Penerapan Teknologi Pengolahan Citra dalam Analisis Data Visual pada Tinjauan Komprehensif

Authors

  • Supiyandi Supiyandi Universitas Pembangunan Panca Budi
  • Muhammad Abdul Mujib Universitas Islam Negeri Sumatera Utara
  • Khairul Azis Universitas Islam Negeri Sumatera Utara
  • Rahmat Abdillah Universitas Islam Negeri Sumatera Utara
  • Salsa Nabila Iskandar Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.59581/jkts-widyakarya.v2i3.3796

Keywords:

Image processing, Visual data analysis, Image segmentation, Edge detection, Pattern recognition, Image processing algorithms, Image processing software, Image resolution, Image noise, Computational requirements

Abstract

Image processing has become a key technology in visual data analysis, making significant contributions across various fields such as healthcare, security, and the creative industry. This article provides a comprehensive review of the application of image processing technology in visual data analysis, focusing on the latest methods, tools, and practical applications. We discuss various image processing techniques, including segmentation, edge detection, and pattern recognition, as well as how these techniques are applied to process and analyze visual data. The study also includes performance evaluations of various commonly used image processing algorithms and software. Additionally, we explore the challenges faced in applying this technology, such as image resolution issues, noise, and high computational demands. By offering an extensive overview of the development and implementation of image processing technology, this article aims to be a valuable reference for researchers and practitioners working in the field of visual data analysis.

References

• Angreini, S., & Supratman, E. (2021). Visualisasi data lokasi rawan bencana di Provinsi Sumatera Selatan menggunakan Tableau. Jurnal Nasional Ilmu Komputer, 2(2), 135-147.

• Effendi, M., Fitriya, F., & Effendi, U. (2017). Identifikasi jenis dan mutu teh menggunakan pengolahan citra digital dengan metode jaringan saraf tiruan. Jurnal Teknotan, 11(2), 67. https://doi.org/10.24198/jt.vol11n2.7

• Fernandez, L., Castillero, C., & Aguilera, J. M. (2005). An application of image analysis to dehydration of apple discs. Journal of Food Engineering, 67, 185-193.

• Hutahaean, H. D., Waluyo, B. D., & Rais, M. A. (2019). Teknologi identifikasi objek berbasis drone menggunakan algoritma SIFT citra digital. ITB Journal of Information and Communication Technology, 04, 193–198.

• Mengko, T. (1991). Algoritma dan arsitektur pengolahan citra. Pusat Antar Universitas Bidang Mikroelektronika ITB, Bandung.

• Widyaningsih, M. (2017). Identifikasi kematangan buah apel dengan Gray Level Co-Occurrence Matrix (GLCM). Jurnal SAINTEKOM, 6(1), 71. https://doi.org/10.33020/saintekom.v6i1.7

• Wibowo, A. S., & Andri, A. (2021). Dashboard Business Intelligence visualisasi data akreditasi Sekolah SMP Negeri 1 Sembawa. Jurnal Nasional Ilmu Komputer, 2(4), 249-256. https://doi.org/10.47747/jurnalnik.v2i4.536

Published

2024-07-05

How to Cite

Supiyandi Supiyandi, Muhammad Abdul Mujib, Khairul Azis, Rahmat Abdillah, & Salsa Nabila Iskandar. (2024). Penerapan Teknologi Pengolahan Citra dalam Analisis Data Visual pada Tinjauan Komprehensif. Jurnal Kendali Teknik Dan Sains, 2(3), 179–187. https://doi.org/10.59581/jkts-widyakarya.v2i3.3796

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