Epilepsy is a disease that attacks the nerves. To detect epilepsy, it is necessary to analyze the results of an EEG test. In this study, we compared the naive bayes, random tree forest and K-nearest neighbor (KNN) classification algorithms to detect epilepsy. The raw EEG data were pre-processed before doing feature extraction. Then, we have done the training in three algorithms: KNN Classification, naïve bayes classification and random tree forest. The last step was validation of the trained machine learning. Comparing those three classifiers, we calculated accuracy, sensitivity, specificity, and precision. The best trained classifier is KNN classifier (accuracy: 92.7%), rather than random tree forest (accuracy: 86.6%) and naïve bayes classifier (accuracy: 55.6%). Seen from precision performance, KNN Classification also gives the best precision (82.5%) rather than Naïve Bayes classification (25.3%) and random tree forest (68.2%). But, for the sensitivity, Naïve Bayes classification is the best with 80.3% sensitivity, compare to KNN 73.2% and random tree forest (42.2%). For specificity, KNN classification gives 96.7% specificity, then random tree forest 95.9% and Naïve bayes 50.4%. The training time of naïve bayes was 0.166030 sec, while training time of random tree forest was 2.4094sec and KNN was the slower in training that was 4.789 sec. Therefore, KNN Classification gives better performance than naïve bayes and random tree forest classification.

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The anisotropic shape variations of the rectum, especially in the ROP regions, should be considered when determining a planning risk volume (PRV) margins for the rectum associated with the acute toxicities.

As a developing country, Indonesia has been consuming energy with 114 Million Ton Oil Equivalent (MTOE) and estimated that the demand for energy will increase up to 167,4 MTOE in 2050 by Indonesian Energy Ministry. It is also estimated that natural gas will play the role in fulfilling the energy demand in Indonesia. However, in utilizing the natural gas spread of the regions, Indonesia still lack of natural gas infrastructures. As natural gas infrastructures are playing a vital role on those problems, the condition of supply demand, capacity of infrastructure and the effectiveness of the route need to be adjusted and considered. In this study, system dynamic method is employed in order to forecast the supply and demand of natural gas in East Java Province. In addition, a simulation is carried out to optimize and simulate the scenario model of the natural gas infrastructure at certain time-year period. With the constraint and condition given to the system dynamics, a supply-demand condition in East Java area that mainly comes from electrical power generation, industry and household is assessed. Based on developed scenarios, the model is expected to fulfil the needs of natural gas in East Java. The possibility of establishing new LNG terminal in certain location or expanding the capacity of existing facilities are also considered in this study.