Judul: Klasifikasi Dokumen Menggunakan Metode Rocchio Dengan Pemilihan Fitur Menggunakan Chi-square
Abstrak: The drawback of document classification technique of a classification system is the significant increase of efforts when the contents of each document are large. One way to improve the performance of the classification system is by implementing the feature selection technique. This research implemented the Rocchio method as the document classifier and Chi-square as the feature selection technique at the significant level of 0.05 and 0.001. The document collections utilized in this research was taken from Reuters-21578 with 20 categories. The result showed that the effectiveness of classification in terms of F-measure and accuracy was insignificant. The highest F-measure value of 72.57% was achieved at a significant level of 0.001, or increasing 2.28% if compared with the previous F-measure value before feature selection method was applied. At a significant level of 0.05, the F-measure value was 72.42%. The obtained classification accuracy with feature selection technique was 83.67% at a significant level of 0.001 and 83.96% at a significant level of 0.05. These classification accuracies were higher than that without feature selection technique of 83.08%.
Keyword: chi-square, rocchio, feature selection, document classification

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: Perencanaan Lanskap Jalan Western By Pass di Kotamadya Denpasar, Bali
Abstrak: Jalan Western By Pass dengan panjang keseluruhan .t 13 km merupakan salah satu jalan yang akan dibangun di wilayah Denpasar bagian barat, digunakan untuk menghubungkan daerah-daerah di sebelah utara (Tabanan, Kintamani, Sangeh, dll) dan selatan Denpasar (Kuta dan Nusa Dua). Jalan yang berfungsi sebagai jalan Arteri Primer ini bersama-sama dengan Northern dan Southern By Pass akan membentuk jaringan Outer Ring Road yang mengelilingi Kotamadya Denpasar dan direncanakan selesai serta mulai beroperasi secara optimal pada tahun 2002.
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