IMPLEMENTASI ALGORITMA MACHINE LEARNING K-MEANS DALAM KOMPRESI CITRA FOTO CANDI BOROBUDUR
IMPLEMENTATION OF K-MEANS MACHINE LEARNING ALGORITHM IN IMAGE COMPRESSION OF BOROBUDUR TEMPLE PHOTOS
DOI:
https://doi.org/10.52972/hoaq.vol16no1.p1-8Keywords:
kompresi citra, k-means clustering, candi borobudur, machine learningAbstract
Kompresi citra merupakan teknik penting dalam pengelolaan data visual, terutama dalam pelestarian dan digitalisasi warisan budaya. Penelitian ini bertujuan untuk mengkaji efektivitas algoritma K-Means Clustering dalam kompresi citra foto Candi Borobudur guna mengurangi ukuran file tanpa kehilangan kualitas visual yang signifikan. Metode penelitian mencakup pengumpulan citra digital Candi Borobudur dengan variasi pencahayaan dan tekstur. Sampel dipilih menggunakan purposive sampling untuk memastikan representasi optimal. Proses pengolahan melibatkan praproses citra, penerapan algoritma K-Means untuk pengelompokan warna, serta evaluasi hasil menggunakan metrik Peak Signal-to-Noise Ratio (PSNR) dan Structural Similarity Index (SSIM). Hasil penelitian menunjukkan bahwa algoritma K-Means efektif dalam mengurangi jumlah warna pada citra sehingga ukuran file berkurang secara signifikan. Analisis kuantitatif menunjukkan bahwa nilai PSNR dan SSIM tetap dalam batas yang dapat diterima, memungkinkan detail relief dan struktur candi tetap terjaga. Kesimpulan dari penelitian ini adalah bahwa algoritma K-Means dapat digunakan sebagai metode kompresi citra yang efisien dalam konteks digitalisasi warisan budaya. Namun, metode ini memiliki keterbatasan dalam menangani perubahan warna ekstrem. Oleh karena itu, penggunaan metode hibrida dengan algoritma lain direkomendasikan untuk meningkatkan efisiensi kompresi tanpa mengorbankan kualitas citra.
Image compression is an important technique in visual data management, especially in the preservation and digitization of cultural heritage. This study aims to examine the effectiveness of the K-Means Clustering algorithm in the compression of Borobudur Temple photo images to reduce file size without significant loss of visual quality. The research method includes collecting digital images of Borobudur Temple with variations in lighting and texture. Samples are selected using purposive sampling to ensure optimal representation. The processing process involves image preprocessing, the application of the K-Means algorithm for color grouping, and the evaluation of results using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. The results show that the K-Means algorithm is effective in reducing the number of colors in the image so that the file size is significantly reduced. Quantitative analysis shows that the PSNR and SSIM values remain within acceptable limits, allowing the details of the reliefs and structure of the temple to be preserved. The conclusion of this study is that the K-Means algorithm can be used as an efficient image compression method in the context of cultural heritage digitization. However, this method has limitations in dealing with extreme color changes. Therefore, the use of hybrid methods with other algorithms is recommended to improve compression efficiency without sacrificing image quality.
References
A. Himmah, A. S. Wibowo, dan A. R. Setiawan, "Penerapan K-Means Clustering untuk Klasifikasi Citra Cabai Keriting: Studi Ekstraksi Warna dan Tekstur GLCM," Jurnal Ilmu Komputer dan Sistem Informasi, vol. 8, no. 2, pp. 24-32, 2024, doi.org/10.31294/ijcs.v3i2.5758.
B. Kasatriyanto dan S. Sularsih, "Pendokumentasian Nilai Relief Candi Borobudur dalam Seni Tari melalui Film Dokumenter," Jurnal Konservasi Cagar Budaya Borobudur, vol. 17, no. 1, pp. 3-18, 2023, doi.org/10.33374/jurnalkonservasicagarbudaya.v17i1.307.
