Dengue Fever is among the world’s fastest-spreading mosquito-borne illnesses. In Indonesia, more than 33 percent of the world community is in danger. The Coastal Zone is one of the region most at risk of contracting dengue fever, particularly from the social and environmental sectors, so an early diagnostic study must deal with this efficiently and effectively. This research aimed to predict and identify the coastal areas with the most severe dengue fever potential to avoid dengue fever. The methodology used is a neural network-based sensitivity Analysis and multiple linear regression. Bagan Deli, Sibolga, Tapanuli Tengah, Langkat, Medan are samples of the coastal regions used in this study. The reports used was secondary data for dengue fever patients suffering and meteorological parameters, the model used in [5-5-1] for prediction, for five years from 2014-2019. The results showed which Langkat (0.4936), Serdang Bedagai (0.4695), The Middle Of Tapanuli (0.4399), Medan (0.4313), and Sibolga (0.3133) are perhaps the most prevalent areas affected by dengue fever with a value of 89.8 percent. The Result is temperature and humidity are the conditions that most affect the transmission of dengue fever.
Indonesia is one of the countries with high plant diversity. Almost every region in Indonesia has distinctive plants and may not be present in other countries. Based on these facts required a strategic step to record and identify plants in Indonesia. One method that can be used to leaf image feature extraction is the Gray Level Co-occurrence Matrix (GLCM). This research will implement k-Nearest Neighbor (k-NN) method to classify type of plants based on leaf texture. The classification result based on GLCM using k-NN classifier showed that the accuracy using k = 3 was 83%. The use of parameter k influence classification results, the greater the value of k then the accuracy would be smaller. Classification errors for some types of leaf images occurred because the value extraction traits generated by GLCM was very similar and had a small range of values.
The purpose of this service is to provide training and assistance to PKK members to be able to manage finances and manage business finances. The method used is a social approach. Where the preparation stage is carried out by conducting surveys and observations to identify problems and analyze the situation and community needs. Then carried out the implementation stage with two main activities, namely training and mentoring. During the training, PKK members were given knowledge in financial management and practical financial management with applications. Assistance is provided when participants practice directly using the application. Based on the evaluation carried out, it is known that the results of this service have increased the knowledge of PKK members about financial management with an average posttest score of 70. Through the assistance carried out, each PKK member already has an account in one of the applications. financial management, and can be used for recording every transaction, debt to recording stock of goods that are still available
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