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 determine the heterogeneity of a texture image. Lacunarity was not really exploited in many fields. Moreover, 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 implemented 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. Keywords: leaf classification, fractal dimension, lacunarity, box counting, gliding box AbstrakTumbuhan memegang peranan penting dalam kehidupan manusia. Tingginya keberagaman spesies tumbuhan membuat metode pengamatan manual dalam klasifikasi daun menjadi 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 dieksplorasi dalam banyak kasus dan belum ada usaha yang cukup signifikan dalam mengkombinasikan dimensi fraktal dan lacunarity dalam bidang klasifikasi tumbuhan secara otomatis. Penelitian ini berfokus pada ekstraksi dan kombinasi fitur dimensi fraktal dan lacunarity untuk meningkatkan akurasi klasifikasi. 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 daun. 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%.
Plant takes a crucial part in mankind existences. The development of digital image processing technique made the plant classification task become a lot of easier. Leaf is a part of plant that can be used for plant classification where texture of the leaf is a common feature that been used for classification process. Texture offers a unique feature and able to work even when the leaf is damaged or overly big in size which sometimes made the acquisition process become more difficult. This study offers a combination of Gabor filter methods and co-occurrence matrices to produce the most representative features for leaf classification. Classification using SVM with 5-fold cross validation system shows that the proposed Gabor Co-Occurence methods was able to reach average accuracy up to 89.83%. Terms: Leaf, Gabor Co-occurence, Support Vector Machine, Texture
Technological developments have enabled the use of vehicle number plate recognition in various fields such as parking systems, e-toll payments, handling motor vehicle theft cases, traffic violations and so on. Vehicle number plate characteristics in each country have characteristics that are not the same. Each country has a standard numbering system in its writing method, colour, language and size of the license plate, so that to take the research standards for license plate numbers from other countries is not effective. Special research is needed in accordance with the format of the number plate used by the country, in this case Indonesia. This research applies simple method, morphological operations in detecting vehicle plates in Indonesia. Morphological results will be segmented to get the image of the plate character separately. The test results show that the method applied can provide a maximum accuracy of up to 100% on taking plate images with a distance of 1.5 meters.
Indonesia is an archipelago country with widest mangrove areas in the world. This potential is supported by length the coastline reaches + 81,000 km. As one of city in Indonesia, Kendari surrounded by the bay with temperatures reaching 22-31°C makes it potential to has mangrove forests. But the mangrove areas in Kendari decrease by 30% in recent year. This study aims to determine the level of mangrove fertility so we hope in future we could identify potential area Mapping for Mangrove in Kendari to increase more mangrove area. To analyze the quality of mangroves area through remote sensing data can be used Normalized Difference Vegetation Index (NDVI) method. By utilizing Landsat 7.0 ETM + image can make it easier to get information without touching the object of research. This experiment used fuzzy logic method (Fuzzy Mamdani) to get fertility rate of land soil. The result showed the highest fertility rate of mangrove area is 58,2034 which categorized as Very Good and the lowest score is 36,991 which categorized as Average.
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