Design of big data technology prototype for classification of village status based on village development index involves k-means algorithm to support village ministry Pdtt work programs

Hasugian, Paska Marto; Hutahaean, Harvei Desmon; Sinaga, Bosker; Sriadhi, S.; Silaban, Saronom

doi:10.1088/1742-6596/1811/1/012012

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…Data mining algorithms include classification analysis, cluster analysis, association analysis, and sequence analysis. The choice of algorithm directly affects the quality of pattern recognition [ 19 ]. Considering that data mining often serves ordinary users or scientific researchers, the selection of algorithms should also be closely combined with user needs.…”

Section: Methodsmentioning

confidence: 99%

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Big Data Technology in the Macrodecision-Making Model of Regional Industrial Economic Information Applied Research

Lin

2022

Computational Intelligence and Neuroscience

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In the era of Internet +, modern industry has developed rapidly, the network economy has promoted the great development of the industrial economy, and the traditional industrial economic statistics method has not been suitable for the development needs of modern enterprises. In today’s society, it can be described as the era of big data, the use of big data technology for industrial economic statistics is needed for the development of industrial modernization, and it is also a new requirement for industrial economic statistics put forward by social development. With the wide application of Internet of Things, cloud computing, mobile Internet, remote sensing, and geographic information technology in the economic field, precise economic policies have gradually developed and matured. Especially for different industries in the regional economy, according to the big data in the region, the big data mining technology and analysis technology can be used to obtain the development situation and future trend of the industrial economy in a timely and effective manner. Applying big data technology to macrodecision of regional economic information is an effective way to make macrodecision of current economy. Based on this background, this paper proposes a macroeconomic decision-making method for regional industries based on big data technology. Using data mining technology, time series data analysis methods combined with artificial intelligence analysis, the development trend of regional industries is obtained, and then the development trend of the industry is obtained. Development makes macroeconomic decisions. Taking agriculture as an example, the most popular analysis of the price trend of a certain agricultural product provides an effective reference for the development strategy of this agricultural product. The results show that the method proposed in this paper can effectively apply big data technology to the macrodecision-making of regional industrial economy. And it has better promotion significance.

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Section: Methodsmentioning

confidence: 99%

“…e choice of algorithm directly a ects the quality of pattern recognition [19]. Considering that data mining often serves ordinary users or scientific researchers, the selection of algorithms should also be closely combined with user needs.…”

Section: Data Mining Processingmentioning

confidence: 99%

Big Data Technology in the Macrodecision-Making Model of Regional Industrial Economic Information Applied Research

Lin

2022

Computational Intelligence and Neuroscience

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“…The K-means ISSN: 2302-9285  clustering algorithm has been employed, using the Weka tool. To determine the optimal value of clusters, the elbow method has been considered [31]. Model evaluation: the quality of the clusters obtained was evaluated on the basis of the number of clusters, cluster instances, sum of squared error (SSE) and construction time…”

Section: Methods For Using K-meansmentioning

confidence: 99%

Segmenting the eating behaviour of university students using the K-means algorithm

2023

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Universities that teach arts education do not only teach how to play an instrument or conduct musical ensembles; they are agents of change in eating behaviour for the praxis of teaching and dissemination of healthy education. The objective of the research was to segment the eating behaviour of students of the artistic education-music speciality of the National University of Education "Enrique Guzmán y Valle" by applying the K-means algorithm. To do this, the methodology consisted of understanding the problem, understanding the data collected, preparing the data, modelling and evaluating the model. For modelling, the free software Weka was used through the K-means clustering technique on a data matrix of 148 instances with forty-three nominal variables collected online based on an instrument designed and validated to assess eating behaviour in university students. Two was determined to be the optimal clustering for eating behaviour in university students, using the elbow method, with a distribution of 49% for the first cluster and 51% for the second cluster. The results of the study population showed that the eating behaviour of university students is adequate.

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Design of big data technology prototype for classification of village status based on village development index involves k-means algorithm to support village ministry Pdtt work programs

Cited by 2 publications

References 17 publications

Big Data Technology in the Macrodecision-Making Model of Regional Industrial Economic Information Applied Research

Big Data Technology in the Macrodecision-Making Model of Regional Industrial Economic Information Applied Research

Segmenting the eating behaviour of university students using the K-means algorithm

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