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 hidden markov model untuk pengenalan tingkat kemanisan buah belimbing
Abstrak: Starfruit (Averrhoa Carambola L) is one of agroproducts which is useful for many people. The fruit looks like a star when it is sliced athwartly. In post-harvest, one of the most important treatment is to classify the fruits based on its sugariness. This treatment aim is to give the best quality information of fruits. In this research, the characteristics of starfruit that can be used to indicate its sugariness is the color intensity. Fruits are grouped by size and ripe phase. In each group, starfruit pictures are analyzed based on their components, (red), (green), and (blue). These components are used to determine the total soluble solids using linear regression. Then the total soluble solids are used in order to determine the sugariness of the fruits. Certainly, an intelligent method is needed to solve this problem. Hidden markov model (HMM) is one the most popular method to recognize some instance in pattern recognition. By using HMM, the sugariness of the fruits can be detected. In order to compare accuracy of HMM, this research uses some different HMMs based on its sum of the hidden state. Finally, conclusion of this research is that the HMM gives good result to recognize the starfruit sugariness and it reaches the maximum accuracy of 75%.
Keyword: Starfruit, Linear regression, Hidden markov model(HMM), Bogor Agricultural University

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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