This study discusses Acute Respiratory Infections (ARI) at the Bah Biak Health Center. Bah Biak Health Center is one of the health centers in Marihat in Pematangsiantar city. Every day the number of patients who come and perform medical treatment at this health center is quite a lot. The high number of patient visits at this puskesmas causes the amount of medical record data to be very large as well. So far, data containing information about patients at the puskesmas has not been used properly. This information can actually be used as knowledge for puskesmas, especially for patients who have a history of ARI disease. Therefore, the purpose of this study was to classify patients with ARI at the Puskesmas. The method used is K-Means clustering. The results of this study were able to classify ARI patients into 2 clusters, cluster 1 gave a high recommendation of 72 patients, and cluster 2 gave a low recommendation of 70 patients. The cluster process stops at the 5th iteration of data. Based on the manual calculation process using Ms. Excel and testing using Rapidminer 5.3, yielded the same value. It can be concluded that in this case, the K-Means algorithm can classify ARI patients at the Bah Biak health center well.

Medicine is an object that is formulated by medical personnel to assist in preventing, healing, recovering, and improving the health of patients. Pharmacies are places that provide various types of drugs that are needed by patients, but pharmacies often have problems in providing drugs which result in a shortage of goods when someone needs it. aspect. This problem will be solved by using the C4.5 algorithm based on data obtained from the Franch Farma Pharmacy. The purpose of this study is to predict the supply of drugs that will be needed by dividing them into 6 variables, namely the name of the item, type of drug, inventory, initial stock, ending stock, and goal. The research test process uses Rapidminer software to create a decision tree. And the results obtained are 12 rules to predict drug supplies to be carried out by Franch Farma Pharmacy with an accuracy rate of 80.00%. From the results of the analysis, it is hoped that it can help pharmacists in making drug supplies more effective and efficient.

This study aims to optimize profits and minimize losses from product sales at X Market has achieved in the future. The data used in this study were obtained directly from X Market Trade by observing and interviewing. X Market Tradingis one of the industries that produces and sells food products to be marketed in the region and outside the region. The data to be processed is the result of the sale of food products at X Market uses the adaptive neuro fuzzy inference system (ANFIS) method which is a combination of Fuzzy Logic and Artificial Neural Networks. The data used is monthly sales data from 2018 to 2020 as many as 36 data. From a total of 36 data, it will be divided into 2 types of training and test data distribution, namely 90:10 with a total of 60 epochs and a learning rate range of 0.1 – 0.9. From the research results obtained the highest accuracy of 88.55% on 90% training data and 10% test data with a learning rate of 0.6. It was concluded that the ANFIS method could be implemented in predicting the sales of tofu. By doing this research is expected to provide input to X Market in optimizing profits and minimizing losses from product sales in the future.

Sales is an effort or concrete steps taken to move a product, whether in the form of goods or services, from producers to consumers as the target. The main purpose of sales is to bring in profits or profits from products or goods produced by producers with good management. Prediction of sales of goods is one way to maintain the stability of sales of goods, the prediction results obtained can be taken into consideration for making decisions in business management planning. Prediction is done to find out the forecast of future sales, to meet consumer needs. One of the problem solving methods used in this research is the Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS is a method that uses a Neural Network to implement a Fuzzy Inference System. By using the ANFIS method as a solution for solving the case of prediction of peanut bread sales in the future so that the prediction results can be input to UD. Family in order to provide production stock in accordance with the predictions made and can increase sales of peanut bread at UD.Family optimally