J. Kurniawan, "Soundscape Sakral Candi Borobudur," Jurnal Konservasi Cagar Budaya, vol. 18, no. 2, pp. 95-109, 2024, doi.org/10.33374/jurnalkonservasicagarbudaya.v18i2.345.
E. Rindrasih, K. C. Effendi, dan D. Silviani, "Persepsi Generasi Z terhadap Pariwisata Berkelanjutan pada Destinasi Super Prioritas Candi Borobudur," Jurnal Pariwisata Terapan, vol. 8, no. 1, pp. 1-18, 2024, doi.org/10.22146/jpt.95415.
S. Susiyanto, "Analisis Penerapan Konsep Pariwisata Hijau dalam Pengembangan Candi Borobudur sebagai Destinasi Super Prioritas," Ideas: Jurnal Pendidikan, Sosial, dan Budaya, vol. 8, no. 4, pp. 1371-1378, 2022,https://doi.org/10.32884/ideas.v8i4.1080.
A. Sutono, S. Tahir, S. Sumaryadi, A. Hernowo, dan W. Rahtomo, "Implementasi Model Ekosistem Pariwisata Halal di Kawasan Candi Borobudur," Indonesian Journal of Halal Research, vol. 3, no. 1, pp. 13-20, 2021, doi.org/10.15575/ijhar.v3i1.11119.
P. Mummoju, A. Wolff, M. Perdacher, C. Plant and C. Böhm, "Enhancing k-Means Algorithm with Tensor Processing Unit," 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 194-200, doi: 10.1109/BigData55660.2022.10020427.
B. S. Bharathi and K. V. Swamy, "Effective Image Segmentation using Modified K-Means Technique," 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), Tirunelveli, India, 2020, pp. 757-762, doi: 10.1109/ICOEI48184.2020.9142910.
M. Hu, E. C. C. Tsang, Y. Guo and Q. Zhang, "An Improved k-Means Algorithm with Spatial Constraints for Image Segmentation," 2021 International Conference on Machine Learning and Cybernetics (ICMLC), Adelaide, Australia, 2021, pp. 1-7, doi: 10.1109/ICMLC54886.2021.9737256.
S. M. Miraftabzadeh, C. G. Colombo, M. Longo and F. Foiadelli, "K-Means and Alternative Clustering Methods in Modern Power Systems," in IEEE Access, vol. 11, pp. 119596-119633, 2023, doi: 10.1109/ACCESS.2023.3327640.
H. Xu, S. Yao, Q. Li and Z. Ye, "An Improved K-means Clustering Algorithm," 2020 IEEE 5th International Symposium on Smart and Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS), Dortmund, Germany, 2020, pp. 1-5, doi: 10.1109/IDAACS-SWS50031.2020.9297060.
J. Qi, Y. Yu, L. Wang and J. Liu, "K*-Means: An Effective and Efficient K-Means Clustering Algorithm," 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), Atlanta, GA, USA, 2016, pp. 242-249, doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.46.
V. Schellekens and L. Jacques, "Quantized Compressive K-Means," in IEEE Signal Processing Letters, vol. 25, no. 8, pp. 1211-1215, Aug. 2018, doi: 10.1109/LSP.2018.2847908
W. Guo, K. Lin and W. Ye, "Deep Embedded K-Means Clustering," 2021 International Conference on Data Mining Workshops (ICDMW), Auckland, New Zealand, 2021, pp. 686-694, doi: 10.1109/ICDMW53433.2021.00090.
L. Wang, X. Zhang, H. Wang and C. Bai, "An improved global k-means clustering algorithm," 2021 11th International Conference on Intelligent Control and Information Processing (ICICIP), Dali, China, 2021, pp. 165-170, doi: 10.1109/ICICIP53388.2021.9642224.
K. R. Žalik and M. Žalik, "Comparison of K-Means, K-Means++, X-Means and Single Value Decomposition for Image Compression," 2023 27th International Conference on Circuits, Systems, Communications and Computers (CSCC), Rhodes (Rodos) Island, Greece, 2023, pp. 295-301, doi: 10.1109/CSCC58962.2023.00055.
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