Pembentukan Pola Desain Motif Karawo Gorontalo Menggunakan K-Means Color Quantization dan Structured Forest Edge Detecion


Syahrial Syahrial, Rizal Lamusu


Sulaman Karawo merupakan kerajinan tangan berupa sulaman khas dari daerah Gorontalo. Motif sulaman diterapkan secara detail berdasarkan suatu pola desain tertentu. Pola desain digambarkan pada kertas dengan berbagai panduannya. Gambar yang diterapkan pada pola memiliki resolusi sangat rendah dan harus mempertahankan bentuknya. Penelitian ini mengembangkan metode pembentukan pola desain motif Karawo dari citra digital. Proses dilakukan dengan pengolahan awal menggunakan k-means color quantization (KMCQ) dan deteksi tepi structured forest. Proses selanjutnya melakukan pengurangan resolusi menggunakan metode pixelation dan binarization. Luaran dari algoritma menghasilkan 3 citra berbeda dengan ukuran yang sama, yaitu: citra tepi, citra biner, dan citra berwarna. Ketiga citra tersebut selanjutnya dilakukan proses pembentukan pola desain motif Karawo dengan berbagai petunjuk pola bagi pengrajin. Hasil menunjukkan bahwa pola desain motif dapat digunakan dan dimengerti oleh para pengrajin dalam menerapkannya di sulaman Karawo. Pengujian nilai-nilai parameter dilakukan pada metode k-means, gaussian filter, pixelation, dan binarization. Parameter-parameter tersebut yaitu: k pada k-means, kernel pada gaussian filter, lebar piksel pada pixelation, dan nilai threshold pada binarization. Pengujian menunjukkan nilai terendah tiap parameter adalah k=4, kernel=3x3, lebar piksel=70, dan threshold=20. Hasil memperlihatkan makin tinggi nilai-nilai tersebut maka semakin baik pola desain motif yang dihasilkan. Nilai-nilai tersebut merupakan nilai parameter terendah dalam pembentukan pola desain motif berkualitas baik berdasarkan indikator-indikator dari desainer.



Karawo embroidery is a unique handicraft from Gorontalo. The embroidery motif is applied in detail based on a certain design pattern. These patterns are depicted on paper with various guides. The image applied to the pattern is very low resolution and retains its shape. This study develops a method to generate a Karawo design pattern from a digital image. The process begins by using k-means color quantization (KMCQ) to reduce the number of colors and edge detection of the structured forest. The next process is to change the resolution using pixelation and binarization methods. The output algorithm produces 3 different state images of the same size, which are: edge image, binary image, and color image. These images are used in the formation of the Karawo motif design pattern. The motif contains various pattern instructions for the craftsman. The results show that it can be used and understood by the craftsmen in its application in Karawo embroidery. Testing parameter values on the k-means method, Gaussian filter, pixelation, and binarization. These parameters are k on KMCQ, the kernel on a gaussian filter, pixel width in pixelation, and threshold value in binarization. The results show that the lowest value of each parameter is k=4, kernel=3x3, pixel width=70, and threshold=20. The results show that the higher these values, the better the results of the pattern design motif. Those values are the lower input to generate a good quality pattern design based on the designer’s indicators.

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ACHANTA, R., SHAJI, A., SMITH, K., LUCCHI, A., FUA, P. AND SÜSSTRUNK, S., 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), pp.2274–2281.

ALBAYRAK, S., 2001. Color Quantization by Modified K-Means Algorithm. Journal of Applied Sciences, .

AZIZ, N.S. AND KAMALUDIN, A., 2016. 8 Colour Quantization of Colour Construct Code in CIELAB Colour Space Using K-Means Clustering and Hungarian Assignment. In: Advanced Computer and Communication Engineering Technology, Lecture No. [online] Switzerland: Springer International Publishing.pp.671–681. Available at: .

BAIHAQI, W.M., PINILIH, M. AND ROHMAH, M., 2020. Kombinasi K-Means dan Support Vector Machine (SVM) untuk Memprediksi Unsur SARA pada Tweet. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), [online] 7(3), pp.501–510. Available at: .

CELEBI, M.E., 2009. Fast color quantization using weighted sort-means clustering. Journal of the Optical Society of America A, 26(11), p.2434.

CELEBI, M.E., 2011. Improving the performance of k-means for color quantization. Image and Vision Computing, [online] 29(4), pp.260–271. Available at: .

CELEBI, M.E., WEN, Q., HWANG, S. AND SCHAEFER, G., 2013. Color quantization of dermoscopy images using the K-means clustering algorithm. Lecture Notes in Computational Vision and Biomechanics, 6, pp.87–107.

CHEN, T.W., CHEN, Y.L. AND CHIEN, S.Y., 2008. Fast image segmentation based on K-means clustering with histograms in HSV color space. Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008, pp.322–325.

