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.
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Judul: Tingkat Kelahiran Ikan Plati Sunset Xiphophorus maculatus (Günther, 1866) pada Beberapa Tingkat Suhu Air
Abstrak: Ikan plati merupakan ikan hias air tawar yang bereproduksi dengan cara melahirkan (viviparous) dalam beberapa waktu. Memproduksi ikan plati dalam skala besar ternyata dihadapkan pada kendala yang besar, yaitu belum ada teknologi kelahiran anak serentak dalam satu waktu. Hal ini berdampak pada umur anak ikan tidak seragam dan pola tanam produksi terganggu, sehingga berujung pada ketidakefisienan dalam produksi. Pada penelitian ini dilakukan upaya penyerentakkan proses kelahiran oleh induk plati melalui rangsangan eksternal berupa peningkatan suhu secara mendadak. Penelitian ini menggunakan rancangan faktorial dengan empat perlakuan sebanyak tiga ulangan. Perlakuan kenaikan suhu yang diberikan adalah 30-32°C selama 12 dan 24 jam. Hasil yang diperoleh perlakuan kenaikan suhu ke 30°C selama 12 jam perlakuan memberikan tingkat induk melahirkan tertinggi.
Keyword: Ikan hias, Kelahiran, Poecillidae, Suhu, Xiphophorus maculatus