Today's lifestyle and eating patterns tend to be irregular due to busyness. People prefer eating foods that are fast and easy to obtain, but often lack knowledge of the nutritional content in them. These eating patterns lead to unbalanced nutrition and can cause various health problems and diseases, such as overweight and obesity. Due to a lack of information, people often turn to drugs instead of learning about healthy diets, making it difficult for them to determine what menu to choose or what type of food to consume. While there have been many studies to recommend healthy food based on user preferences, there is currently no recommender system that includes serving size and budget for each daily food recommendation that is implemented in a chatbot framework. This study proposes using ontology and the Semantic Web Rule Language (SWRL) to store knowledge in the ontology and then process it using SWRL to produce food recommendations based on user preferences. From a sample of user data which obtained 170 recommended meal menus. System performance is pretty good. Based on the validation results from nutritionists, the precision value was 0.852941, the recall was 1, and F-score of 0.920634 So that a healthy food recommendation system can be used to help the user follows a diet that meets his nutritional needs and is within his budget needed
One of the sectors affected by the covid-19 pandemic is the education sector. SMKN 1 Peureulak Timur as the only vocational high school in Peureulak Timur sub-district, East Aceh must continue to provide education services for all students of the best quality. One of the efforts to improve educating and teaching skills for teachers at SMKN 1 Peureulak Timur is by providing e-learning training. One of the e-learning media that is often used is Goggle Classroom because of its complete features and quite easy to operate. Therefore, the activity that will be carried out at this community dedication is Google Classroom training. It is hoped that after receiving this Google Classroom training, teachers of SMKN 1 Peureulak Timur can improve their performance in facing the Industrial Revolution 4.0.
Tourism in the city of Bandung has various potentials in the field of culture, regional specialties, buildings, and other tourist attractions. On the Tripadvisor page there are many reviews from users who have visited tourist attractions in the city of Bandung. In this case, user reviews are an important element for analysis. The analysis process is carried out using rule-based sentiment analysis. In conducting the review analysis, we use vaderSentiment to weight the positive and negative values. Positive values are subtracted from negative values to get a compound value and converted to a rating value. The rating value obtained is then processed using the Cosine Similarity and Singular Value Decomposition methods to obtain recommendations for tourist attractions in the city of Bandung. For this method, we use the Root Mean Square Error method as a measure of the level of accuracy between the predicted values. The results of the measurement of the level of accuracy produce a value of 3,489 in the Cosine Similarity method, while the Singular Value Decomposition method gets a value of 1,231. The value in the Singular Value Decomposition method is smaller than the Cosine Similarity method with a difference of 2,258 values
The Sumedang Larang Kingdom is one of the kingdoms in Indonesia which was founded by Prabu Tajimalela in 721 AD. The Sumedang Larang Kingdom is known as the national history of Indonesia. Still, most of the current generation does not know the history of the Sumedang Larang Kingdom, especially the younger generation. Therefore, we developed a question-and-answer system to seek information about the Sumedang Larang Kingdom. With the development of information technology, research on question answering systems is applied to research on Biomedical Questions to produce correct answers. Our system will help literacy about the Sumedang Larang Kingdom for the younger generation, especially students, and increase Indonesian cultural assets. The QA system aims to generate and provide precise short answers to user questions by automatically using information extraction and natural language processing methods. To collect and create questions, we use the concept of ontology. In addition, we use the Natural Language Naïve Bayes method to answer user questions. We built a QA system that can help students find information about the history of the Sumedang Larang Kingdom. Based on the accuracy of the results of testing the method we propose. In our evaluation, we involve the Decision Tree method as the base model. We note that the accuracy of the Naïve Bayes method is higher than that of the Decision Tree. The accuracy result of Naïve Bayes at the ratio of 8:2 and 7:3 is 67%, while the Decision Tree is only 56%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.