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: Karakteristik Massa Air Arlindo Di Pintasan Timor Pada Musim Barat Dan Musim Timur
Abstrak: Pintasan Timor merupakan jalur keluar dari Arlindo (Arus Lintas Indonesia), yaitu arus yang membawa massa air dari Samudera Pasifik melintasi perairan Indonesia menuju Samudera Hindia. Oleh karena itu, Pintasan ini memiliki peranan yang penting dalam sirkulasi massa air khususnya di lintang rendah. Penelitian ini dilaksanakan pada dua periode pengamatan yaitu Januari 2004 (5 stasiun) dan Juni 2005 (6 stasiun). Data yang digunakan adalah data suhu, salinitas, sigma-t, kedalaman, kecepatan dan arah arus. Pengolahan data dilakukan dengan menggunakan perangkat lunak Ocean Data View (ODV), Surfer, dan Matlab untuk memperoleh hasil berupa sebaran melintang dan menegak (suhu, salintas, dan σt), diagram T-S, dan arus Geostropik. Setiap parameter yang diamati memiliki pola pelapisan. Pelapisan ini mengalami perubahan kisaran nilai dan ketebalan pada dua musim yang diamati. Kisaran nilai suhu pada Musim Timur lebih rendah daripada Musim Barat, yaitu mencapai 28.01oC (Juni 2004) dan 29.60 oC (Januari 2005). Pada pelapisan suhu, lapisan homogen yang terbentuk pada Musim Timur lebih tebal daripada Musim Barat yaitu perbedaan ketebalannya mencapai 45 m, namun lapisan termoklin yang terbentuk pada Musim Barat lebih tebal (perbedaan ketebalan mencapai 103 m) daripada Musim Timur. Kisaran nilai salinitas permukaan pada Musim Timur lebih rendah daripada Musim Barat, yaitu mencapai 33.91 psu (Musim Timur) dan 34.33 psu (Musim Barat). Nilai sigma-t di permukaan pada Musim Timur lebih tinggi dari Musim Barat, yaitu berkisar antara 21.20-21.80 (Musim Timur) dan 20.08-21.70 (Musim Barat). Analisis dengan diagram T-S menunjukkan adanya dua jenis massa air, yaitu massa air dicirikan oleh salinitas minimum South Pacific Intermediate Water (SPIW) dan Antarctic Intermediate Water (AAIW). Pengamatan Musim Barat dan Musim Timur tidak ditemukan massa air kerkarakteristik salinitas maksimum baik yang berasal dari massa air Pasifik Utara maupun Pasifik Selatan. Perhitungan arus dengan menggunakan metode geostropik, diketahui bahwa arus di Pintasan Timor bergerak ke arah barat dan timur. Arah arus Geostropik di lapisan permukaan didominasi menuju arah barat baik pada Musim Barat maupun Musim Timur. Transpor massa air pada Musim Barat (Januari 2004) secara menyeluruh bergerak ke arah timur dengan transpor total massa air sebesar 4.61 Sv. Musim Timur (Juni 2005)memiliki transpor total massa air pada sebesar 1.85 Sv dengan total transpor massa air yang bergerak ke timur sebesar 38.92 % dan bergerak ke barat sebesar 61.08 %.
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