Judul: Clustering Dokumen Tumbuhan Obat Menggunakan Concept Indexing
Abstrak: Penelitian ini mengusulkan algoritme concept indexing untuk identifikasi famili tumbuhan obat berdasarkan teks. Proses identifikasi famili dilakukan dengan dokumen tumbuhan obat. Pada dokumen tumbuhan obat dapat ditemukan berbagai karakteristik dari tumbuhan obat. Karakteristik-karakteristik pada dokumen tumbuhan obat dapat dimanfaatkan untuk menentukan famili dari tumbuhan obat. Metode penelitian ini terdiri atas enam tahapan yaitu pengumpulan data, praproses, ekstraksi ciri, clustering dokumen, identifikasi famili dan evaluasi. Pengumpulan data merupakan tahapan mengumpulkan dan melakukan digitasi dokumen. Data yang digunakan pada penelitian ini merupakan dokumen dalam format xml. Tahapan praproses melibatkan lowercasing, tokenisasi dan penghilangan kata hubung. Praproses bertujuan untuk melakukan pemotongan kata dan juga menghilangkan karakter-karakter tertentu seperti tanda baca dan juga kata hubung . Ekstraksi ciri bertujuan untuk menemukan kata-kata penting yang merepresentasikan karakteristik dari famili tumbuhan obat. Dokumen diubah kedalam bentuk vektor berdasarkan frekuesi kemunculan kata di dalam sebuah dokumen. Concept indexing melakukan pengelompokkan terhadap dokumen-dokumen yang memiliki kemiripan dan menggunakan centroid dari cluster sebagai sumbu pada dimensi yang lebih kecil. Clustering dokumen dilakukan dengan algoritma fuzzy C-means. Fuzzy C-means mengizinkan sebuah dokumen masuk kedalam beberapa kelompok bedasarkan derajat keanggotaan ke masing-masing kelompok. Hasil percobaan menunjukkan bahwa akurasi sistem mencapai 80.6%. Algoritme yang diusulkan pada penelitian ini dapat digunakan untuk membantu ahli botani dalam menentukan famili tumbuhan berdasarkan dokumen teks.
Keyword: clustering, concept indexing, medicinal plant family

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: Approximation of the Median of Poisson Distribution Using the Relation of Poisson-Gamma Distribution
Abstrak: A major drawback of median is that median cannot usually be defined in closed form, even in the cases where their distribution functions are known. This study gives an approximation of the closed form for the median of the Poisson distribution by using the relation of Poisson-Gamma distribution. The approximation uses elementary techniques based on the monotonicity of certain sequences involving tail probabilities of the Poisson distribution and the Central Limit Theorem. Furthermore, a closed form expression for the mean absolute deviation 􀜧|􀜰􀰒 􀵆 􀜯􀝁􀝀􁈺􀜰􀰒􁈻| is also studied. The median is relevant because of the fact that it minimizes the mean absolute deviation of a Poisson random variable (􀜰􀰒􁈻 by considering median as central tendency.
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