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Creating Tour Packages in

Bandung Based onMarket Basket Analysis

Ika Pretty S Kega Kurniawan Mahendra Sunt S Ruth Teodora

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+

=Knowledge

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Knowledge Discovery Proc

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Data Mining Techniques

ClusteringClassification Association

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Data

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Our Analysis Method

Apriori

FP-Growth algorithm

Association Rules Method (Market-BasketAnalysis)

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Apriori

Apriori is an algorithm for frequent item set mining and associ

rule learning over transactional databases.It proceeds by identifying the frequent individual items in the

database and extending them to larger and larger item sets as

as those item sets appear sufficiently often in the database.

The frequent item sets determined by Apriori can be used to

determine association rules which highlight general trends in t

database: this has applications in domains such as market ba

analysis.

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Frequent Pattern Analysis

This stage looking for a combination of items that meet the

minimum requirements of the support values in the database.

Support the value of an item is obtained by the following form

The support from 2 items obtained from the following formu

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Association Rule

After all of the high frequency pattern is found, then look for

association rules which meet the minimum requirements for

calculating confidence with associative rule A => B. Confiden

value of the rule A => B is obtained by the following formula

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Our Target

Determining tour

packages

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Our Software

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Rapidminer Node Schema

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Result

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Travel

Pack

Confi0.

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Travel

Pack

Confi0.

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Travel

Pack

Confi0.

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Travel

Pack

Confi0.

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Travel

Pack

Confi0.

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Travel

Pack

Confi0.

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Conclusion

Its easy using RapidMiner as a software to analyze the dano coding required to processing the data.

Apriori method in Association rule presentsthe value of cthat can be used to determine a tour packages.

Based on the results of RapidMiner, the favorite package package with a confidence value of 0.783.

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