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Applying association rule mining to determine losses occurrences on sistem operasi terpadu (sot) data

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Skk migas as a supervisor to control the activities of upstream oil and natural gas made by psc (production sharing contractor) need some method to perform data analysis from sistem operasi terpadu (sot) data to discover knowledge. Based on that, this research will focus on analyzing loss production opportunity of sot data using association rule mining (arm) techniques. Arm is one of the most significant and well researched techniques of data mining for discovering interesting correlation or association among sets of data to maximizing the knowledge discovered in sot data. This research utilizes rapid miner as data mining open source software to produce arm rules. However, the arm result produces thousands of rules that are redundant. The post processing analysis needs to be done to reduce redundant rules and to sort the rules by their priority. Since production loss can be determined as a risk, then risk probability and impact matrix analysis used to prioritize rules. The objective interestingness measure also used by using support and confidence value to sort rules. The final results are encouraging and also produced valuable information to identify sets of loss that cause highest impact on financial value for every occurrence of this set.

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Availability

M00288

(Rack Thesis)

Available

Detail Information

Series Title

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Call Number

288

Publisher

: Swiss German University., 2014

Collation

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Language

English

ISBN/ISSN

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Classification

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Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

MIT
association rule mining
loss production opportunity
objective interestingness measure
rapid miner
risk matrix

Specific Detail Info

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Statement of Responsibility

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