail environments
Classification of Roasting Maturity Levels of Coffee Beans Using CNN Method Based on Mobilenetv2 Rafidan Arsyan, Renalda Geriel; Kurniawardhani, Arrie; Paputungan, Irving Putra
Journal Research of Social Science, Economics, and Management Vol. 5 No. 6 (2026): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i6.1269

Abstract

Determining the roasting maturity level of coffee beans is an important process to maintain consistency in flavor quality. However, the assessment process, which is still largely manual, tends to be subjective and highly dependent on the experience of farmers. This research develops an automatic classification model for four categories of coffee bean roasting levels—green, light, medium, and dark—using a convolutional neural network (CNN) architecture based on MobileNetV2. The dataset was divided into training, validation, and testing sets with a ratio of 75:15:10. The model was trained in two stages: initial training with a frozen base model, followed by fine-tuning of the last quarter of the layers. The experimental results show that the model achieved an accuracy of 96% with stable performance, as indicated by the loss and accuracy curves. These findings demonstrate that MobileNetV2 can serve as an effective solution for classifying coffee bean roasting levels with efficient computational time and competitive accuracy.
Co-Authors Abidatul Izzah Afdhari, Fahrezi Bintang Agus Eko Minarno Agus Zainal Arifin Ahmad Faris Akbar Alamsyah, Juan Parez Mangku Alfaz, Revansha Zulfikar Alhanafi, Ahmad Azzam Andaru, Gabriel Imam Avilba, Pandu Ayu Septya Maulani Aziz, Achmad Noer Catur Singgih Daniel Swanjaya Dhomas Hatta Fudholi Diana Purwitasari Dimas Setyawan Ramadhansyah Fathurrahman, Ihya Fayruz Rahma Ferdian Nursulistio Fitri Bimantoro Fudholi, Dhomas Hatta Huda, Sheila Nurul Ihya Fathurrahman Fathurrahman Indra Lukmana Indrayanti, Indrayanti Indrayanti, Indrayanti Irving V. Paputungan Isye Arieshanti Izzati Muhimmah Khoirunnisa Shofwatul Laili Mufti Leriansyah Muhammad Rizqi Az Zayyad Muhimmah, Izzati mukhamad masrur Najmudin, Nabil Nanik Suciati Nur Jati Lantang Marfu'ah Paputungan, Irving Putra Rafidan Arsyan, Renalda Geriel Rahadian Kurniawan Rasyadi, M Javier Rio Galang Jati Respati Salsabila Zahirah Pranida Santoso, Muhammad Lucky Lazuardi Setya Winarno Siti Mutrofin Sri Kusumadewi Yuda Munarko