Penggabungan fitur dimensi fraktal dan lacunarity untuk klasifikasi daun

Muchtar, Mutmainnah (2015) Penggabungan fitur dimensi fraktal dan lacunarity untuk klasifikasi daun. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Tanaman memegang peranan penting dalam kehidupan manusia dan makhluk hidup lainnya. Dengan semakin tingginya keanekaragaman spesies tanaman di dunia, sulit untuk mengidentifikasi atau mengklasifikasi tanaman secara manual melalui pengamatan langsung. Perkembangan penelitian di bidang pengolahan citra digital telah membuka kesempatan luas bagi banyak peneliti di berbagai bidang penelitian untuk mengklasifikasi tanaman secara cepat dan otomatis. Daun merupakan bagian pada tanaman yang paling sering digunakan dalam klasifikasi tanaman, baik secara manual maupun otomatis. Melalui pengamatan pada daun, beberapa karakteristik bisa diperoleh; di antaranya adalah bentuk pinggiran daun, bentuk urat daun serta tekstur daun. Banyak objek-objek di alam memiliki sifat yang mirip fraktal, dimana terdapat pola yang berulang pada skala tertentu, termasuk pada objek seperti daun. Dimensi fraktal merupakan deskriptor fitur bentuk maupun tekstur yang telah banyak diterapkan pada berbagai bidang penelitian karena mampu mendeskripsikan kompleksitas sebuah objek dalam bentuk dimensi pecahan. Sementara itu, lacunarity, merupakan deskriptor fitur tekstur yang mampu menunjukkan seberapa heterogen suatu citra tekstur. Namun lacunarity belum cukup dieksplorasi dalam banyak bidang penelitian dan belum ada penelitian signifikan yang mencoba menggabungkan fitur dimensi fraktal dengan lacunarity dalam penelitian yang berfokus pada klasifikasi citra digital daun. Pada penelitian ini, diajukan penerapan konsep fraktal dalam menyelesaikan masalah klasifikasi daun dengan berfokus pada penggabungan fitur dimensi fraktal dan lacunarity. Ekstraksi fitur bentuk pinggiran dan tulang daun dilakukan melalui perhitungan dimensi fraktal dengan menerapkan metode box counting. Sedangkan fitur hasil perhitungan nilai lacunarity diperoleh melalui proses ekstraksi fitur tekstur daun dengan menerapkan metode gliding box. Menggunakan 626 dataset dari flavia, pengujian dilakukan dengan menganalisis performa dari dimensi fraktal dan lacunarity ketika digunakan secara terpisah dan ketika dikombinasikan satu sama lain dalam memperbaiki hasil klasifikasi daun dari metode fraktal sebelumya, serta dengan mempertimbangkan parameter ukuran kotak r yang paling optimal. Hasil uji coba dengan pengklasifikasi support vector machine menunjukkan bahwa penggabungan fitur dimensi fraktal dan lacunarity mampu meningkatkan akurasi klasifikasi hingga 93.92 % ============================================================================================ Plant plays an important role in the existence of all beings in the world. With the high diversity in plant species, it is hard to classify plant manually only by observing their properties. The development of study in digital image processing opened a wide chance for many researches from various area of study to quickly and automatically classify plant species. Plant leaf was the main properties that commonly used in plant classification whether it is manually or automatically. By looking at plant leaf, some unique characteristics can be obtained; between them were leaf contour shape, leaf vein shape, and leaf surface texture. There are many natural objects and phenomenons that have characteristic of fractals, like a pattern that repeated in a certain scale, including natural objects like plant leaf. Fractal dimension was a widely known feature descriptor for shape or texture that able to describe the complexity of an object in a form of fractional dimension’s value. On the other hand, lacunarity is a feature descriptor that able to describe the heterogeneity of a texture image. However, lacunarity was not really exploited in many fields and there are no significant efforts that trying to combine fractal dimension and lacunarity in the study of automatic plant leaf classification. In this study, a fractal concept and its performances in leaf classification will be analyzed by using two fractal based feature: fractal dimension and lacunarity. We focused on how to extract the two features and combine them for a better classification result. A box counting approach is implemented to get the fractal dimension feature vectors of leaf contour and vein, while an improved gliding box algorithm is implemented to get the lacunarity feature vectors of leaf texture. By combining this two feature, a feature vectors that highly represents the unique feature of each leaf is then expected to be obtained. Using 626 leaf images from flavia, experiment was conducted by separately or jointly analyzing the performace of both fractal dimension feature vectors and lacunarity feature vectors, while considering the optimal box size r. Using support vector machine classifier, result shows that combination between fractal dimension and lacunarity was able to increase the classification accuracy up to 93.92%.

Item Type: Thesis (Masters)
Additional Information: RTIf 004.42 Muc p
Uncontrolled Keywords: klasifikasi daun; dimensi fraktal; lacunarity; box counting; gliding box
Subjects: Q Science > Q Science (General) > Q337.5 Pattern recognition systems
Divisions: Faculty of Information Technology > Informatics Engineering > 55101-(S2) Master Thesis
Depositing User: - Taufiq Rahmanu
Date Deposited: 28 Oct 2019 03:00
Last Modified: 28 Oct 2019 03:00
URI: http://repository.its.ac.id/id/eprint/71435

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