Model Sistem Intelejensia Bisnis Untuk Perbaikan Pelayanan E-Service Pada PT. X

Rina Fitriana, Johnson Saragih, Marya Anggi Aseanita

Abstract


PT. X is one of the best airlines in Indonesia. The business intelligence system can participate as a tool to provide accurate and useful information for decision makers within the time limit that is determined to support decision making in the company's E-service services. The purpose of this study is to identify the factors that are attributes of E-Service services at PT. X. and proposed the adoption of the Business Intelligence system model for PT.X airline E-service services as a proposed service improvement. The data used are customer comment data on the Jakarta route to Singapore and Singapore to Jakarta for the period January to December 2015. The research method used is a combination to develop business intelligence research is the Pareto Diagram, Unified Modeling Language (UML), Naïve Bayes Data Mining algorithm, On Line Analytical Processing (OLAP), Extract, Transform, Loading (ETL), and Data Warehousing. From the results of data processing that has been done, it can be seen the factors attributes of E-service services are case origin, comment type, flight number, root case, and unit to charge. From the results of the calculation of the second stage of Naïve Bayes data mining, it is obtained that the greatest probability of the highest probability on the Jakarta route to Singapore is the prior probability between the customer care classification class and the suggestion form with a prior probability value of 0.92, between the inflight service classification class and the customer care priority value. equal to 1, class classification comment type compliment and customer care with prior values probability of 0.76. The prior probability of the greatest probability on the Singapore route to Jakarta is the prior probability between the customer care classification class and the suggestion form with the prior probability value of 0.92, between the inflight service classification class and the customer care with a probability prior value of 1, and between class classifications comment type compliment and customer care with a prior value of probability of 0.78. Based on the results of the largest posterior calculations, the proposed improvements were prioritized more on divisions or units to charge.

Keywords


Diagram Pareto;Unified Modelling Language (UML); Naïve Bayes;Business Intelligence (BI); Data Warehouse; On Line Analytical Processing (OLAP).

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DOI: http://dx.doi.org/10.25105/jti.v9i2.4925

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