PERBANDINGAN JARAK EUCLIDEAN, CITYBLOCK, MINKOWSKI, CANBERRA, DAN CHEBYSHEV DALAM SISTEM TEMU KEMBALI CITRA BATIK

  • Mutmainnah Muchtar
    Universitas Sembilanbelas November Kolaka
  • Noorhasanah Zainuddin
    Universitas Sembilanbelas November Kolaka
  • Adha Mashur Sajiah
    Universitas Halu Oleo Kendari
  • Nurfitria Ningsi
    Universitas Sembilanbelas November Kolaka
  • Yuwanda Purnamasari Pasrun
    Universitas Sembilanbelas November Kolaka
DOI: https://doi.org/10.23960/jitet.v12i3S1.5324
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Abstract

Batik is a highly valuable cultural heritage in Indonesia, showcasing a rich diversity of motifs with deep meaning and aesthetics. To enhance the accessibility and utilization of batik collections, an efficient image retrieval system is essential. This study compares distance measurement methods in a batik image retrieval system: Euclidean, Cityblock, Minkowski, Canberra, and Chebyshev, using a combination of color and texture features. The dataset comprises 50 types of batik images. The results show that the Cityblock method achieves the highest Mean Average Precision (MAP) of 97.71, followed by Canberra with MAP 96.87. The Euclidean method also performs well with a MAP of 94.56, while Minkowski and Chebyshev have lower MAP values of 92.93 and 90.89, respectively. Chebyshev experiences the largest MAP drop when images are rotated (5.98), while Cityblock demonstrates the best resistance to rotation with the smallest MAP drop (1.51). This research successfully developed a Content-Based Image Retrieval (CBIR) system with a GUI in MATLAB and suggests integrating the latest image processing and machine learning techniques for further enhancement.

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References

B. Patel, K. Yadav, and D. Ghosh, “State-of-art: Similarity assessment for content based image retrieval system,” Proc. - 2020 IEEE Int. Symp. Sustain. Energy, Signal Process. Cyber Secur. iSSSC 2020, 2020, doi: 10.1109/iSSSC50941.2020.9358899.

L. K. Pavithra and T. S. Sharmila, “An efficient framework for image retrieval using color, texture and edge features,” Comput. Electr. Eng., vol. 70, pp. 580–593, 2018, doi: 10.1016/j.compeleceng.2017.08.030.

A. E. Minarno and N. Suciati, “Batik Image Retrieval Based on Color Difference Histogram and Gray Level Co-Occurrence Matrix,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 12, no. 3, p. 597, 2014, doi: 10.12928/telkomnika.v12i3.80.

A. E. Minarno, Y. Munarko, A. Kurniawardhani, F. Bimantoro, and N. Suciati, “Texture feature extraction using co-occurrence matrices of sub-band image for batik image classification,” 2014 2nd Int. Conf. Inf. Commun. Technol. ICoICT 2014, pp. 249–254, 2014, doi: 10.1109/ICoICT.2014.6914074.

H. Prasetyo and B. A. Putra Akardihas, “Batik image retrieval using convolutional neural network,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 17, no. 6, pp. 3010–3018, 2019, doi: 10.12928/TELKOMNIKA.v17i6.12701.

M. Sharma and A. Batra, “Analysis of Distance Measures in Content Based Image Retrieval,” Glob. J. Comput. Sci. Technol., vol. 14, no. 2, pp. 11–16, 2014.

N. M. Varma and A. Choudhary, “Evaluation of Distance Measures in Content Based Image Retrieval,” Proc. 3rd Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2019, vol. 2, no. 2, pp. 696–701, 2019, doi: 10.1109/ICECA.2019.8821957.

M. Muchtar, “Penggabungan Fitur Dimensi Fraktal dan Lacunarity untuk Klasifikasi Daun,” Institut Teknologi Sepuluh Nopember, Surabaya, 2015.

L. M. G. Jaya, M. Muchtar, S. Sarimuddin, and S. H. Idrus, “Enhancing Accuracy in Detection and Counting of Islands Using Object-Based Image Analysis: A Case Study of Kepulauan Seribu, DKI Jakarta,” Indones. J. Geogr., vol. 56, no. 2, 2024, doi: https://doi.org/10.22146/ijg.82037.

