Rahmat Gernowo

Detection Lung Cancer Using Gray Level Co-Occurrence Matrix (GLCM) and Back Propagation Neural Network Classification

Lung cancer prevalence is one of the highest of cancers, at 18 %. One of the first steps in lung cancer diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis using lung tissue sample microscopic analysis has some weakness. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this research is to design a lung cancer detection system based on analysis of microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted with the Gray Level Co-Occurrence Matrix (GLCM) method and classified using back propagation neural network. This method is implemented to detection both normal and cancerous lung of biopsy samples. In the stage of training, 20 biopsy image samples were analyzed using back propagation neural network with 95% accuracy. On the other hand, 16 biopsy samples were analyzed during testing, with an accuracy of 81.25%. These results show that microscopic biopsy image processing can be implemented in a system of lung cancer detection.

Geographic Information System Carbon Development Landed on Land of Primary Dry Limits using Method Stock Difference

Land use emission factor is one of the causes of global climate change The Intergovernmental Panel on Climate Change (IPCC) concludes that most of the global average temperature increase is due to human activities through GHG emissions. GHG emissions come from a variety of sources from agriculture, forestry, and other land uses to a land ecosystem, derived from changes in carbon stock and from non-CO2 emissions as well as from various sources. Changes in carbon stocks in a Biomass can be calculated using the Stock-Difference method in which carbon stores in a cage are measured at two different times to assess changes in carbon stores. In finding the value of carbon stocks in primary dryland forest is highly dependent on the availability of data that becomes the supporting variable that is the data of forest for each region, then GIS becomes one of the solutions in obtaining data. Our results conclude that in 2012 and 2016 there was a decrease in carbon savings of an average of 1,842 C tons/ha and an average rate of emission increase of 5,430.73 CO2 tons/ha.