A Big Data Architecture to Support Bank Digital Campaign
Irfan Wahyudin1, Salmah2

1Irfan Wahyudin, Department of Computer Science, Universitas Pakuan Bogor, West Java, Indonesia.
2Salmah, Department of Economic, Universitas Pakuan Bogor, West Java, Indonesia.
Manuscript received on 02 August 2019 | Revised Manuscript received on 25 August 2019 | Manuscript Published on 05 September 2019 | PP: 25-29 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10060782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1006.0782S719
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Bank marketers still have difficulties to find the best implementation for credit card promotion using above the line, particularly based on customers preferences in point of interest (POI) locations such as mall and shopping center. On the other hand, customers on those POIs are keen to have recommendation on what is being offered by the bank. On this paper we propose a design architecture and implementation of big data platform to support bank’s credit card’s program campaign that generating data and extracting topics from Twitter. We built a data pipeline that consist of a Twitter streamer, a text preprocessor, a topic extractor using Latent Dirichlet Allocation, and a dashboard that visualize the recommendation. As a result, we successfully generate topics that related to specific location in Jakarta during some time windows, that can be used as a recommendation for bank marketers to create promotion program for their customers. We also present the analysis of computing power usages that indicates the strategy is well implemented on the big data platform.
Keywords: Big Data, Hadoop, Topic Modeling, LDA, Bank Marketing, Product Campaign.
Scope of the Article: Big Data Security