Judul: Sistem Identifikasi Famili Secara Otomatis Berbasis Teks Menggunakan Dokumen Etnofitomedika
Abstrak: This research represents a text-based system that can be used for automatic identification of plant families according to the taxonomy of plants. The identification was done by utilizing information from etnofitomedika documents on plant characteristics that can represent the family of each plant including morphology, habitats, habitus, and biochemical compounds. The method used in this research is Chi-Square method to select important words in each document and Naïve Bayes method to classify the words. The experimental results showed that the critical value using significance level of 0,001 had better accuracy than the critical value using significance level of 0,01. The accuracy of classification systems based on family category using K-fold cross validation with significance value of 0,001 was 85,5%. It was found this system is useful in helping users especially researchers and taxonomists for plant family identification. Furthermore, it can help strengthening the knowledge on biodiversity and the use of medical plants.
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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: Kondisi Minimal Bagi Kesetimbangan Duopoli Cournot dan Stackelberg
Abstrak: Dalam model duopoli, dimana dalam pasar terdapat dua perusahaan yang saling bersaing, setiap perusahaan bertujuan memperoleh imbalan yang maksimum. Untuk mewujudkan tujuan tersebut diperlukan strategi. Kuantitas merupakan salah satu strategi perusahaan Strategi tersebut dapat dimainkan secara simultan atau sekuensial. Permainan simultan terjadi jika para pemain memutuskan bergerak pada saat yang sama, sedangkan permainan sekuensial terjadi jika para pemain memutuskan bergerak pada waktu yang berbeda. Permainan simultan dengan kuantitas sebagai strategi yang dipilih akan menghasilkan model duopoli Cournot, sedangkan permainan sekuensial dengan kuantitas sebagai strategi yang dipilih akan menghasilkan model duopoli Stackelberg. Karya tulis ini membahas bagaimana suatu kondisi minimal pada harga pasar dan fungsi biaya perusahaan akan menyebabkan perusahaan lebih memilih model duopoli Cournot atau Stackelberg, sehingga imbalan yang didapatnya maksimum. Harga pasar yang log konkaf akan menimbulkan model duopoli Cournot. Pada kasus ini pilihan koresponden tanggapan terbaiknya merupakan fungsi turun. Jika harga pasarnya log konveks dan tak ada biaya produksi, pilihan koresponden tanggapan terbaiknya merupakan fungsi naik dan akan menimbulkan model duopoli Stackelberg.
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