In the business of selling goods, there must be goods that do not sell well and sell well. How to make unsold items sell well by giving customers a discount or discount strategy. The goal is to provide discounted prices to attract customers' attention and increase sales turnover. Prediction to give the right discount presentation is needed in the discount strategy. How to determine discount prediction using linear regression method, looking for line equations by training data taken from Kaggle.com. The data were trained to find the constants and coefficients of the independent variables. The resulting line equation will be tested to determine the discount prediction that will be given and calculate the error value. The data used is painting sales data taken from Kaggle.com, a total dataset of 1525, and a prediction calculation is made with 225 lines of test data. The input variable used is the price of the painting, and the output variable is the discount presentation. The process of evaluating the prediction results by calculating the MAE value, the difference between the actual data and the predicted data, the MAE value from the trial is 0.108.

Sales in supermarkets during the Covid 19 pandemic will change normally. Sales in normal times may always be crowded and a lot. It is different during a pandemic because several layers of society experience economic difficulties. Supermarkets that cannot solve this problem may have a loss or may have to close temporarily. From this background, we propose a predictive analysis of sales of electronic and health goods in supermarkets using Linear Regression and Moving Average methods. The data used in this research were taken from the Kaggle dataset website. The data consists of several types of goods, but we use the electronics and health categories. The purpose of this research to analyze the two algorithms in predicting electronic sales and health data. The tool used to help analyze is python. How to analyze the two algorithms with MSE (Mean Square Error) and RMSE. The results of the analysis show that the Moving Average method has the best performance as evidenced by the MSE and RMSE values ??of electronic data which are 57,603, 7.59, health data are 50,489, 7,106. While the linear regression method prediction results on electronic data and health, the MSE and RMSE values ??are 114.79, 10.71, 59.965, 7,744.

Abstract An apple is a fruit that is widely planted in mountainous or cold regions, for example in Malang. Apples are fruits that have many colors, there are green, yellow, and red colors. With a variety of colors that make consumers feel confused whether the apples to be eaten are sweet or sour. Because almost most consumers do not know the type of apple that will be purchased. Sometimes the type of apple that will be purchased is green, but because it does not know ripe or raw, it is wrong to choose. In order to help consumers in knowing the level of maturity of apple again, a system was made. With the aim to be able to classify the level of maturity of the apple again. So that the system created will display information whether the apple is more ripe or raw. The system will process the image of the apple again and take the GLCM texture features (intensity, contrast, energy, smoothness, entropy, skewness). And the process of determining fruit maturity using the KNN method. The system was built using the matlab tool, with 200 datasets, consisting of 130 training datasets, and 70 testing datasets. In applying the KNN algorithm to determine the maturity level of apples, the accuracy results are 51.4%. With output data that is not in accordance with the target number of 34 data and according to the target number of 36 data.