Search

Big data refers to big data, fast data processing, diversity of data structures, and data values so that it is not possible to be processed with outdated methods. Big data technology is used in various industrial sectors. Big data technology is the whole technology that can handle Big Data. Some of the uses of big data are based on the largest significant data traffic sources such as social media, financial transactions, public data, sensor data, and corporate data. The same is the case with the status of so many villages in Indonesia, so it is better to use big data for the classification of village status based on the village index build by involving an algorithm process. This research aims to produce a description of the role of big data in supporting activities in a grouping. The method used is a qualitative approach related to data collection based on scientific work with the source of data information needed to study literature techniques from various studies that have been published in national and international journals. A decision is made that big data has been widely used in different circles to facilitate performance and speed up the decision-making process.

Big data technology is the overall technology that can handle the processing associated with analyzing the data to explore the potential that is in it. Some of the uses of big data are based on the biggest data traffic sources, such as social media, financial transactions, public data, censorship data, and company data. The problem found in the accumulation of village status data that has not been utilized in the decision-making process to determine the status of the next village and the absence of an application to process the data stored in the data database so that a tool is developed to analyze the buildup. The results of this study are to produce a prototype design that is Unified Modeling Language (UML) design, Form Design, and Database Design that will be used to develop Big Data Technology in determining the classification of village status based on the Village Build index.

Data mining is a technique of extracting information that has not been known before in a collection of data in the database. Data mining has been applied in various fields that require extracting information, some of the work that can be generated with data mining is classification, prediction, and data grouping. In this study, an analysis of village data collection was carried out to explore the potential or knowledge of the data that has been presented with the main objective of producing a grouping of village status. To support clustering or grouping activities the K-means algorithm used with the general process is to carry out the modeling process without supervision and is one of the methods for grouping data with a system partition, with the principle of allocating each data to the centroid or the closest average, work steps conducted in support of this research is to collect data related to the analysis of data grouping and proceed with the calculation process in accordance with the work steps of the K-means algorithm, the amount of data used as a test of 303 villages scattered in the old Padang regency. The results of calculations by displaying a new group of Cluster 0 is occupied by 120 villages, cluster 1 with a total data of 123 villages, Cluster 2 with a total of 6 villages, cluster 3 with a total of 33 villages while cluster 4 with a total of 21.

Consumer purchasing patterns are a form of purchases made by consumers, whether someone or a lot of people to get the desired item by making a purchase transaction. One characteristic of the purchase pattern is the existence of acquiring something through exchanging money. This study aims to create an application that is used in determining consumer purchasing patterns by applying a priori algorithms and using Visual Basic 2010 as a tool for determining consumer purchase patterns. This application uses a priori algorithm calculation method where the sample consumer purchase data will be sorted and calculated by providing the value of the minium support and configuration parameters and based on the results of confidence the largest number of conclusions such as: can be used as information for determining sales, the application of a priori algorithms can provide information pattern combination item set from consumer purchase data that is with support above 15% and confidence above 50% on item set.