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: 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.
Keyword: 

Judul: Jagorawi Toll Highway's Traffic Accident (1996-1997
Abstrak: Jalan tol Jagorawi adalah jalan tol yang pertama dibangun di Indonesia, menghubungkan antara Jakarta, Bogor dan Ciawi. Jalan tol telah dirancang sedemikian rupa untuk memberi keamanan bagi pemakai jalan, namun kenyataan yang ada memperlihatkan jumlah kecelakaan lalu lintas yang terjadi cukup besar. Penelitian ini bertujuan untuk melihat gambaran kecelakaan lalu lintas di jalan tol Jagorawi mengenai jalur dan ruas tempat kejadian, hari, waktu, serta cuaca saat kecelakaan terjadi. Hasil perhitungan memperlihatkan tingkat kecelakaan di jalur yang menuju arah Jakarta lebih tinggi daripada arah meninggalkan Jakarta. Pada setiap ruas di setiap jalur, tingkat kecelakaan yang mengakibatkan luka-luka jauh lebih tinggi daripada tingkat kecelakaan tanpa korban; namun belum bisa ditentukan ruas mana yang paling berbahaya karena belum ditemui adanya pola tertentu yang khas dari angka-angka hasil perhitungan tingkat kecelakaan per ruas yang ada. Frekuensi kecelakaan di hari Sabtu dan Minggu lebih tinggi dari hari-hari lain, untuk hari Sabtu frekuensi kecelakaan pada jalur yang meninggalkan Jakarta lebih tinggi sedangkan di hari Minggu lebih tinggi pada jalur menuju arah Jakarta. Kecelakaan lebih banyak terjadi pada siang hari, namun kecelakaan fatal lebih banyak terjadi di malam hari. Sebagian besar kecelakaan di jalan tol Jagorawi terjadi pada saat cuaca cerah, di alinemen jalan yang lurus dengan permukaan jalan yang kering.
Keyword: tingkat kecelakaan, statistika deskriptif, uji khi kuadrat