S. Rifky et al., Artificial Intelligence: Teori dan Penerapan AI di Berbagai Bidang. PT. Sonpedia Publishing Indonesia, 2024.

M. Muchtar and R. A. Muchtar, “Perbandingan Metode KNN dan SVM Dalam Klasifikasi Kematangan Buah Mangga Berdasarkan Citra HSV dan Fitur Statistik,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 2, pp. 876–884, 2024.

M. Muchtar, Y. P. Pasrun, R. Rasyid, N. Miftachurohmah, and M. Mardiawati, “PENERAPAN METODE NAÏVE BAYES DALAM KLASIFIKASI KESEGARAN IKAN BERDASARKAN WARNA PADA CITRA AREA MATA,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 1, Jan. 2024, doi: 10.23960/jitet.v12i1.3879.

Irma, M. Muchtar, R. Adawiyah, and Sarimuddin, “Klasifikasi tingkat kematangan cabai merah keriting menggunakan svm multiclass berdasarkan ekstraksi fitur warna,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 3, pp. 1747–1755, 2024.

L. Amatullah, I. Ein, and M. M. Santoni, “Identifikasi Penyakit Daun Kentang Berdasarkan Fitur Tekstur dan Warna Dengan Menggunakan Metode K-Nearest Neighbor,” Semin. Nas. Mhs. Ilmu Komput. dan Apl., no. April, pp. 783–791, 2021.

M. Muchtar, “Classification of Chicken Meat Freshness Based on YCbCr Color and Fractal Features Using KNN Method,” semanTIK, vol. 10, no. 1, pp. 43–50, 2024, doi: http://dx.doi.org/10.55679/semantik.v10i1.47238.

M. Muchtar and R. A. Muchtar, “Integrasi fitur warna, tekstur dan renyi fraktal untuk klasifikasi penyakit daun kentang menggunakan linear discriminant analysis,” J. Mnemon., vol. 7, no. 1, pp. 77–84, 2024.

M. Muchtar, N. Suciati, and C. Fatichah, “Fractal Dimension and Lacunarity Combination for Plant Leaf Classification,” J. Ilmu Komput. dan Inf., vol. 9, no. 2, p. 96, Jun. 2016, doi: 10.21609/jiki.v9i2.385.

M. Faisal, E. M. Zamzami, and Sutarman, “Comparative Analysis of Inter-Centroid K-Means Performance using Euclidean Distance, Canberra Distance and Manhattan Distance,” J. Phys. Conf. Ser., vol. 1566, no. 1, 2020, doi: 10.1088/1742-6596/1566/1/012112.

Y. Zheng et al., “Histopathological Whole Slide Image Analysis Using Context-Based CBIR,” IEEE Trans. Med. Imaging, vol. 37, no. 7, pp. 1641–1652, 2018, doi: 10.1109/TMI.2018.2796130.

A. Minarno and N. Suciati, “Batik 300,” Mendeley Data, vol. V1, 2022, doi: 10.17632/vz7pzt2grf.1.

M. Muchtar, M. N. Sutoyo, A. Paliling, Sunyanti, and J. N. Iin, “Penerapan analisis berbasis fraktal dalam klasifikasi citra retakan pada permukaan jembatan beton,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 9, no. 1, pp. 21–29, 2024.

S. Sarimuddin, M. Muchtar, Y. P. Pasrun, L. A. F. Hasidu, and R. Riska, “Penentuan Tingkat Kesehatan Komunitas Mangrove Secara Otomatis Menggunakan Otsu Thresholding,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 6, no. 1, pp. 30–39, 2024.

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Published
2024-10-12
How to Cite
Muchtar, M., Zainuddin, N., Sajiah, A. M., Ningsi, N., & Pasrun, Y. P. (2024). PERBANDINGAN JARAK EUCLIDEAN, CITYBLOCK, MINKOWSKI, CANBERRA, DAN CHEBYSHEV DALAM SISTEM TEMU KEMBALI CITRA BATIK. Jurnal Informatika Dan Teknik Elektro Terapan, 12(3S1). https://doi.org/10.23960/jitet.v12i3S1.5324

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