Judul: Klasifikasi Dokumen Bahasa Indonesia Menggunakan Adaptive Classifier Combination (ACC)
Abstrak: Search engine is a tool to find an information that match with user’s request based on query or keywords. However, the query of search engine often shows irrelevance sites that didn’t match with user’s desire. Classification was developed to focused user’s request on the information that they searched, so the result would be more accurate and less effort yet effective and efficient for the system. Adaptive Classifier Combination is one of many methods that can be used for text classification. This method is a combination between k-nearest neighbor classification and Naive Bayes classification to get a better result than using single method. The result of this research is an implementation of web-based application that developed using PHP language. The document is from horticulture reasearch journal from 2002 until 2009. From 340 documents, 300 document is used for data training and 40 documents is for testing. The accuracy of ACC is 80% and for Naive Bayes classification is 42.5%.
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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: Karakteristik Piezoelektrik Film LaxSr₁-xTiO3 di atas Substrat TCO
Abstrak: Studi material perovskite SrTiO3 sudah banyak dilakukan dan menunjukkan sifat feroelektrik, piezoelektrik, kuantum paraelektrik dan superkonduktor yang baik. Dengan penambahan dopant La2O3 diduga menambah performa sifat piezo dan karakteristik lainnya. Karakteristik film LaxSr₁-xTiO3 (LST) dengan variasi x= 0, 0.02, 0.04, 0.06 terbentuk pada suhu annealing 550 oC di atas substrat TCO melalui metode (Chemical Solution Deposition) CSD dan spin coating dengan 700 rpm pada kondisi suhu ruang dan tekanan atmosfer standar. Hasil menunjukkan, semakin banyak pendadah La maka nilai ketebalan film, ukuran rerata kristalit, parameter kisi, transmitansi, nilai konduktivitas (maksimum 589.7 S/cm pada sampel x=0.04 di frekuensi 10 Hz), nilai konstanta tegangan (c) dan muatan (d) piezo semakin meningkat. Dinamika bentuk grafik nilai tegangan (V) dan konstanta regangan mekanik (s) nampak mirip. Nilai V mencapai 0.127 V untuk x=0.06 (beban 50 g). Nilai maksimum c, d, s masing-masing mencapai 20.07 Vm/N, 1.14x10-8 C/N, 13642.71. Nilai band gap untuk x=0, 0.02, 0.04, 0.06 masing-masing 3.21 eV, 3.1 eV, 2.6 eV, 2.4 eV. La2O3 menambah performa sifat piezo, sehingga LST berpotensi sebagai sensor tekanan.
Keyword: CSD method, La2O3, LaxSr1-xTiO3, piezoelectric, TCO