Dengue Hemorrhagic Fever (DHF) is one of the serious public health problems in Indonesia, including in Rada Mata Village. DHF prevention is highly dependent on community behavior, especially housewives, who play an important role in maintaining environmental cleanliness. Objective: This study aims to determine the relationship between the level of knowledge and attitude with maternal actions regarding DHF prevention in Rada Mata Village. Method: This study used an analytical study design with a cross-sectional approach. The sample consisted of 100 mothers selected through purposive sampling technique. Data were collected using a structured questionnaire that measured the level of knowledge, attitude, and DHF prevention actions. Data analysis was performed using the chi-square test. The results showed that there was a significant relationship between the level of knowledge and DHF prevention actions (p = 0.002), as well as between attitude and DHF prevention actions (p = 0.001). Conclusion: The level of knowledge and attitude of mothers is closely related to DHF prevention actions. Efforts are needed to improve education and health promotion for housewives to reduce the incidence of DHF in Rada Mata Village.
 
 

The purpose of this research is to create a JST (artificial neural network) model that can forecast population growth at the Population and Civil Registration Office of West Sumba Regency. population growth at the Population and Civil Registration Office of West Sumba Regency. Regency. Regional development planning must consider the increasing number of population, therefore proper forecasting is essential to encourage sustainable policies and initiatives. sustainable policies and initiatives. Because it can identify complex patterns in past data and produce more accurate forecasts than traditional techniques, an ANN model is used. traditional techniques, the ANN model is used. The data used in this study is the population growth of Southwest Sumba Regency over the past including characteristics such as birth and death rates and population movements. deaths and population movements. The backpropagation algorithm is used to optimize the multilayer perceptron (MLP) architecture for ANN training. Separating the data into training and testing sets and assessing the models model using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) based on the error. Error (RMSE) based on the prediction error are the steps involved in the training process. involved in the training process. The research findings show that, with a low level of error, the artificial neural network model can estimate the population increase in Southwest Sumba Regency with a reasonable level of accuracy. reasonable level of accuracy. The model is expected to serve as a reference for relevant authorities to better manage population data and as a tool to create more focused and successful population policies.

The growth of the internet has influenced the tourism industry because the internet makes it easier for individuals to obtain reviews about places to visit and because the internet is a tool used by tourist site managers to assess the quality of their offerings. The increase in the number of tourists of almost two million in just three years in West Sumba is proof of this influence. Social media is a tool that people use to interact with each other online; some people have multiple accounts on platforms such as Instagram, WhatsApp, Facebook, Telegram, Twitter, and so on. Tourists can receive recommendations for tourist attractions based on price and type of trip desired through a tourist attraction recommendation system that uses the KNN algorithm. Three factors were used in this research: activity, type of tourism, and type of price. An accuracy of 63.16% is found in the test results using the KNN algorithm and the Rapid Miner application with a K value of 5. The analysis results show that the K-Nearest Neighbor (K-NN) approach can be used as a guideline for recommending tourist destinations to visitors in West Sumba.