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 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
Keyword: 

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.
Keyword: