Land and Building Tax (PBB) is a type of area regulated by the government in determining the amount of tax for implementation and development as well as increasing the prosperity and well-being of the people. Based on taxpayer compliance data in Tanjung Keliling Plantation, the results of tests carried out using the Clustering algorithm can determine the variables of ownership area, hamlet name and payment level. Clusters 1,2,3 of 600 PBB taxpayer data, namely where cluster 1 has 166 data, can be grouped based on the Ownership Area of "500,001-600,000m2" with the Hamlet Name "Ujung Bangun" and the Payment Level "Quite Good". Cluster 2 consists of 196 data, which can be grouped based on ownership area "200,001-300,000m2" with the hamlet name "Karang Jati" and payment level "fairly good".  And Cluster 3 with a total of 238 data, can be grouped based on the Ownership Area "400,001-500,000m2" with the Hamlet Name "Mojosari" and the Payment Level "Quite Good".

Members of the KPPS (Voting Organizing Group) are responsible for organizing voting in a polling station (TPS) during general elections in Indonesia. They are the spearhead in carrying out the democratization process by supervising and ensuring the continuity of elections honestly, fairly, and transparently. The duties of KPPS members include preparing TPS before voting begins, receiving and examining voters, supervising the election process to ensure compliance with applicable regulations, counting votes after voting is complete, reporting election results, and maintaining security and order around TPS. Decision support system is a Decision support system or Decision Support System (DSS) is an interactive system that supports decisions in the decision-making process through alternatives obtained from data processing results. The purpose of this study is to facilitate the recruitment of members of the Voting Organizing Group (KPPS). The research method is Weighted Aggregated Sum Product Assessment (WASPAS). WASPAS is to find the most appropriate priority location choices using weighting. The results of this study are that the development of this support system can help the KPU in selecting or selecting KPPS members and this decision support system as a tool in developing KPPS members by viewing or using criteria according to the criteria needed using the WASPAS method.
 
 
 

The advancement of information technology and globalization has transformed shopping behaviors, with social media becoming the primary platform for online shopping. This study aims to analyze the online shopping preferences of residents in Binjai City through social media using clustering methods, specifically the K-Means algorithm. Data were collected via a questionnaire targeting 523 respondents in Binjai City, focusing on variables such as gender, age, and the social media platforms used. Clustering methods are employed to group online shopping data into representative clusters, helping identify community preferences for specific social media platforms for shopping. Matlab is used to process the data and generate relevant insights into online shopping patterns, facilitating decision-making regarding the selection of the most suitable social media platform for transactions.The findings of this study are expected to provide valuable insights for both sellers and buyers in determining the most effective social media platforms for online shopping. Additionally, the results will be useful for residents of Binjai City to understand and choose the social media platforms that best meet their online shopping needs.