Judul: Klasifikasi Dokumen Tumbuhan Obat menggunakan Algoritma KNN Fuzzy
Abstrak: Many documents about the medicinal plants are not fully utilized because of difficulty finding information. Computational techniques in the form of search engine may be one solution to solve the issue. This research proposed a new system for document classification and information retrieval system of medical plants. Chi-Square is used to extract term that represents a specific category. This terms are called features. KNN Fuzzy algorithm is used to classify documents of medical plants. Document classification done for two categories, there are family and disease. The experiment result show that number of features produced in significant level of 0.01 is greater than number of features produced in significant level of 0.001. This makes the level of accuracy produced in significant level of 0.01 is higher than significant level of 0.001. KNN Fuzzy algorithm is effective for document that has uniform characteristic and document that has uneven distribution. According to experiment result, level of accuracy achieved in family model is 94.87% and level of accuracy achieved in disease model is 100%. Average precision of information retrieval for single query is 0.96 and for double query is 0.89.
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Judul: Klasifikasi Dokumen Tumbuhan Obat Berbasis Famili Menggunakan Probalistic Neural Network
Abstrak: This research presents a system for the extraction of features and classification of the document medicinal plants using the chi-square method and neural network classification probalistic. Automation family identification by grouping characteristics medicinal plants contained in the document. In this research, the phase consists of collecting documents, doing conversion from hardcopy documents to softcopy into XML format, pre-process the document, featuring selection using the chi-square, document classification using PNN and evaluation using Confusion Matrix. Classification results are influenced by the number of family identifier words. Number of words identifier of a family affected by the same number of words in each document training. The more same words in each document training, the greater probability of the word being said identifier. In this research, identifier of a family does not describe the characteristics of plant taxonomy. This is due to the same number of words in each document has not practically described the characteristics of plant taxonomy. The evaluation research of classification algorithm Probalistic Neural Network (PNN) with weight value 1 and layer patterns using feature extraction using the chi-square values of alpha (α) value of 0.1 overall result of classification is 82.14%. This research represents a medical plant document system that can be used for automatic identification of families according to the taxonomy of plants.This system is useful to help users especially researchers and taxonomists in the identification document through labeling family automatically on each document, with the existing system, therefoe the users dont need to bring a book or a guide book to identify the field. Furthermore, it can overcome the limitations of people's knowledge of the diversity and the use of medicinal plants. That is because there are a lot of documents to train the same word in each document which is not characteristic of plant taxonomy.
Keyword: probalistic neural network classifier, medical plant document, document identification, document classification, chi-square

Judul: Perilaku Reproduksi dan Karakteristik Mikrohabitat tempat Bertelur Burung Gosong (Megapodius reinwardtii Dummont 1823) Di Taman Buru Pulau Moyo
Abstrak: Burung gasang (Megapodius reinwardtii) merupakan jenis burung terestrial yang selalu berpasangan sepanjang hidupnya. Burung ini dilindungi berdasarkan SK Menteri Kehutanan No. 882/Kpts-II/1992 tanggal 28 September 1992. Penyebaran satwa ini hanya terbatas di wilayah Indonesia bagian timur.
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