scholarly journals Apriori Algorithm On Car Rental Analysis With The Most Popular Brands

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 47 ◽  
Author(s):  
Leo Fernando Panjaitan ◽  
Yopi Handrianto ◽  
Achmad Nurhadi
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Business Strategy ◽  
A Priori ◽  
Apriori Algorithm ◽  
Research Material ◽  
Operational Activities ◽  
Common Function

Nowadays, vehicle rental has become a common function for companies that have busy operational activities. Every company in carrying out operational activities requires a vehicle that is always there when needed. PT. Agung Solusi Trans is a vehicle rental company that rents various vehicle brands commonly used by customers to rent vehicles. In addition, PT. Agung Solusi Trans is also difficult to get updated information regarding the level of sales per period. Therefore, we need a decision support system and a method that can be used to design a business strategy that can provide an efficient and effective information, namely data mining using the a priori algorithm association method. The researcher specializes in taking only vehicle types as research material by selecting fifteen brands, including Agya, Yaris, Sienta, Calya, Avanza, Innova, Rush, Vios, Altis, Camry, Fortuner, Alphard, Hi Ace, Voxy, and Hilux. In analyzing the data, the writer uses a priori algorithm calculation by testing the hypothesis of two variables between the value of support and the value of confidence. After that, apriori algorithm is calculated using Tanagra. Based on the analysis done by the author, that the brands most sought after by customers are Calya, Avanza, Hilux. From these results can be used by PT. Agung Solusi Trans to prepare vehicle brands that are widely leased by customers and increase brand inventory.

scholarly journals Application of Apriori Algorithm Method in Sales Analysis of Mountain Bag Brands in Post Stores 1

2020 ◽  
Vol 4 (1) ◽  
pp. 1-10
Author(s):  
Agus Salim ◽  
Mochammad Nizar
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Young People ◽  
Decision Support Systems ◽  
Support Systems ◽  
A Priori ◽  
Business Strategies ◽  
Apriori Algorithm ◽  
Research Material ◽  
Sports Shoes

Nowadays, climbing mountains has become a lifestyle for young people. Outdoor industries that produceclothing, bags and sports shoes participate in developing and following the desires of the market. Eachcompany in producing its products has a special brand. Shop Pos 1 is one of the shops that sell variousclimbing equipment commonly used by climbers to climb mountains. In addition, Pos 1 stores also find itdifficult to get updated information about the level of sales per period. Therefore, we need a decision support systems and methods that can be used to determine business strategies that can provide efficientand effective information, namely data mining using a priori technology association methods. The authorchooses mountain bag products only as research material by selecting brands, completing Avtech, Consina,Co-tracks, Cozmed, Eiger, Forester, Rei, Loss. In analyzing the data, the writer uses a priori algorithmcalculation by testing the hypothesis of two variables between the value of support and the value of trust.After that, a priori algorithm is calculated using Tanagra. Based on analysis conducted by the author, theoperator most preferred by climbers is Avtech, Consina, Cozmed. From these results, it can be used by Pos 1to prepare brand inventory of mountain bag products that are widely bought by buyers and increasebrand inventory.Keywords: Bag Brand, Data Mining, apriori algorithm.

scholarly journals DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR ANALYSIS OF EMERGENCIES ON URBAN TRANSPORT

2021 ◽  
Vol 12 (4) ◽  
pp. 6-18
Author(s):  
Valerii Lakhno ◽  
Borys Husiev ◽  
Andrii Blozva ◽  
Andrii Sahun ◽  
Tetiana Osypova ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Road Network ◽  
A Priori ◽  
Urban Transport ◽  
Urban Street ◽  
The Road ◽  
Emergency Situations ◽  
Transport Problems

The article discusses some aspects of the design of a decision support system (DSS) module during the analysis of major accidents or emergencies in urban transport in large cities, megalopolises, as well as in Smart City. It is shown that the computational core of such a DSS can be based on the methods of cluster analysis (CA). It is shown that the implementation of even basic spacecraft algorithms in the computational core of the DSSS allows an iterative search for optimal solutions to prevent a large number of emergencies in urban transport by establishing characteristic signs of accidents and emergencies and measures of proximity between two objects. It is shown that such a toolkit as DSS can provide all interested parties with a scientifically grounded classification of multidimensional observations, which summarize the set of selected indicators and make it possible to identify internal connections between emergencies in urban transport. The DSS module for analyzing emergencies in urban transport is described. It has been found that to solve such a problem, it is possible to use the "weighted" Euclidean distance in the computational core of the DSS. It is this parameter that makes it possible to take into account the significance of each characteristic of emergency situations in urban transport, which, in turn, will contribute to obtaining reliable analysis results. It is shown that the spacecraft methods can also be in demand when, along with the analysis of emergency situations in urban transport, problems of designing and reconstructing the configurations of urban street-road networks are solved in parallel. This task, in particular, requires an analysis phase (not least using CA methods) in order to minimize unnecessary uncompensated costs in the event of errors in the road network. When solving such a problem, sections of the urban street and road network are analyzed in order to identify problem areas that need reconstruction or redevelopment. The use of CA methods in such conjugate problems is due to the absence of a priori hypotheses regarding the classes that will be obtained as a result.

Knowledge Discovery and Data Mining Applications in the Healthcare Industry

2015 ◽  
pp. 241-262
Author(s):  
Iman Barazandeh ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Knowledge Discovery ◽  
Support System ◽  
Healthcare Industry ◽  
Actionable Knowledge ◽  
System View ◽  
Data Market

The healthcare industry is one of the most attractive domains to realize the actionable knowledge discovery objectives. This chapter studies recent researches on knowledge discovery and data mining applications in the healthcare industry and proposes a new classification of these applications. Studies show that knowledge discovery and data mining applications in the healthcare industry can be classified to three major classes, namely patient view, market view, and system view. Patient view includes papers that performed pure data mining on healthcare industry data. Market view includes papers that saw the patients as customers. System view includes papers that developed a decision support system. The goal of this classification is identifying research opportunities and gaps for researchers interested in this context.

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