Judul: Klasifikasi dokumen bahasa Indonesia menggunakan metode Dynamic Classifier Selection with Local Accuracies
Abstrak: Dynamic classifier selection with local accuracies (DCS-LA) uses k-Nearest Neighbor (kNN) to combine multiple classifiers. The purpose of this research is to compare classification performance using single classifiers (Rocchio, Bayes, and Bernoulli) with DCS-LA combining them. This research classified 249 agricultural documents (3 classes) and 750 news documents (5 classes). The single classifiers obtained different accuracies for the agricultural documents and the news documents. The accuracies for agricultural documents are 52%, 44%, and 61.34% for Rocchio, Bayes, and Bernoulli, respectively, while using news documents resulted in better accuracies, i.e., 81.6%, 86.4%, and 85.6% for Rocchio, Bayes, and Bernoulli, respectively. Similarly, the accuracy of DCS-LA is 56% and 86% for agricultural documents and news documents, respectively. This accuracy is higher than the average accuracy of single classifiers because of the local accuracy calculation in DCS-LA. The accuracy for the news documents is always higher than that of the agricultural documents, because the classes in agricultural documents are highly related. The relation can be seen from the percentage of common words shared among the classes. In the agriculture documents, in average of common words is 48% while it is 10% in the news documents.
Keyword: Classification, DCS-LA, Bayes, Rocchio, Bernoulli, Bogor Agricultural University, Institut Pertanian Bogor, IPB

Judul: Classification of Documents in Bahasa Indonesia using DCS-LA with Inverse Distance Weighting
Abstrak: Dynamic Classifier Selection with Local Accuracy (DCS-LA) is a document classification method that combines several classification methods and k-NN. In this study, we implemented the DCS-LA with Inverse Distance Weighting for documents writen in Bahasa Indonesia as well as comparing between the DCS-LA with Inverse Distance Weighting and DCS-LA without Inverse Distance Weighting. We used four classifiers: Rocchio, Naïve Bayes, Bernoulli, and Poisson Naïve Bayes as classifiers in the DCS-LA. For the data, we used agriculture documents consisting of 174 training documents and 75 test documents, and news documents consisting of 500 training documents and 250 test documents. This method can yield an accuracy of 66% and 96% for agriculture documents and news documents, respectively. Without Inverse Distance Weighting, DCS-LA only yields an accuracy of 56% and 86% for agriculture documents and news documents, respectively. Therefore, Inverse Distance Weighting can improve the accuracy of the DCS-LA in classifying text documents in Bahasa Indonesia.
Keyword: 

Judul: Pemanasan sekunder plasma menggunakan gelombang mikro untuk reaksi fusi terkontrol
Abstrak: elah diteliti pengaruh pemanasan sekunder pada plasma dengan menggunakan gelombang mikro, yang dijalarkan tegak lurus dengan medan magnetik toroidal dalam tokamak. Didapat nilai numerik daya P =ψψm ≈ 1,305×10-15 W/m3 yang diserap oleh elektron per volume terhadap pemanasan sekunder menggunakan gelombang mikro yang penjalarannya tegak lurus dengan arah medan magnetik toroidal. Nilai yang kecil menunjukkan nilai rerata terhadap banyaknya partikel dalam plasma. Adapun diperolehnya nilai radius rm ≈ 1m yang mendapat nilai serapan terbesar menunjukkan bahwa pada lokasi tersebut terdapat sumbu magnetik, yang sebelumnya diasumsikan berada di pusat lingkaran penampang poloidal tokamak.
Keyword: sumbu magnetik, Gelombang mikro