Judul: Penggabungan Fitur Fuzzy Local Binary Pattern dan Fuzzy Color Histogram untuk Aplikasi Mobile Identifikasi Citra Tumbuhan Obat.
Abstrak: This research develops a mobile application based on Android operating system for identifying medicinal plant images. The identification is conducted based on texture and color feature. This research uses medicinal plants leaf images. The total medicinal plants used in this research is 51 species taken from Biofarmaka IPB, Cikabayan Farm, Green house Center Ex-Situ Conservation of Medicinal Plant Indonesia Tropical Forest and Kebun Raya Bogor. Each species consist of 48 images, thus the total image used in this research is 2448. This research investigates effectiveness of the fusion between the Fuzzy Local Binary Pattern (FLBP) and the Fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting medicinal plants texture. This method extends the Local Binary Pattern (LBP) approach by employing fuzzy logic for representing the local patterns of texture images. Fuzzification allows the distribution of the LBP values is used as a feature vector. Moreover, the FCH method is used for extracting medicinal plants color. This method considers the color similarity of each color pixel associated with all the histogram bins through fuzzy-set membership function. Next, Fuzzy C-Means (FCM) algorithm is used to compute the membership values. The fusion of FLBP and FCH is done by using Product Decision Rules (PDR) method. This research uses Probabilistic Neural Network (PNN) method to classify the FLBP and FCH feature vector. The experimental results show that the fusion between FLBP and FCH can improve the accuracy of medicinal plants identification (FLBP: 59.61%, FCH: 50.78%, fusion of FLBP and FCH: 74.51%).
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Judul: Aplikasi Mobile Untuk Identifikasi Tumbuhan Obat Menggunakan Local Binary Patterndengan Klasifikasi Probabilistic Neural Network
Abstrak: This research proposed a new mobile application for medicinal plants identification. Local Binary Pattern (LBP) is used to extract image features and Probabilistic Neural Network (PNN) is used to classify medicinal plants on mobile phone. LBPP,Rriu2 was used as LBP descriptor in this research, which uses rotation invariant and uniform patterns of image texture. The aim of this research was to implement this application in open source Android 2.3 (Gingerbread) operating system. The data used is the medicinal leaf image from Botanical Garden, Bogor Indonesia which consist of 15 species with 10 variations for each species. The experiment showed that the accuracy of identifications is 31.11% and the computation time is 50.02 seconds.The experimental result show the accuracy is still low, so this research need to be explored further to improve the accuracy. This new mobile application is useful to help user in plant identification.
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Judul: Solusi Geometri Schwarzschild untuk Dimensi 4+1 dengan Invarian Rotasi Isotropik
Abstrak: Metrik Schwarzschild merupakan solusi eksak pertama dari persamaan medan Einstein dengan mangasumsikan metrik statik dan simetris. Metrik Schwarzschild yang ditemukan oleh Karl Schwarzschild masih dalam dimensi 3+1, namun seiring dengan teori-teori terbaru yang menunjukkan bahwa alam semesta tidak hanya di dimensi 3+1. Maka melalui penambahan dimensi, tujuan penelitian ini memperoleh solusi pada geometri Schwarzschild untuk dimensi 4+1 dengan memodifikasi koordinat bola yang invarian rotasi isotropik dan melalui penurunan rumus seperti koordinat bola, simbol Christoffel, dan tensor Ricci. Selain mendapatkan solusi ini, metrik Schwarzschild dimensi 4+1 dapat digunakan sebagai metrik dasar untuk memperoleh solusi blackhole dan wormhole pada dimensi 4+1. Hasil akhirnya, dengan memanfaatkan modifikasi koordinat bola, metrik Schwarzschild dapat dibentuk sehingga diperoleh solusi geometri Schwarzschild untuk dimensi 4+1.
Keyword: metrik Schwarzschild, persamaan medan Einstein, modifikasi koordinat bola, penambahan dimensi, invarian rotasi isotropik