Learning Vector Quantization 3 (LVQ3) and Spatial Fuzzy C-Means (SFCM) for Beef and Pork Image Classification

Base on some cases in Indonesia, meat sellers often mix beef and pork. Indonesia is a predominantly Muslim country. Pork is forbidden in Islam. In this research, the classification of beef and pork image was performed. Spatial Fuzzy C-Means is used for image segmentation. GLCM and HSV are used as a feature of segmentation results. LVQ3 is used as a method of classification. LVQ3 parameters tested were the variety of learning rate values and window values. The learning rate values used is 0.0001; 0.01; 0.1; 0.4; 0.7; 0.9 and the window values used is 0.0001; 0.4; 0.7. The training data used is 90% of the total data, and the testing data used is 10%. Maximum epoch used is 1000 iterations. Based on the test results, the highest accuracy was 91.67%

Sintesis 3-(4-bromofenil)-1-(naftalen-1-il)prop-2-en-1-on dari 1-asetilnaftalen dengan 4-bromobenzaldehid

Chalcone of 3-(4-Bromo-phenyl)-1-naphthalen-1-yl-propenone was synthesizedby the stirring method in Claisen-Schmidt Condensation with NaOH as the catalyst. Thestructure of the compound was confirmed by 1 H-NMR, 13 C-NMR, IR, MS, and UVspectroscopy method. The compound produced was in 81.9% yields. Datacharacterization showed that the compound obtained was the targetted compound