Judul: Klasifikasi Jenis Shorea Berdasarkan Morfologi Daun Menggunakan Jaringan Syaraf Tiruan Propagasi Balik Levenberg-Marquardt
Abstrak: Shorea belongs to the Dipterocarpaceae family and is one of tropical rain forest plants which is being used in timber production. Due to the existence of its various species, Shorea’s identification remains a challenge. The aim of this research was classifying Shorea’s species using Levenberg-Marquardt Backpropagation Neural Network based on 23 morphological characteristics of its leaves. This research utilized the data from 10 species of Shorea that lived in Bogor Botanical Garden, with 10 samples each. Those species were Shorea johorensis foxwf, Shorea pinanga sp, Shorea macroptera dyer, Shorea leprosula miq, Shorea lepida blume, Shorea materialis ridley, Shorea platyclados, Shorea javanica koord & val, Shorea palembanica, dan Shorea seminis. It was found that a 100% identification accuracy can be obtained with Hidden Neuron 8 and 10.
Keyword: Backpropagation, Levenberg-Marquardt

Judul: Identifikasi Jenis Shorea Menggunakan Jaringan Syaraf Tiruan Propagasi Balik Resilient Berdasarkan Karakteristik Morfologi Daun
Abstrak: Shorea (Meranti) is one of the best kind of wood from tropical forests in Indonesia, and one of the best timber of the family Dipterocarpaceae. Shorea has the highest species diversity among its family, which consists about 194 species. This makes it difficult to identify the species of Shorea. Experts in the field of automatic Shorea identification is still rare. Hence, identification using the morphological characteristic of Shorea leaves is more preferred. This research is focused to identify Shorea species using morphological characteristics of leaves. The data used are manual measurement from a sample of the leaves which are taken from each type of Shorea ovalis, Shorea leprosula, Shorea platyclados, Shorea seminis, and Shorea beccariana. Sample of the leaves is taken from the Shorea collection at the Bogor Botanical Gardens. Classification model is built by using resilient backpropagation neural network. After the data has been collected, the feature values of these five species of Shorea is used in the experiment for training and testing the resilient backpropagation neural network. In the training process, we use a variation of the resilient backpropagation neural network parameters in a 5-fold cross-validation experiment. Each experiment unit is divided into two types: with regression and without regression. Overall, the accuracies of the experiment was 98%. Both types produce the same accuracy with different resilient backpropagation neural network optimal parameters, and the same incorrect identification, which is an instance of Shorea leprosula being identified as an instance of Shorea ovalis.
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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