A System Analysis and Design of Marketing Strategy for Improving Pineapple Agritourism

Marketing strategy is the most important factor to increase the visitor's number to an agritourism. Accordingly, the selection of the best marketing strategy is needed so it provides the maximum result. Failure of strategy implementation can cause income loss for agritourism operational. Hence, adequate system analysis and business process design are needed to reduce the possibility of failure. This paper aims to analyze the requirements of the system and to design the business process including identify and determine visitor preferences in agritourism facilities and determine association rules of the priority marketing strategy for improving pineapple agritourism. Analytical system entity construction was used to describe the requirements of the system, the Unified Modeling Language and Business Process Model and Notation 2.0 were used to design the business process. Due to the limited marketing budget, the top eight most interesting facilities based on visitor preferences were determined by RELIEF-F algorithm. Then, the actionable marketing rules were determined by ARM algorithm. The result of system analysis shows that the system requires facilities and visitor preferences as inputs, agritourism operational and visitor as stakeholders, to result in an output of association rules as improved marketing strategies. From the design, we obtained the top eight facilities are camping ground, photo spot, pineapple field tour, pineapple factory tour, culture attraction, bicycle track, gazebo, and playground. ARM results association rules design is composed of five facilities: pineapple factory, pineapple field, gazebo, playground, and photo spot. From the result, we can conclude that the agritourism operational should consider placing the facilities close to each other according to obtained association rules.

[1]  Charles S. Wasson,et al.  System Analysis, Design, and Development , 2008 .

[2]  Hong Chen,et al.  An Improved Recommendation Algorithm in Knowledge Network , 2013, J. Networks.

[3]  David Bruce Weaver,et al.  The vacation farm sector in Saskatchewan: a profile of operations , 1997 .

[4]  Fillia Makedon,et al.  Application of Relief-F feature filtering algorithm to selecting informative genes for cancer classification using microarray data , 2004 .

[5]  Ian Bowler,et al.  The development of alternative farm enterprises: A study of family labour farms in the northern Pennines of England , 1996 .

[6]  Chai-Lee Goi,et al.  A Review of Marketing Mix: 4Ps or More? , 2009 .

[7]  D. E. Hussey Why strategies fail , 1997 .

[8]  Edward Szczerbicki,et al.  PREDICTION BASED ON INTEGRATION OF DECISIONAL DNA AND A FEATURE SELECTION ALGORITHM RELIEF-F , 2013, Cybern. Syst..

[9]  Valery A. Petrushin,et al.  Emotion recognition in speech signal: experimental study, development, and application , 2000, INTERSPEECH.

[10]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[11]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[12]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[13]  Ivar Jacobson,et al.  The Unified Modeling Language User Guide , 1998, J. Database Manag..

[14]  Heinz Stoewer System Engineering Analysis, Design, and Development: Concepts, Principles, and Practices, 2nd Edition by Charles S. Wasson Hoboken, NJ, US: John Wiley @ Sons, Inc., 2016 (ISBN-978-1-118-44226-5) , 2017 .