This research designs a system to classify apple leaf diseases using RGB (red, green and blue) color feature extraction. The essence of this research is to design a system to recognize and determine disease on apple leaves based on RGB color features using the Matlab 2024 application. The data in this research uses apple leaf images from kaggle.com, which are then cropped and adjusted to the image shape and precision in the leaf image. , Increasing the contrast of the cropped image and converting it to a grayscale image, Determining the threshold for binarization and converting the grayscale image to a binary image, Detection of green, yellow, and black/gray pixels based on RGB values ??and calculating the proportion of each color, Detection of pixels scab by filtering out black/grey pixels that do not include green or yellow pixels Classification of leaves based on the proportion of detected colors.
With the method that has been passed and uses apple leaf data, namely Healthy, Rust and Scab, each data contains 20 images with a total of 60 images and the level of accuracy is determined using the labeling method for each data and reaches the final result with an accuracy level of 86, 6667% which has a fairly accurate meaning
 

Predicting sales is an important aspect in sales development. Sales prediction simulation is an estimated calculation of the level of product sales in a certain period. Sales of pempers tend to fluctuate due to the choice of many brands available, resulting in sales having a little difficulty in estimating sales of MAKUKU diaper products at Greens Mart stores. This research aims to predict sales Pempers products. This research uses several stages of identifying problems, determining research objectives, collecting data, data collected or obtained from interviews with salespeople, managing data using Monte Carlo stages, implementing/testing data and testing results to see the accuracy of the method used. The analysis results show that Comfit M and Comfit L have almost the same level of accuracy, namely Comfit M is around 90.63% and sales of Comfit L are 90.48%. These values ??provide an indication of the level of accuracy of the sales predictions made.
 
 
 

Indonesian people are very dependent on bottled mineral water resources (gallons) and LPG gas for their daily needs. The dependence on these two resources provides a great opportunity for business people to sell gallons and LPG gas. Mu’afah shop is a basic food store that provides all household needs for daily use, one of the needs sold is gallons and lpg gas. Sales of gallons and lpg gas are erratic in this store, sometimes in a day the sales are very high but it can also be that in a day there is no single sale. This makes the seller unable to ensure the right time to restock goods. This research is qualitative using  the weight moving average  method  with mu’afah shops as research material. Based on the test results using the Weighted Moving Average method, it shows that the method runs well as expected. This can be proven by the calculation of MAD of 1.195 (gallons) and 0.792 (gas), MSE of 0.72 (gallons) and 0.34 (gas), and MAPE of 37.08 (gallons) and 33.2 (gas). So that the accuracy of test results can also be considered quite good.