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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Keteknikan Pertanian Tropis dan Biosistem Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Multimedia Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jiko (Jurnal Informatika dan komputer) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Teknik Informatika (JUTIF) Jurnal Informatika dan Multimedia
Yuita Arum Sari
Brawijaya University, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mechanical Engineering Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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A Comparative Analysis of Color Channel-Based Feature Extraction using Machine Learning versus Deep Learning for Food Recognition Sari, Yuita Arum; Nugraha, Dwi Cahya Astria; Adinugroho, Sigit
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5001

Abstract

Automated Dietary Assessment Accurate food recognition is a big challenge in computer vision which is critical for developing Automated Dietary assessment and health monitoring systems. The key question it answered was whether traditional machine learning with feature engineering by hand can beat modern deep learning approaches? In this Context, this study serves as a comparative analysis of these two paradigms. The baseline method worked by extracting texture (LBP,GLCM) and color information from different channels of five colors spaces (RGB, HSV, LAB, YUV,YCbCr) followed by feeding these features into multiple classifiers such as Nearest Neighbor(NN), Decision Tree and Naïve Bayes. These were then compared to deep learning models (MobileNet_v2, ResNet18, ResNet50, EfficientNet_B0). The best traditional one can reach an accuracy of 93.33%, using texture features extracted from the UV channel and classified with a NN. Nevertheless, the deep learning models consistently presented higher performance and MobileNet_v2 reached up to 94.9% accuracy without requiring manual feature selection. In this paper, we show that end-to-end deep learning models are more powerful and error robust for food recognition. These results highlight their promise for constructing more effective and scalable real-world applications with less need for intricate, domain-specific feature engineering.
Improving Direct Image Regression for Blood Cell Enumeration with a Fine-Tuned Backbone Adinugroho, Sigit; Sari, Yuita Arum; Utaminingrum, Fitri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5004

Abstract

Complete blood count (CBC) examination provides an important insight for diagnosis or disease treatment. Currently, CBC examination requires complex and expensive devices that limit their deployment in remote area. The development of computer vision based method offers simplification to the process. However, its implementation is limited to the availability of large size labelled dataset. This research aims to develop a direct image regressor that is able to regress directly from image. There are two stages in estimation process. First, the backbone is trained using large dataset available for blood cell classification problem. Then the trained backbone is plugged into the final model by adding a fully connected neural network that acts as regressor. The whole model is then trained using limited whole blood cell count dataset. The evaluation process shows that training the backbone using large size related dataset improve the performance by 50%. This study can be used to create a low-cost blood component evaluation tool, particularly in rural areas where access to advanced laboratory equipment is limited.
Co-Authors Achmad Arwan Adinugroho, Sigit Agus Wahyu Widodo, Agus Wahyu Agus Zainal Arifin Alayasi, Mutia Anggi Gustiningsih Hapsani Bihanda, Yusuf Gladiensyah Budi Darma Setiawan Chastine Fatichah Dahnial Syauqy Dewa Gede Trika Meranggi Dhea Rahma Widyadhana Edy Santoso Evy Kamilah Ratnasari Fadhil Yusuf Rahadika Fatchurrahman, Danial Hapsani, Anggi Gustiningsih Hapsani, Anggi Gustiningsih Hurriyatul Fitriyah Indriati Indriati Iqbal, Rizky Maulana Jaya Mahar Maligan Jaya Mahar Maligan Jayawarsa, A.A. Ketut Laksono Trisnantoro Lukman Hakim Luthfi Maulana, Luthfi M. Ali Fauzi Muhammad Aminul Akbar Muhammad Hafiz Azhar Muhammad Tanzil Furqon, Muhammad Tanzil Mustika Mentari Nabila Nur’aini Novanto Yudistira Nugraha, Dwi Cahya Astria Nuraini, Nabila Oktaviana, Natasya Eldha Pranata, Alfisyar Jefry Putra Pandu Adikara Putra Pandu Adikara Putra Pandu Adikari Randy Cahya Wihandika Ratih Kartika Dewi Renaldi Primaswara Praetya Rinandyaswara, Raditya Rizal Maulana, Rizal Sigit Adinugroho Simangunsong, Bryan Nicholas Josephin Hotlando Siregar, Rifki Akbar Siti Mutrofin Siti Mutrofin Tesa Eranti Putri Utaminingrum, Fitri Wijaya Kurniawan Yusuf Gladiensyah Bihanda