Judul: Comparison of centroid contour gradient and centroid contour distance algorithm for leaf shape recognition
Abstrak: Penelitian ini membandingkan algoritme CCG (Centroid Contour Gradient) dan CCD (Centroid Contour Distance) untuk ekstraksi fitur dalam pengenalan bentuk daun. CCG dan CCD adalah algoritme untuk merepresentasikan bentuk dengan pendekatan berbasis kontur (contour-based). CCG menghitung nilai gradient antar titik sepanjang tepi daun pada setiap interval sudut tertentu sedangkan CCD menghitung jarak titik tengah terhadap titik-titik tepi. Bentuk daun yang digunakan dalam penelitian ini adalah ellips, cordate, ovate, dan lanceolate. Data yang digunakan sebanyak 200 citra daun dengan jumlah citra masing-masing kelas sebanyak 50. Probabilistic Neural Network digunakan untuk mengklasifikasi bentuk daun. Didapatkan akurasi terbaik CCD sebesar 96.67%, jauh lebih besar dibanding akurasi terbaik CCG sebesar 60.00%., This research compares the CCG (Centroid Contour Gradient) and CCD (Centroid Contour Distance) algorithms for feature extraction in leaf shape recognition. CCG and CCD are algorithm of shape representation based on contour. Contour is an important cue for object recognition. CCG calculates gradient between pairs of boundary points corresponding to interval angle while the CCD calculates the distance between the midpoint and the boundary points. Leaf shapes that used in this study are elliptical, cordate, ovate, and lanceolate. We used 200 Indonesian tropical leaf images. Each class consists of 50 images. Probabilistic Neural Network (PNN) is used to classify leaf shape. The experimental result shows that CCD has better accuracy than CCG. The accuracy achieved by CCD and CCG are 96.67 % and 60.00% respectively.
Keyword: Bentuk daun, Centroid Contour Gradient, Centroid Contour Distance

Judul: Penggabungan ciri morfologi, tekstur, dan bentuk untuk identifikasi daun menggunakan Probabilistic Neural Network
Abstrak: This research proposes a new system for leaf identification using classifier combination and probabilistic neural network. The features that used for classification are morphology, texture, and shape of leaves. Classifiers combination is used to combine these features. The method to extract texture and shape are co-occurrence matrix and Fourier descriptors, respectively. After feature extraction, the feature is classified using probabilistic neural network. The experiment data consist of thirty species flora from Bogor Botanical Garden, Indonesia. The experiment results show that classification without classifier combination has the accuracy of 79.05%, and using classifier combination the accuracy increased to 83.33%. Hence, the propose system is promising for leaf identification and supporting plant biodiversity in Indonesia..
Keyword: Leaf identification; Probabilistic Neural Network; Classifier combination, Probabilistic Neural Network, Classifier combination, Bogor Agricultural University, Institut Pertanian Bogor, IPB

Judul: Sarcosporidiasis in slaughter animals
Abstrak: Sarcocystis which belong to the generally occuring parasites for they are found almost all over the countries of the world. Infection of Sarcocystis usually throught inges- tion of feces or flesh of infected animals. The pathogenesis of Sarcosporidiasis is considered as benign but when the Sarcocystis die in a host, sarcocystin or sarcosporidin is liberated thereby which lead to condemnation of meat in the abattoir. Besides condemnation of meat, Sarcocystis also causes: loss of appetite, anemia, lameness, weakness in the lion, diarrhea, fever, pyrexia, paralysis, emaciation, neuro logical disturbances, etc. " Amprolium "at the rate of 100 mg/Kg body weight per day has been practiced for prevention or to minimize Sarco- sporidiasis. Treatment must be started during incubation period. Shedding of Sarcocystis sporocysts in the feces of carnivores is the key factor in the spread of Sarcocystis infection. Therefore, control efforts must be based primarily on measures designed to break the cycle of Sarcocystis.
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