Judul: Penentuan Subjek Otomatis Berbasis DDC pada Dokumen Perpustakaan miMenggunakan Algoritme Lin Similarity
Abstrak: Subject classification for library document with Dewey Decimal Classification (DDC) system is difficult to perform manually. The goal of this research is to make an application that automatically do perform subject classification for library document using similarity method. We use Natural Language ToolKit (NLTK) with Wordnet module to find similarity between keyword and DDC class. DDC is a hierarchy classification. We use Lin Similarity to find similarity between two words, with Brown corpus for Information Content (IC) of Wordnet. Wordnet can find similarity for only noun and verb, so we do not process other kinds of word. We use 30 documents combination of theses and dissertations in Bogor Agriculture University. We use 3 different methods to decide the relevant class of DDC which is similar to a document keyword. The first method is maximum-maximum method, the second one is maximum-average method, and the third method is maximum-minimum method. The first method results in 6 documents having the same main class, 2 documents having the same division class, and 0 document having the same section class. The second method results in 5 documents having the same main class, 1 document having the same division class, and 0 document having the same section class. The third method results in 3 documents having the same main class, 2 documents having the same division class, and 0 document having the same section class.
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Judul: Penentuan Nomor Panggil Otomatis Berbasis DDC pada Dokumen Perpustakaan Menggunakan Klasifikasi Naive Bayes
Abstrak: This research discusses automatic call number classification for library document with Dewey decimal classification (DDC) scheme. The goal of this research is to determine the call number of library document automatically based on DDC using naive Bayes classification. This research uses control vocabulary to determine DDC class of each document. Documents used in this are 190 documents with 114 training documents and 76 testing documents. This research uses information gain for feature selection. Result of the feature selection will be used as a reference in modelling classification. In modelling classification, a similarity calculation between results of token feature selection processing and control vocabulary using Natural Language ToolKit (NLTK) is performed. The accuracy obtained in the application of the naive Bayes method with the use of stemming is 45.26% and the non-stemming is 44.21%.
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Judul: Definisi zat besi ( Fe ) pada anak babi
Abstrak: Dalam bidang sub sektor peternakan, ternak babi mempunyai prospek yang cukup baik untuk dikembangkan dalam rangka meningkatkan eksport non migas. Namun anak-anak babi yang sedang menyusu sering mengalami defisiensi zat besi (Fe). Bila masalah ini tidak segera ditanggulangi, peternak akan mengalami kerugian. Zat besi dapat diperoleh dari makanan yang berasal dari hewan (tepung tulang, tepung ikan, tepung daging, "skimmed milk"); tumbuh-tumbuhan (leguminosae, rumput-rumputan, biji-bijian) dan mineral alam (Girindra, 1979). Dalam tubuh hewan, zat besi berperanan sebagai pembawa 02 ke seluruh jaringan, juga sebagai pembawa oksigen pernafasan di dalam sel urat daging (Underwood, 1981). Dalam duodenum dan yeyenum bagian atas, besi dalam bentuk ferro akan diserap. Penyerapan ini akan diatur berdasarkan teori "mucosal block" dari Granick. Kemudian dalam plasma darah, besi akan berikatan dengan B₁ globulin dan membentuk transferrin, yang akan segera dibawa ke seluruh jaringan. Kelebihan besi akan diikat oleh apoferritin dan membentuk ferritin yang ditimbun sebagai besi cadangan, terutama di hati. Bila kapasitas besi yang dapat disimpan sebagai ferritin telah jenuh, maka besi akan ditimbun sebagai hemosiderin. Zat besi dalam jumlah yang sedikit akan diekskresikan melalui pengelupas- an epitil, tinja, air kemih, cairan empedu, keringat. ...
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