The welfare of teachers in Indonesia, especially honorary teachers, is still very poor when compared to other ASEAN countries. There is a gap between the salaries of teachers in Indonesia and the salaries of teachers in several ASEAN countries. There have been several demonstrations carried out by teachers, especially honorary teachers, because the salary received is not in accordance with the performance that has been carried out. On the other hand, there is a problem of high unemployment in Indonesia. The factors causing the high unemployment rate in Indonesia are the mismatch of qualifications and the low quality of education in Indonesia, as well as limited job vacancies and the high need for employment. To overcome these problems, a new innovation is needed that can help the community in creating jobs and improving the quality of education in Indonesia. The Pasar Ilmu educational marketplace application was developed to overcome these problems. The development of this Pasar Ilmu application uses the prototype system development method. This research aims to create a system or application of the Pasar Ilmu educational marketplace that can help improve the quality of education in Indonesia as an application that can help honorary teachers to get additional income, help reduce unemployment in Indonesia, and provide flexible courses or lessons for students.

Food allergies are medical conditions caused by particular immunological reactions brought on by exposure to certain foods. All age groups can experience food allergies, albeit the prevalence varies between children and adults, with children experiencing this condition more frequently than adults. Find food ingredients or substances that can trigger allergies, often known as allergens. This project attempts to determine whether or not the food includes allergies by applying the SVM data mining method to a public dataset of food goods and allergens that was acquired via Kaggle. High accuracy, effective memory use, and the ability to handle non-normally distributed data are some of the benefits of the SVM method. Data collection is the first step in the research process. Data pre-processing, which includes data transformation, handling missing values, and copy objects, comes next. Validation comes next. Split validation with 90% training data and 10% testing data, 10-fold cross validation, and split validation with an 80%–20% ratio were all compared in this study. The SVM method is applied after the dataset has passed validation, and the confusion matrix is used for the last evaluation step. SVM has an accuracy rate of 97.24% when using 10-fold cross validation, according to the accuracy value produced by the validation process comparison. Split validation yields an accuracy value of 97.50% when the ratio of training data to testing data is 90% to 10%. In contrast, an accuracy rate of 98.75% was achieved by using split validation with a ratio of 80% and 20%.