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: Resistansi Escherichia coli yang Diisolasi dari Kucing di Klinik Hewan Kota Depok Terhadap Antibiotik
Abstrak: Kejadian resistansi antibiotik pada Escherichia coli pada hewan kesayangan sangat erat kaitannya dengan penggunaan antibiotik yang tidak tepat dalam waktu panjang dan tidak terkontrol. Penelitian ini bertujuan mengetahui gambaran resistansi E. coli yang diisolasi dari kucing rawat inap di klinik hewan Kota Depok terhadap antibiotik amoksisilin, gentamisin, sefotaksim, enrofloksasin, dan doksisiklin. Uji kepekaan antibiotik pada penelitian ini menggunakan metode Disk Diffusion Kirby-Bauer. Didapati sebanyak 12 isolat merupakan Escherichia coli. Hasil uji menunjukkan sampel resistan terhadap doksisiklin (sembilan isolat), amoksisilin (lima isolat), dan sefotaksim (lima isolat). Hasil intermediet ditunjukkan oleh amoksisilin dan sefotaksim, 16.7% resistan terhadap gentamisin, dan 8.3% resistan terhadap enrofloksasin. Escherichia coli pada penelitian ini masih cenderung sensitif terhadap gentamisin dan enrofloksasin., Antibiotic resistance in Escherichia coli in pets is closely linked to the long term and uncontrolled use of antibiotics. The study aims to determine the resistance of isolated E. coli cats in the City Depok veterinary clinic to the antibiotics amoxicillin, gentamicin, cefotaxime, enrofloxacin, and doxycycline. Antibiotic sensitivity test in this study using the Kirby-Bauer Disk Diffusion method. Twelve isolates were found to be Escherichia coli. The test results showed samples resistant to doxycycline (nine isolates), amoxicillin (five isolates), and cefotaxime (five isolates). Intermediate results were shown by amoxicillin and cefotaxime, with 16.7% resistance to gentamicin, and 8.3% to enrofloxacin. Escherichia coli in this study still tends to be sensitive to gentamicin and enrofloxacin.
Keyword: antibiotics, antibiotic resistance, escherichia coli