Judul: Klasifikasi Dokumen Berita Menggunakan Metode Support Vector Machine dengan Kernel Radial Basis Function
Abstrak: Every day the number of text documents, especially online news documents increase. As a result, it is more difficult for information seekers in obtaining the desired information. This problem requires a text processing technique that is able to automatically classify text documents based on predetermined categories. This technique is document classification. A very good and popular classification method is support vector machine (SVM). SVM tries to find the best hyperplane which separates 2 classes of data in a vector space. By applying kernel trick, SVM can implement classification in non-linear case. The goals of this research are applying the radial basis function kernel of SVM to classify Reuters-21578 news documents, and comparing the weighting method term frequency (tf) and term frequency-inverse document frequency (tf-idf). The research uses chi-square in feature selection, producing 1716 features out of 7279 terms from tokenization and stopwords removal. The final result shows that the SVM classification produces an accuracy of 93.21% using tf weighting and 92.97% using tf-idf weighting
Keyword: Bogor Agricultural University (IPB), support vector machine, radial basis function, feature selection, document classification, chi-square

Judul: Klasifikasi Dokumen Teks Menggunakan Metode Support Vector Machine dengan Pemilihan Fitur Chi-Square.
Abstrak: Increasing number of documents makes people more difficult to obtain the information which they desired. This problem requires text processing techniques to organize the documents in accordance with the categories. One of which is text classification. Text classification can organize document in accordance with predefined categories automatically (supervised machine learning). One popular method of text classification is support vector machines (SVM) that tries to find the best hyperplane in the input space. This algorithm is the best classification algorithm compared with other vector space classification method, namely Rocchio, k-nearest neighbor (KNN) and decision tree. This research measures the suitability of SVM for text classification and to prove whether the SVM is able to classify the documents in a linear separable manner. The final result shows that linear kernel and polynomial kernel in the SVM test produce the same accuracy value of 96.3504% and testing the RBF kernel produces accuracy of 95.6204% for classification of text documents using chi-squared feature selection
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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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