No abstract

No abstract

No abstract

Natural disasters are unpredictable events in both the location and the time of occurrence. Natural disasters can cause property loss and can even be claimed by life. To reduce the amount of losses, the handling of rapid evacuation should be conducted by the SAR team to help victims of natural disasters. But in fact, there are a lot of obstacles in the evacuation process. Starting from the difficulty of searching the victim’s body, the difficulty of the terrain reached until limited equipment needed. In this study designed the body detection system of natural disaster victims using image processing where the shooting of victims was carried out using drones aiming to help find victims in a difficult or prone location when reached directly by humans. Background of the problem, in this research proposed a development method for the detection of victims of natural disaster that aims to help the SAR team as well as natural disaster volunteers in the search for victims who are in a difficult to reach place. The method used by You Only Look Once (YOLO) uses the Python programming language associated with image processing. From the research has been obtained accuracy detection object disaster victims with good accuracy. Based on the experiments that have been done obtained a good accuracy value of 95.49% with epoch of 12000.

The wealth of opinions expressed by users on micro-blogging sites can be beneficial for product manufacturers of service providers, as they can gain insights about certain aspects of their products or services. The most common approach for analyzing text opinion is using machine learning. However. opinion data are often imbalanced, e.g. the number of positive sentiments heavily outnumbered the negative sentiments. Ensemble technique, which combines multiple classification algorithms to make decisions, can be used to tackle imbalanced data to learn from multiple balanced datasets. The decision of ensemble is obtained by combining the decisions of individual classifiers using a certain rule. Therefore, rule selection is an important factor in ensemble design. This research aims to investigate the best decision combination rule for imbalanced text data. Multinomial Naïve Bayes, Complement Naïve Bayes, Support Vector Machine, and Softmax Regression are used for base classifiers, and max, min, product, sum, vote, and meta-classifier rules are considered for decision combination. The experiment is done on several Twitter datasets. From the experimental results, it is found that the Softmax Regression ensemble with meta-classifier combination rule performs the best in all except in one dataset. However, it is also found that the training of the Softmax Regression ensemble requires intensive computational resources.