No abstract

Type of lung disease is very much manifold, but type of lung disease caused by smoking there are only 4, namely Bronchitis, Pneumonia, Emphysema and Lung Cancer. Doctors usually diagnose lung disease from CT scans using the naked eye, then interpret data one by one.This procedure is not effective. The aim of this research is improvement accuracy of lung diseases detection caused by smoking using support vector machine on computed tomography scan (CT scan) images. This study includes 4 (four) main points. First is the development of software for segmentation of lung organ automatically using Active Shape Model (ASM) method. Second is the segmentation of candidates who are considered illness by using Morphology Mathematics. The third process of lung disease detection using Support Vector Machine (SVM). Fourth is visualization of disease or lung disorder using Volume Rendering.

The research was conducted to develop the information system model on nutrition status of child monitoring based on geographical information system (GIS) to support the plan of increasing the nutrition improvement at the District Health Office, Sukoharjo Regency. This descriptive research was carried out by implementing interview to subjects who were involved in the activity of the monitoring. Observation was also performed to two objects, namely the structure and the procedure of information. The collected data were analyzed descriptively by applying result of structure and the procedure analysis. The system development was designed by using the approach of FAST (Framework for the Application of System Techniques). The observation to the problem, scope, and property had been conducted by the interview with the subjects indicate that the research subjects at all levels from top managers to persons in the transactional level as well as those who are at cross section department support the development of monitoring system to the improvement of nutrition status program, and this system is reliable to mapping perform of nutrition status of child based on the category as severe malnutrition, under nutrition, normal and overweight. In the future nutrition information based on GIS have the benefits of the new system in supporting the monitoring activity toward the nutrition improvement program and it also supports the plan. Suggestions from this research might go to the government health institution to develop spatial or terrestrial data on the health programs have to be designed GIS for the each other program. Moreover, the other model should be developed GIS in the other spatial data and information can be accessed by informative map.

Introduced is a new algorithm for the classification of numerical data using the theory of fuzzy soft set, named Fuzzy Soft Set Classifier (FSSC). The algorithm uses the fuzzy approach in the pre-processing stage to obtain features, and similarity concept in the process of classification. It can be applied not only to binary-valued datasets, but also be able to classify the data that consists of real numbers. Comparison tests on seven datasets from UCI Machine Learning Repository have been carried out. It is shown that the proposed algorithm provides better accuracy and higher accuracy as compared to the baseline algorithm using soft set theory.

This paper presented the research result on the design of pulmonary TB (Tuberculosis) detection systems using a statistical approach. The study aimed to address two problems in detecting pulmonary TB by doctors, especially in remote areas of Indonesia, namely the long waiting time for patients to get the doctor's diagnosis and the doctor's subjectivity. We used hundreds of X-ray images from radiology department of Sardjito Hospital, Yogyakarta, as primary data and thirty data from various sources on the internet as secondary data. Using statistical approach, we exploited statistical image feature from image histogram, examined two statistical methods of PCA and LDA transformation for feature extraction, and two minimum distance classifier in image classification. We also used histogram equalization in the image enhancement process and bicubic interpolation in image segmentation and template making. Test results on primary and secondary data images show the identification accuracy of 94% and 83.3%, respectively.