Judul: Development of non destructive grading for export mangosteen using neural network
Abstrak: Grading of mangosteen for export in Indonesia, so far, is done manually based on visual evaluation. The practices cause ununi form quality, since theinside defects of fruits are undetected. Human errors also occur frequently due to the fatigue of the workers. This research aimed to develop a non destructive grading system to evaluate mangosteen quality for export using neural network. Input data for neural network were parameters obtained from image pr ocessing and ultrasonic measurement which had strong relationships with quality characteristics of mangosteen. The results indicated that area, red color index (r), blue color index (b), hue, saturation, intensity, and the velocity of ultrasonic wave were correlated with the physico-chemical characteristics of mangosteen. The accuracy level in evaluation of calyx wholeness using image processing was 100 %.
Keyword: Non destructive grading, Mangosteen, Neural network, Ultrasonic wavelength, Image processing, Pengolahan citra, Jaringan syaraf tiruan

Judul: Development of Mangosteen Maturity Classification Model on Color Based Using Fuzzy Neural Network.
Abstrak: Fuzzy Neural Network (FNN) has a capability to classify a pattern located within two different classes where a classical Neural Network (NN) is failed to do so. The fuzzy pattern classification is using membership degree on output of neuron as learning target. Objective of this research is to develop an artificial intelligence system model for non-destructive classification of fresh mangosteen using Fuzzy Neural Network. Component of color result in from image processing that influential against level of mangosteen’s maturity is used as input parameter. Percentage accuracy ratio of FNN model compare to NN for five, three, and two classification classes is 70:40, 86:65 and 90:90 respectively. The best result of FNN modeling is achieved on three class target classification (unripe, export and local) with green color index, value, a* u*, v*, entropy, contrast, energy and homogeinity as predictor parameters and 15 neurons hidden layer. Comparison of percentage capability of FNN against NN to identify the class is 100:0, 100:87 and 63:75.
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Judul: Infeksi Virus Parainfluenza Pada Anjing
Abstrak: Virus parainfluenza anjing termasuk famili Paramyxoviridae, berbentuk pleomorf dengan ukuran bervariasi (bergaris tengah antara 100-500 nm), berpembungkus peka ether, berisi genom RNA dan secara serologis mempunyai kesamaan dengan virus SV-5 kera; dikenak sebagai salah satu kausa penting dari sindrom "kennel cough". Infeksinya dapat mengenai anjing semua umur yang belum pernah terinfeksi atau mendapatkan vaksinasi mengakibatkan gejala klinis ringan yang terbatas pada traktus respirasi atu infeksi yang secara klinis tidak memperlihatkan gejala nyata. Gejala klinis yang nampak setelah masa inkubasi (1-5 hari) terlewati berupa batuk yang tidak berat selama beberapa hari, berbagai tingkat tonsilitis dan pharyngitis dan kadang-kadang ditemukan kenaikan temperatur rektal antara 1-2 derajat C dari temperatur normal rat-rata. Anjing terinfeksi mengandung virus pada traktus respirasi bagian atas dan bawah serta limfonoduli sampai hari ke 9 setelah infeksi. Penyebaran virus antar anjing terjadi secara aerogen.
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