Judul: Identifikasi Daun Shorea Menggunakan Probabilistic Neural Network dengan Normalisasi Fitur Morfologi Daun
Abstrak: Shorea is a timber plant from the genus of meranti. Shorea belongs to Dipterocarpaceae family which has 194 species growing in tropical area. The species of Shorea is difficult to be identified because of it is similarity to each other. This research utilized Probabilistic Neural Network (PNN) to classify Shorea species. The parameters used for classification were area, perimeter, diameter, smooth factor, form factor, perimeter ratio of diameter, perimeter ratio of physiological length and physiological width with 0.1 bias value. It was found that the accuracy of the proposed method was 100% (without normalization) and 91% (with normalization). It can be concluded that the morphological features of area, perimeter, diameter, and ratio of leaf length and width significantly affect the accuracy.
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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: 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