In the pandemic after the occurrence of COVID-19, there are significant changes in economic statistics, this is influenced by economic activity that is not stable compared to before. The price of food staples was also affected by the pandemic, meetings between buyers and traders, usually held in traditional and modern markets, were hampered due to government restrictions on the territory. This causes a decrease in existing transactions in the market, therefore foodstuffs have the possibility of price volatility. Multiple Linear Regression (MLR) algorithm is a method that can overcome predictions with the type of seasonal dataset prediction, therefore the MLR algorithm is implemented to predict food prices, especially in the modern market, based on the predicted prices, then a decision support system is made to make an alternative ranking of food selection accumulation. Based on the available food ingredients there are nutrients contained in these foods, therefore experts are needed to determine the weighting of nutrition in each food ingredient. Simple Additive Weighting (SAW) method is a method that can do weighting and ranking of alternatives. Therefore the SAW method is applied to rank alternative food staples that have nutritional weight and price. Based on the application of MLR, the error level testing concluded that the prediction of the price of food "Rice" has the least error results compared to other foodstuffs with the value of MSE 21261.04, MAE 145.79, RMSE 145.812, MAPE 0.81 while for the best R2 values found at food ingredients "Garlic" with a value of 0.576. Based on testing of the application of SAW, the same results are obtained between manual calculations and calculations provided by the system, so that the accuracy of the system can be ascertained.

Traveling activities are increasingly being carried out by people in the world. Some tourist attractions are difficult to reach hotels because some tourist attractions are far from the city center, Airbnb is a platform that provides home or apartment-based rentals. In lodging offers, there are two types of hosts, namely non-super host and super host. The super-host badge is obtained if the innkeeper has a good reputation and meets the requirements. There are advantages to being a super host such as having more visibility, increased earning potential and exclusive rewards. Support Vector Machine (SVM) algorithm classification process by these criteria data. Data set is unbalanced. The super host population is smaller than the non-super host. Overcoming the imbalance, this over sampling technique is carried out using ADASYN and SMOTE. Research goal was to decide the performance of ADASYN and sampling technique, SVM algorithm. Data analyses used over sampling which aims to handle unbalanced data sets, and confusion matrix used for testing Precision, Recall, and F1-SCORE, and Accuracy. Research shows that SMOTE SVM increases the accuracy rate by 1 percent from 80% to 81%, which is influenced by the increase in the True (minority) label test results and a decrease in the False label test results (majority), the SMOTE SVM is better than ADASYN SVM, and SVM without over sampling.

Diabetes is a metabolic disease in which blood sugar rises high. If blood sugar is not controlled properly, it can cause a variety of critical diseases, one of which is diabetes. The purpose of this study was to find out the results of comparing the performance values of Naïve Bayes and C4.5 algorithms with 7 different scenarios in the classification of diabetes that will be tested for accuracy, precision, and recall performance. The method used in this study is descriptive, and the source of skunder data obtained from the data of diabetic patients available on Kaggle with the format .csv issued by Ishan Dutta as many as 520 data and 17 fields. The tool used for data analysis is Rapidminer for the process of classification and performance testing of Naïve Bayes algorithm and C4.5 Algorithm. Our results showed that the C4.5 algorithm (scenario 4) had good results in the classification of diabetes compared to Naïve Bayes' algorithm (scenario 2) where the performance of the C4.5 algorithm had an accuracy of 99.03%, precision 100%, and recall 98.18%.

Central Processing Unit (CPU) and External Graphics Processing Unit (eGPU) technology are known as overclocks which aim to make the device exceed the benchmarks set by the device maker. Until now there is no determination to rank the two hardware within certain limits such as hardware price range and year-by-year. Therefore, it is necessary to process the ranking of the hardware using Simple Additive Weighting (SAW) to obtain a ranking range and determine the weight per type of hardware analyzed. It can be classified using Naïve Bayes to determine results of criteria combination between two hardware to determine possible criteria into "not good" and "good". This classification used to determine probability criteria of choosing a combination of CPU and eGPU hardware. The results of this study are getting the best CPU and eGPU every year using SAW and then classifying it for pricing. In testing conducted on application of Naïve Bayes using 80% of training data has 2776 data and 20% of testing data has 695 data that will be tested for accuracy, precision, recall, and F1-score. For results of tests that have been carried out get 0.78 accuracy results, precision 1, Recall 0.764, and F1-Score 0.866.