Judul: Identifikasi Jenis Shorea Berdasarkan Morfologi Daun Menggunakan Probabilistic Neural Network
Abstrak: Indonesia is a country which has a large forest area. Dipterocarpaceae is the best timber tree of tropical rain forest. Shorea is a genus of Dipterocarpaceae family which consist of 194 species. Shorea has a considerable number of benefits. It is the most important timber source. Shorea can be used to produce varnish, paint and other chemical materials. Species diversity and morphological similarity may lead to difficulty in identifying the species of Shorea. Mistake in identifying Shorea can lead to inappropriate selection for the final usability. Identification using leaf is the first choice for plant classification compared to biology methods which use cell and molecule. This research aims to develop an identification system to identify Shorea based on morphological characteristic of Shorea. The system identifies 10 species of Shorea. The identification system being built uses Probabilistic Neural Network. The data are divided into five subsets. The five subsets are used as training data and test data. The PNN is trained using 80 leaves to kinds of Shorea. The identification using Probabilistic Neural Network produced 84% average 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: Katalis Zink Ferit Tersubstitusi Cu untuk Oksidasi Benzil Alkohol
Abstrak: Katalis berperan dalam menurunkan energi aktivasi. Katalis ZnFe2O4 dan ZnFe2O4 tersubstitusi Cu disintesis menggunakan metode kopresipitasi untuk digunakan dalam oksidasi benzil alkohol. Katalis dicirikan dengan spektrofotometer inframerah (FTIR) dan difraktometer sinar-X. Spektrum FTIR menunjukkan puncak serapan dari ikatan Zn-O dan Fe-O masing-masing pada rentang 559‒575 cm-1 dan 419‒447 cm-1. Difraktogram menunjukkan terbentuknya fase tunggal Zn0.6Cu0.4Fe2O4 pada katalis ZnFe2O4 tersubstitusi Cu 20%. Benzil alkohol dioksidasi dengan sistem refluks. Konversi benzil alkohol meningkat dengan meningkatnya suhu. Kadar benzaldehida yang dihasilkan dengan katalis ZnFe2O4 tersubstitusi Cu 20% lebih tinggi dibandingkan dengan katalis ZnFe2O4 yang tidak tersubstitusi dan yang tersubstitusi Cu 60 dan 80%. Hal tersebut menunjukkan bahwa katalis ZnFe2O4 tersubstitusi Cu 20% memiliki kinerja yang lebih baik dalam oksidasi benzil alkohol menjadi benzaldehida dengan kadar benzaldehida 8.763%.
Keyword: Benzil alkohol, katalis, oksidasi, ZnFe2O4