Judul: Text Document Classification with Minor Component Analysis.
Abstrak: Document classification can improve information retrieval process by decreasing the search time and increase the relevance of the results. Many classification algorithms have been developed, e.g., Naïve Bayes Classifier, Nearest Neighbor, Principal Component Analysis, and Minor Component Analysis (MCA). This research investigates the performance of MCA in classifying text documents in Bahasa Indonesia. MCA has been applied for image classification, but has not been widely used in text classification. The dataset used in this research contains 750 documents from Media Indonesia Online, consisting of five classes, i.e., economics, education, crime, environment, and badminton. This research also observes the influence of stemming and stoplist in preprocessing to the classification performance. The experiment results show that MCA achieves more 90% accuracy and the preprocessing methods do not have significant effect to the performance. Keywords: document text classification, minor component analysis.
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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: 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