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 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: 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