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: Penggabungan ciri morfologi, tekstur, dan bentuk untuk identifikasi daun menggunakan Probabilistic Neural Network
Abstrak: This research proposes a new system for leaf identification using classifier combination and probabilistic neural network. The features that used for classification are morphology, texture, and shape of leaves. Classifiers combination is used to combine these features. The method to extract texture and shape are co-occurrence matrix and Fourier descriptors, respectively. After feature extraction, the feature is classified using probabilistic neural network. The experiment data consist of thirty species flora from Bogor Botanical Garden, Indonesia. The experiment results show that classification without classifier combination has the accuracy of 79.05%, and using classifier combination the accuracy increased to 83.33%. Hence, the propose system is promising for leaf identification and supporting plant biodiversity in Indonesia..
Keyword: Leaf identification; Probabilistic Neural Network; Classifier combination, Probabilistic Neural Network, Classifier combination, 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