Judul: Identifikasi shorea menggunakan k-nearest neighbour berdasarkan karakteristik morfologi daun
Abstrak: Dipterocarpaceae is a group of tropical plants that is used in a timber industry. One of Dipterocarpaceae clans is Shorea, that is the best timber-producing plant in the industrial world. Shorea is difficult to be identified because it has a lot of diversity. The inability to recognize Shorea in forest will enlarge the exploitation of Shorea that has a good timber quality, and silviculture work becomes less of the target because it is not known which Shorea species that will extinct. Shorea tree is usually identified by using the stems, leaves, fruits, and flowers. However, leave is used for the identification in this research because it tends to be available as a source of observation at anytime. The leaves in this reasearch are the collection from Bogor Botanical Gardens. Data are obtained by manual calculating to get the characteristics of the leaves. The obtained data will be processed using k-Nearest Neighbor to get the closeness of new data and training data. The leaves that are included in this research are Shorea multiflora, palembanica, balangeran, lepida and assamica, each one has 10 data. Each set of data has 10 attributes that support the leaf characteristics. From the whole data, Shorea are divided into five subsets including data training and data testing for each subset. This research has two experiments the first experiment without normalization produces 84% accuracy and the second experiment with a normalization produces 100% accuracy.
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Judul: Identifikasi jenis shorea menggunakan jaringan syaraf tiruan propagasi balik berdasarkan karakteristik morfologi daun
Abstrak: Dipterocarpaceae is a major timber tree of tropical rain forest. Shorea is a genus of the Dipterocarpaceae family which consists of around 194 species. Species diversity may lead to difficulty in distinguishing species of Shorea of one another. It takes knowledge from an expert in the field of Shorea to be able to identify the types of Shorea. Errors in identifying the type of Shorea wood can lead to inappropriate selection for the final usability. Identifying the type of Shorea carried out on five species of Shorea owned Bogor Botanical Gardens on the basis of morphological characteristics of leaves. The identification was carried out using Backpropagation Neural Network. To obtain the values of each leaf morphological characteristics of Shorea, each sample measurement data collected manually. The values are then processed using Backpropagation Neural Network to get the pattern from the training and the accuracy of the test phase. This research used a total of 50 data from five species of Shorea. The data are divided into five subsets. The fifth subset is used in the training and testing phases and conducted five times. The use of methods Backpropagation neural networks in identifying species Shorea produces an average accuracy of 90%.
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Judul: Sintesis, Iuraicterisasi Struictur Dan Sifat Termal Pada Bahan Iconduktor Superioniic Berbasis Gelas (Ag2S)x(AgP03)l-x
Abstrak: Bahan Konduktor superionik telah banyak diteliti terutama sebagai zat padat elektrolit untuk perangkat elektronik maupun elektrokimia dan salah satunya adalah bahan dengan Ag+ ditemukan memiliki konduktivitas tinggi pada temperatur ruang yaitu 0.01 A/cm. Pada penelitian ini gelas konduktor (Ag+S)x(AgPO3)l-xmelalui metoda pendinginan cepat (rapid quenching)di Laboratorium sediaan Cuplikan, Balai Spektometri Batan.
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