Efektivitas Penggunaan Ruang Warna HSV untuk Klasifikasi Daging Sapi Segar dan Busuk dalam Industri Pangan
DOI:
https://doi.org/10.35870/jtik.v9i1.3129Keywords:
Hsv Color Space, Fresh and Bad Meat, ClassificationAbstract
Beef is a source of animal protein which is very important in the human diet. The quality of beef determines the nutritional value and taste of the processed meat product. However, the quality of beef can decrease over time, especially if it is not stored properly. Therefore, identifying the condition of beef is crucial to ensure that consumers get safe and quality products. The use of the HSV (Hue, Saturation, Value) color space for beef classification is an interesting method to research. The HSV color space is closer to human perception of color compared to the RGB color space, making it more effective for image analysis in the context of visual quality assessment of meat. In this study, researchers used HSV space extraction to classify fresh beef and bad beef. This research aims to develop a method for classifying fresh, medium and rotten beef using the HSV color space. This research produces accurate extraction results with appropriate classification of fresh and bad beef.
Downloads
References
Amalia, V. F., & Dewi, R. R. (2024). PENILAIAN KESEGARAN IKAN DENGAN METODE K-NEAREST NEIGHBOR DAN PENGOLAHAN CITRA DIGITAL. JATI (Jurnal Mahasiswa Teknik Informatika), 8(4), 7823-7829. DOI: https://doi.org/10.36040/jati.v8i4.10441.
Areni, I. S., Amirullah, I., & Arifin, N. (2019). Klasifikasi kematangan stroberi berbasis segmentasi warna dengan metode HSV. Jurnal Penelitian Enjinering, 23(2), 113–116.
Arfika, D. D., Syafitri, I., & Pahutar, P. H. (2024). SISTEM PENDETEKSI KEMATANGAN BUAH ALPUKAT DENGAN TRANSFORMASI RUANG WARNA HSI. JATI (Jurnal Mahasiswa Teknik Informatika), 8(4), 6061-6066. DOI: https://doi.org/10.36040/jati.v8i4.10117.
Bugis, S. A., Cakra, C., Islah, A. M., Said, M. S., Suarna, D., & Said, M. S. (2024). Implementasi Algoritma K-Nearest Neighbor (K-Nn) Dalam Perancangan Alat Pendeteksi Tingkat Kesegaran Daging. Simtek: jurnal sistem informasi dan teknik komputer, 9(1), 55-61. DOI: https://doi.org/10.51876/simtek.v9i1.376.
Fauzi, J. F., Tolle, H., & Dewi, R. K. (2018). Implementasi Metode RGB To HSV pada Aplikasi Pengenalan Mata Uang Kertas Berbasis Android untuk Tuna Netra. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(6), 2319-2325.
Gonzalez, R. C. (2009). Digital image processing. Pearson education india.
Hadinegoro, A., & Rizaldilhi, D. A. (2021). Pengaruh HSV pada pengolahan citra untuk kematangan buah cabai. Building of Informatics, Technology and Science (BITS), 3(3), 155–163. https://doi.org/10.47065/bits.v3i3.1020.
Hastawan, A. F., Septiana, R., & Windarto, Y. E. (2019). Perbaikan hasil segmentasi hsv pada citra digital menggunakan metode segmentasi rgb grayscale. Edu Komputika J, 6(1), 32-37.
Iskandar, D., & Marjuki, M. (2022). Classification of Melinjo Fruit Levels Using Skin Color Detection With RGB and HSV. Journal of Applied Engineering and Technological Science (JAETS), 4(1), 123-130.
Pah, N. E. R., Mola, S. A. S., & Mauko, A. Y. (2021). Ekstraksi ciri warna HSV dan ciri bentuk moment invariant untuk klasifikasi buah apel merah. Jurnal Komputer dan Informatika, 9(2), 142–153. https://doi.org/10.35508/jicon.v9i2.5043.
Saputra, Y. D., & Setiawan, F. B. (2023). Penerapan deteksi garis pada AGV menggunakan metode HSV. Transmisi: Jurnal Ilmiah Teknik Elektro, 25(4), 172–178. https://doi.org/10.14710/transmisi.25.4.172-178.
Yohannes, Y., Udjulawa, D., & Sariyo, T. I. (2021). Klasifikasi jenis jamur menggunakan SVM dengan fitur HSV dan HOG. PETIR, 15(1), 113–120. DOI: https://doi.org/10.33322/petir.v15i1.1101.
Zhu, Z., & Nandi, A. K. (2015). Automatic modulation classification: principles, algorithms and applications. John Wiley & Sons.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Dadang Iskandar Mulyana, Veri Arinal, Feri Akbarulloh

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
