Judul: Pemutuan belimbing manis dengan citra pelatihan tunggal menggunakan algoritme VF15 berbasis histogen warna
Abstrak: Information on starfruit grading is necessary to professional distributor to give the best service. One of modern technique is digital image processing which using image and manipulating it to get one or more information. RGB, HSV, and YCbCr are standard models used in various color imaging applications. This research uses histogram of RGB, HSV, and YCbCr models and to show that not all of information is effective to classify starfruit. Image histogram is the basic for numerous spatial domain processing technique. Image histogram is used as input to Voting Feature Interval 5 (VFI5) algorithm. Histogram will be divided into several intervals and On training stage, it will be determined vote value from single training image per class. The vote value is used to classify testing data. This research shows that R, H, and Cr give the best accuracy to classify testing data with accuracy reaches 90.20%.
Keyword: Starfruit classification, color models, Voting Feature Interval 5 (VFI5), single training image

Judul: Pengembangan Model Probabilistic Neural Networks untuk Sortasi Belimbing Manis Berdasarkan Citra RGB
Abstrak: Starfruits (Averrhoa Carambola L.) is one of agroproducts which is liked by most society. Typically it is seen like a star when sliced athwartly. The characteristics of starfruits type which pre-eminent are size big, colour draw, smooth fibre, water containment, and fresh sweet. One of the vital process in post-harvest treatment is sortation. Fruits are grouped according to size measure and ripe phase. Starfruits which marketed have to fulfill the standard which accepted by consumer widely, either in domestic market and also global market. This matter in order to give best quality and also improve product competitiveness by farmer. Determination of starftruits sweetness is generally based on fruit colour visually. But, change of colour gradually complicated determination the taste of fruit. This needs more intelligent sortation methods and tools that overcome the sortcomings of manual process. Probabilistic Neural Network (PNN) is one of Artificial Neural Network (ANN) variants that can be used to develop a computer-based sortation engine for starfruits. This research aim is to develop PNN model for starfruits sortation based on RGB image value. Conclusion at this research indicates that PNN can be used to classify starfruits with high accuration reach 92%.
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Judul: Enteric Collibacillosis Pada Anak Babi Baru Lahir: Patogenesa dan Perubahan-Perubahan Patologisnya
Abstrak: Diare disebabkan oleh infeksi bakteri Escherichia coli pada anak-anak babi baru lahir yang peka terhadap en terotoksin produk bakteri tersebut. Dikenal sebagai colibacillosis. Patogenesa dari colibacillosis anak babi baru lahir adalah (1) infeksi oleh E. coli patogen, (2) proliferasi E. coli di lumen usus halus, (3) produksi dan pelepasan enterotoksin, dan (4) aktifitas toksin pada usus halus. Dalam proses proliferasi, adanya antigen pili K88 sangat penting karena memungkinkan bakteri perlekatan dengan epitel mucosa usus halus. mengadakan Setelah ko lonisasi dan proliferasi segera toksin dilepaskan. Akti- fitas toksin menginduksi sekresi yang berlebihan dari u- sus sehingga banyak cairan yang dikeluarkan ke lumen usus. Hal ini mengakibatkan banyak cairan dan elektrolit hilang sehingga timbul diare, dehidrasi dan asidosis yang sering menyebabkan kematian. Perubahan-perubahan patologis yang umum adalah dis tensi usus halus, dilatasi lambung dan infark pada kurva- tura mayor. Perubahan-perubahan histologis yang terjadi adalah enteritis hemorrhagica acuta, oedem dan kongesti pembuluh darah di lamina propria, akumulassi leukosit inti polimorf, sel monosit, dan inti sel mengalami piknosis Thrombus pada vena dan degenerasi serta desquamasi sel epitel usus….
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