CHENG, G. AND WEI, J., 2019. COLOR QUANTIZATION APPLICATION BASEd on K-Means in Remote Sensing Image Processing. Journal of Physics: Conference Series, 1213(4).

DISTANTE, A. AND DISTANTE, C., 2020. Handbook of Image Processing and Computer Vision. Cham, Switzerland: Springer Nature Switzerland.

DOLLAR, P. AND ZITNICK, C.L., 2013. Structured Forests for Fast Edge Detection. In: 4th IEEE International Conference on Computer Vision (ICCV ’13). Sydney, Australia: IEEE Xplore.pp.1841–1848.

GERSTNER, T., DECARLO, D., ALEXA, M., FINKELSTEIN, A., GINGOLD, Y. AND NEALEN, A., 2013. Pixelated image abstraction with integrated user constraints. Computers and Graphics (Pergamon), [online] 37(5), pp.333–347. Available at: .

GERSTNER, T., DECARLO, D., ALEXA, M., FINKELSTEIN, A., GINGOLD, Y. AND NEALEN, A., 2012. Pixelated image abstraction. Proceedings of the international symposium on non- photorealistic animation and rendering (NPAR), [online] pp.29–36. Available at: .

HASDIANA., ADIATMONO, F.. AND NAINI, U., 2013. Peningkatan Brand Image Kerawang Melalui Penciptaan Desain Ragam Hias Kreatif Beridentitas Kultural Budaya Gorontalo Untuk Mendukung Industri Kreatif. [online] Gorontalo, Indonesia. Available at: .

HASDIANA, NAINI, U., MOHAMAD, I. AND MALANUA, N., 2019. Engineering Design of Traditional Gorontalo Motif for Learning Karawo Embroidery. In: 1st International Conference on Education, Social Sciences and Humanities (ICESSHum 2019) - Advances in Social Science, Education and Humanities Research. [online] Atlantic Press.pp.327–332. Available at: .

HU, Z. AND URAHAMA, K., 2014. Cartesian resizing of line drawing pictures for pixel line arts. IEICE Transactions on Information and Systems, E97-D(4), pp.1008–1010.

INGLIS, T.C. AND CRAIG, A., 2012. Pixelating Vector Line Art. In: E. Association, ed. Proceedings of the Symposium on Non-Photorealistic Animation and Rendering. GoslarGermany: Eurographics Association.pp.21–28.

JIANG, Y., WANG, Y., JIN, L., GAO, H. AND ZHANG, K., 2011. Investigation on Color Quantization Algorithm of Color Image. In: International Conference on Electronic Commerce, Web Application, and Communication. Guangzhou, China: Springer-Verlag Berlin Heildelberg.pp.181–187.

KOPF, J. AND LISCHINSKI, D., 2011. Depixelizing Pixel Art. ACM Transactions on Graphics, 30(4), pp.1–8.

PALUS, H. AND FRACKIEWICZ, M., 2013. Colour quantisation as a preprocessing step for image segmentation. Lecture Notes in Computational Vision and Biomechanics, 8, pp.119–138.

PALUS, H. AND FRACKIEWICZ, M., 2019. Deterministic vs. random initializations for K-Means color image quantization. Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019, pp.50–55.

PARK, C., YANG, H., KWON, H. AND MIN, K., 2018. Generating Pixel Art from Game Characters with Convolutional-Neural Network. Journal of The Korean Society for Computer Game, 31(2), pp.17–24.

ROSE, K., 1998. Deterministic Annealing for Clustering , Compression , Classification , Regression , and Related Optimization Problems. In: Proceedings of the IEEE.

SCORPY, 2015. Free Vector Images. [online] Available at: [Accessed 17 Jul. 2020].

SERPA, Y.R. AND RODRIGUES, M.A.F., 2019. Towards Machine-Learning Assisted Asset Generation for Games: A Study on Pixel Art Sprite Sheets. In: 18th Brazilian Symposium on Games and Digital Entertainment (SBGAMES). Rio de Janeiro, Brazil: IEEE.

SUDANA, I.W., 2019. Dinamika Perkembangan Seni Karawo Gorontalo. Gelar : Jurnal Seni Budaya, 17(1), pp.31–43.

SUMIJAN, S.S., PURNAMA, A.W. AND ARLIS, S., 2019. Peningkatan Kualitas Citra CT-Scan dengan Penggabungan Metode Filter Gaussian dan Filter Median. Jurnal Teknologi Informasi dan Ilmu Komputer, 6(6), p.591.

TAKIMOTO, H., YOSHIMORI, S. AND MITSUKURA, Y., 2012. Method for automatic generation of pixel art based on color-difference tolerance. Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers, 66(11).

THOMPSON, S., CELEBI, M.E. AND BUCK, K.H., 2020. Fast color quantization using MacQueen’s k-means algorithm. Journal of Real-Time Image Processing, [online] 17(5), pp.1609–1624. Available at: .

TUKEY, J.W., 1977. Exploratory Data Analysis. Addison-Wesley.