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: 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: Sintesis Timbal Zirkonat Titanat (Pb(ZrxTi(1-x))O3) dengan Metode Lelehan Garam: Pengaruh Suhu dan Waktu Sintering
Abstrak: Timbal zirkonat titanat (Pb(ZrxTi(1-x))O3, x = 0.52) atau PZT merupakan suatu bahan keramik perovskit yang memiliki sifat piezoelektrik sangat baik dibandingkan bahan lainnya. Ukuran bahan ini sangat penting untuk aplikasi ultrasonografi, sehingga diperlukan metode sintesis yang dapat mengendalikan ukuran PZT, salah satunya adalah metode lelehan garam. Metode ini dapat mengendalikan ukuran partikel dengan meragamkan parameter sintesis, seperti suhu dan waktu sintering. Pembentukan PZT diawali dengan reaksi PbO dan TiO2 membentuk intermediet PbTiO3 lalu bereaksi dengan ZrO2 membentuk PZT. Produk sintesis dicirikan dengan difraktometer sinar-X, mikroskop elektron payaran, penganalisis ukuran partikel, dan adsorpsi Brunauer Emmett Teller. PZT dengan metode lelehan garam berhasil disintesis pada suhu sintering 775 °C selama 4 jam. Suhu dan waktu sintering tidak secara nyata memengaruhi ukuran partikel dan kristalinitas PZT. Partikel terkecil 659 nm diperoleh pada suhu sintering 875 °C selama 4 jam.
Keyword: lelehan garam, perovskit, piezoelektrik, PZT