Judul: Identifikasi Jenis Aglaonema Menggunakan Probabilistic Neural Network
Abstrak: Aglaonema is an ornamental plant that is quite popular in Indonesia. It is estimated that there are nearly 8000 species Aglaonema in the world both native and hybrid. The many types of Aglaonema in the world causes difficult in identifying some types of Aglaonema. This research attempts to identify the type of Aglaonema based on the image using Probabilistic Neural Network (PNN). The data used in this study have 900 images of leaves, which consists of 30 types of Aglaonema. Each image of Aglaonema that will be identified by the system will first be subjected to two stages, texture feature extraction and colour feature extraction. The texture feature extraction used Local Binary Pattern Variance (LBPV) and Co-occurrence Matrix while the colour feature extraction used Histogram-162 (HSV-162). We perform two type of experiment, one where we uses each feature seperately and another where the two features are combine. For classifier we used Probabilistic Neural Network (PNN). The results indicates that the combination of Co-occurrence Matrix with HSV-162 yield a better accuracy compact two when the feature are used seperately. On the other hand, the combination of Local Binnary Pattern Variance (LBPV) and HSV-162 does not yield an increase in accuracy, however the accuracy on this case is better than the accuracy of the combination between Co-occurrence Matrix and HSV-162. The highest accuracy is obtained in the case of the Local Binnary Pattern Variance (LBPV) and HSV-162 with the value of 55.56%
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Judul: Identifikasi tanaman hias secara otomatis menggunakan Metode Local Binary Patterns descriptor dan Probabilistic Neural Network
Abstrak: Identification house plant species automatically still be a problem for recognizing various kind of house plants. This research proposes a new method for identifying a house plant using Local Binary Patterns (LBP) descriptor and Probabilistic Neural Network (PNN). There are three kinds of LBP descriptor used in this research, i.e. , , and . Database of 300 house plant images belong to 30 different types of house plant in Indonesia are extracted using , , and based on texture feature and classified using PNN. The experimental result shows that has the best accuracy to indentify house plants with accuracy 73.33%. The proposed system is promising since it is capable to identify house plants species efficienty and accurately
Keyword: Plant extraction, Local binary patterns, Texture feature, Probabilistic neural network, Bogor Agricultural University, Institut Pertanian Bogor, IPB

Judul: Kinetika dan Termodinamika Korosi Logam Cu dan Inhibisinya oleh Zink (Disikloheksilditiofosfat) dengan Teknik Polarisasi Potensiodinamik
Abstrak: Zinc dialkyldithiophosphates (ZDTP) is a lubricant additives which has many functions such as anti-friction, anti-wear, antioxidants, and anti-corrosion. In this research, ZDTP type which was made is ZDTP with alkyl cyclohexyl group (ZDTPS). The yield of ZDTPS were 83.50% and 82.10%. Inhibition effectivity of zinc dicyclohexyldithiophosphate were 94.30% in concentration 3% and increase significantly in concentration 1%. The parameters of thermodynamics was measured to show the easiness of corrosion reaction occur. Themodynamics parameters of blank revealed ΔH∗ was 26.8583 kJ/mol, ΔS∗ was -150.4102 J/molK, and ΔG∗ was 72.4295 kJ/mol. Thermodynamics parameters of ZDTPS revealed ΔH∗ was 50.0563 kJ/mol, ΔS∗ was -79.9807 J/molK, and ΔG∗ was 74.2963 kJ/mol. Activation energy from the corrosion process with ZDTPS presence was 52.6509 kJ/mol. This value is bigger than energy activation of blank with the value was 29.4548 kJ/mol.
Keyword: Bogor Agricultural University (IPB), ZDTP, thermodynamic, polarization, kinetic, inhibition effectivity