Judul: Identifikasi Plat Nomor Menggunakan Fitur Zoning dengan Klasifikasi Support Vector Machine
Abstrak: Vehicle identification detection is one of the significant problems with the increasing number of vehicles. Therefore, a computer-based method is needed that can identify the vehicle based on license plate numbers quickly and accurately. Previous research have applied the image centroid and zone (ICZ) feature extraction method to identify vehicle license plates. In this research, ICZ and support vector machine (SVM) will be used for license plate identification. SVM which is used is the multi class SVM one against all using linear kernel, the polynomial, and RBF. The testing is performanced twice, on each character and on the overall plate (with or without fault tolerance). From the three kernels, the kernel which produces the best accuracy is the polynomial kernel with a value of C equals to 0.125 and d equals to 2 with on accuracy of 95.44%, while the accuracy produced at plate testing without fault tolerance is 81.54% and testing with fault tolerance equal to 1 is 90.77%
Keyword: number plate identification, multi class SVM one against all, kernel, image centroid and zone (ICZ)

Judul: Identifikasi Karakter Pada Plat Nomor Kendaraan Menggunakan Ekstraksi Fitur ICZ dan ZCZ dengan Metode Klasifikasi K-NN
Abstrak: License plate is a unique code consisting of letters and numbers arrangement. Each vehicle has a license plate that serve as the identity code of the vehicle itself. Since license plates are unique, it often used for recording, such as in the parking area. The character identification characters on the license plates are made automatically using the input in the form of license plate images to facilitate the recording process. K-NN classification and feature extraction ICZ and ZCZ used in the identification of character identification. ICZ and ZCZ feature extraction obtain features by computing the character centroids and divide the image into n equal zones then computing the average distance from the character centroid to every pixel in the image. The character identification accuracy using 14 zones and ICZ are 97.00% while using ICZ+ZCZ feature extraction are 98.17%. The license plate identification accuracy using feature extraction plates ICZ+ZCZ are 72.31% for zero tolerance and 89.23% for one fault tolerance.
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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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