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Abstrak :
World Health Organisation (WHO) has predicted 300 million peoples will suffer of diabetic in 2025. Long-term diabetics can lead to diabetic retinopathy that can cause blindness in developing countries. One of the abnormalities of diabetic retinopathy is exudate. Exudates are classified into two categories, i.e. hard and soft exudates. This paper proposes feature extraction based on texture for distinguishing hard, soft and non-exudates. The green channel of the original images is enhanced by CLAHE and followed by median filtering and thresholding in red channel to detect and remove the optic disc. The enhanced image is segmented based on clustering to obtain the region of interest of exudates. Feature extraction based on texture is conducted by using GLCM and lacunarity. Results show that classification based on NaïveBayes algorithm achieves accuracy, specificity and sensitivity of 92.13%, 96% and 87.18%, respectively.

Abstrak :
Plants play important roles for the existence of all beings in the world. High diversity of plant?s species make a manual observation of plants classifying becomes very difficult. Fractal dimension is widely known feature descriptor for shape or texture. It is utilized to determine the complexity of an object in a form of fractional dimension. On the other hand, lacunarity is a feature descriptor that able to deter-mine the heterogeneity of a texture image. Lacunarity was not really exploited in many fields. More-over, there are no significant research on fractal dimension and lacunarity combination in the study of automatic plant?s leaf classification. In this paper, we focused on combination of fractal dimension and lacunarity features extraction to yield better classification result. A box counting method is implement-ed to get the fractal dimension feature of leaf boundary and vein. Meanwhile, a gliding box algorithm is implemented to get the lacunarity feature of leaf texture. Using 626 leaves from flavia, experiment was conducted by analyzing the performance of both feature vectors, while considering the optimal box size r. Using support vector machine classifier, result shows that combined features able to reach 93.92 % of classification accuracy.

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Tumbuhan memegang peranan penting dalam kehidupan manusia. Tingginya keberagaman spesies tumbuhanmembuat metode pengamatanmanual dalam klasifikasi daunmenjadi semakin sulit. Dimensi fraktal merupakan deskriptor bentuk dan tekstur yang mampu mendeskripsikan kompleksitas dari suatu objek dalam bentuk dimensi pecahan. Di sisi lain, lacunarity adalah deskriptor tekstur berbasis fraktal yang mampu mendeskripsikan heterogenitas dari citra tekstur. Namun lacunarity belum cukup dieks-plorasi dalam banyak kasus dan belum ada usaha yang cukup signifikan dalam mengkombinasikan di-mensi fraktal dan lacunarity dalam bidang klasifikasi tumbuhan secara otomatis. Penelitian ini berfokus pada ekstraksi dan kombinasi fitur dimensi fraktal dan lacunarity untuk meningkatkan akurasi klasi-fikasi. Metode box counting diterapkan untuk memperoleh dimensi fraktal dari bentuk pinggiran dan urat daun, sementara metode gliding box diterapkan untuk memperoleh fitur lacunariy dari tekstur da-un. menggunakan 626 citra daun dari flavia, percobaan dilakukan dengan menganalisis performa dari kedua fitur dengan mempertimbangkan ukuran kotak r yang paling optimal. Klasifikasi dengan support vector machine menunjukkan bahwa hasil kombinasi kedua fitur mampu mencapai rata-rata akurasi hingga 93.92%.