Implementasi Projection Profile dan K-Nearest Neighbors dalam Pengenalan Tulisan Karakter Aksara Lampung
Article Sidebar
Main Article Content
Abstract
As one of the countries with a rich culture, Indonesia faces considerable challenges to preserve the diversity of its languages and writings. Lampung Province is one of the regions that has its language and script. However, over time, the variety of writings in the form of the Lampung script began to be forgotten because this variety of writings was not often used as a means of daily communication. One effort can be done to digitize the Lampung script, using the character recognition method using the projection profile as the feature extraction method, and K-Nearest Neighbors (KNN) algorithm to predict character shape. This study has two types of data: training data and testing data with 18-character labels. The comparison of the training and testing data used is 75.56% for testing data, and 24.43% for testing data. The features used are features obtained from image extraction measuring 20 x 20 pixels using a projection profile. The types of features obtained are Horizontal Projection Profile (HPP), Vertical Projection Profile (VPP), and Combined Projection Profile (CPP) which are obtained by combining HPP and VPP features. KNN classification is carried out with 10-fold cross-validation, using a base model and an optimized model with GridSearchCV. The best classification results are obtained using the Combined Projection Profile (CPP) feature with an optimized model using GridSearchCV. The evaluation results obtained for the best image data classification with an accuracy of 86.23%, precision of 86.23%, the sensitivity of 86.23%, specificity of 99.19%, F-Measure of 86.23%, and Matthew Correlation Coefficient (MCC) of 85.09%.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
A. Andana, R. Widyati, & M. Irzal, “Pengenalan Citra Tulisan Tangan Dengan Metode Backpropagation,” Jurnal Matematika Terapan, vol. 2, no. 1, pp. 36–44, 2018.
M. K. Mahto, K. Bhatia, & R. K. Sharma, “Combined horizontal and vertical projection feature extraction technique for Gurmukhi handwritten character recognition,” Conference Proceeding - 2015 International Conference on Advances in Computer Engineering and Applications, ICACEA 2015, pp. 59–65, 2015.
B. D. Puspasari, “Aplikasi Pengenalan Huruf Alfabet Bagi Anak Usia Dini Menggunakan Metode Pengolahan Citra Berbasis Data Suara,” Jurnal ELTEK, vol. 11, no. 01, pp. 1693–4024, 2013.
A. Junaidi, “Lampung Handwritten Character Recognition,” Dissertation, Technical University Dortmund, Dortmund, 2016.
A. M. M. O. Chacko & P. M. Dhanya, “A Comparative Study of Different Feature Extraction Techniques for Offline Malayalam Character Recognition,” Smart Innovation, Systems and Technologies, vol. 32, 2015, pp. 9–18.
L. Likforman-Sulem, A. Zahour, & B. Taconet, “Text line segmentation of historical documents: A survey,” International Journal on Document Analysis and Recognition, vol. 9, no. 2–4, pp. 123–138, 2007.
J. Han, M. Kamber, & J. Pei, Data Mining: Concepts and Techniques, Amsterdam: Elsevier, 2012.
X. Wu & V. Kumar, The Top Ten Algorithms in Data Mining-Data Mining and Knowledge Discovery, Florida: CRC Press, 2009.
W. Wahyono & A. Nugroho, “Penerapan Algoritma C4.5 Untuk Prediksi Tingkat Kompetensi Karyawan Pt Multistrada Arah Sarana,” Joutica, vol. 3, no. 1, p. 145, 2018.
P. Cattin, “Image Restoration Introduction to Signal and Image Processing,” MIAC, vol. 9, no. 19, pp. 1–70, 2016.
N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans Syst Man Cybern, vol. 9, no. 1, pp. 62–66, Jan. 1979.
G. X. Ritter & J. N. Wilson, Handbook of Computer Vision Algorithms in Image Algebra, Florida: CRC Press, 2000.
R. Szeliski, Computer Vision: Algorithms and Applications 2nd Edition, New York: Springer, 2021.
J. Memon, M. Sami, R. A. Khan, & M. Uddin, “Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR),” IEEE Access, vol. 8, pp. 142642–142668, 2020, doi: 10.1109/ACCESS.2020.3012542.
A. Kacem, N. Aouiti, & A. Belaïd, “Structural features extraction for handwritten Arabic personal names recognition,” in Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR, 2012, pp. 268–273. doi: 10.1109/ICFHR.2012.276.
B. M. Vinjit, M. K. Bhojak, S. Kumar and G. Chalak, "A Review on Handwritten Character Recognition Methods and Techniques," 2020 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2020, pp. 1224-1228, doi: 10.1109/ICCSP48568.2020.9182129.
B. Amaliah, A. Yuniarti, A. S. Nugroho, & A. Z. Arifin, “Pemisahan Gigi Pada Dental Panoramic Radiograph Dengan Menggunakan Integral Projection yang di Modifikasi,” Jurnal ilmiah KURSOR, vol. 6, no. 2, pp. 121–128, 2011.
J. L. Bentley, “Multidimensional Binary Search Trees Used for Associative Searching,” Commun ACM, vol. 19, 1975.
P. Refaeilzadeh, L. Tang, & H. Liu, “Cross-Validation,” in Encyclopedia of Database Systems, Boston, MA: Springer US, 2009, pp. 532–538.
M. Bekkar, H. K. Djemaa, & T. A. Alitouche, “Evaluation Measures for Models Assessment over Imbalanced Data Sets,” Journal of Information Engineering and Applications, vol. 3, no. 10, pp. 27–38, 2013.