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: Analisis Struktur Box Girder Jembatan Fly Over Rawabuaya Sisi Barat Terhadap Gempa
Abstrak: The Rawabuaya flyover bridge is the solution that is offered by the local government to overcome the traffic jam that often happens in the Rawabuaya area. It has an extremely vital function to disentangle the traffic congestion; therefore the structure must be strong in holding the on-going load, specifically the seismic load. The seismic load is dangerous to a structure because it has period that causes the structure to repeatedly shaken. If the movements happened continually, the structure will collapse ― depending on how much earthquake load that is being loaded on the structure. Therefore, it is essential to conduct an structure analytical on the earthquake resistance level of Rawabuaya flyover bridge based on the "Standar Perencanaan Ketahanan Gempa Untuk Struktur Bangunan Gedung dan Non-Gedung" RSNI 03-1726-2010 and "Peta Zonasi Gempa Indonesia 2010" to know further about the on-going deformation. This research is done thoroughly by recognizing the maximum forces in the combination of ultimate loads based on the most recent rules of encumbering, including the rules regarding the seismic load. The maximum forces in the combination of ultimate loads will then compared with the forces' nominal values, especially those that related to the seismic load. From the research, it can be concluded that the values of maximum forces in the combination of ultimate loads have the combined seismic load that works on the upper structure of box girder as well as on the structure beneath the pier of Rawabuaya flyover bridge with a maximum momentum values of 800300,80 KNm that applies to Pier P6B with the combination of the singular ultimate load, the additional dead load, the prestress load, and the “T” truck load. Impairment to the structure is bound to happen if the combination of ultimate load is forced to work continuously on the Rawabuaya flyover bridge. Meanwhile, the result of the compared maximum forces in the combination of ultimate load with the values of nominal forces indicates that the Rawabuaya flyover bridge has a large amount of seismic load with 10% difference. It means this structure can resist 90% of ultimate seismic load, which requires the usage of reinforcements on the upper structure of box girder as well as the on the structure beneath the pier to increase the concrete stress power and the tendon on the upper structure of box girder to increase the tensile of the concrete.
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