REAL-TIME COMPANY DECISION SUPPORT SYSTEM USING MICROBLOGS STUDY CASE BOLT! 4G ULTRA LTE (Indonesian Mobile Broadband Service)
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45720 |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><relation>http://repository.mercubuana.ac.id/45720/</relation><title>REAL-TIME COMPANY DECISION SUPPORT SYSTEM USING MICROBLOGS STUDY CASE : BOLT! 4G ULTRA LTE (Indonesian Mobile Broadband Service)</title><creator>HIDAYATULLAH, TAUFIK</creator><subject>005.5 General Purpose Application Programs/Program Aplikasi dengan Kegunaan Khusus</subject><subject>651 Office Services/Layanan Kantor</subject><subject>651.8 Computer Application for Office Management/Aplikasi Komputer untuk Manajemen Perkantoran</subject><subject>658.01-658.09 [Management of Enterprises of Specific Sizes, Scopes, Forms; Data Processing]/[Pengelolaan Usaha dengan Ukuran, Lingkup, Bentuk Tertentu; Pengolahan Data]</subject><subject>658.05 Data Processing Computer Applications/Pengolahan Data Aplikasi Komputer</subject><description>With 3 million users and stable customer growth, BOLT! is a service with a high potential to be extracted information using Data Mining approach. By the end of February 2018, BOLT!’s Twitter account reached more than 54,000 followers, 220,000 tweet and replies, and no fewer than 100 complaints per day. In social media, content analysis and text mining are often used to analyze user-generated text and support decision-making. It is an opportunity for the government or even company, to obtain information that is important to improve public satisfaction and enhance the performance of their products and services.
This study proposed the Real-time Company decision support system that handles the complaint from the user by using microblogs data. Data collection in this application is using real-time tweet data which fetched from Streaming API services that provided by Twitter. After that, the raw data tweet being processed using text mining techniques, including preprocessing, the classifying process and extracting location methods. To detect and classify the complaint from the user, this research applied Naïve Bayes classifier. With a total of 15808 tweets data are trained which is balance in the number annotated as a complaint or either non-complaint. Afterward, the model is evaluated by using K-Fold Cross Validation. The result shows the classifier gets the value of Accuracy 94.86%, High precision with 99.18%, and Recall 91.29%. To extract the location from a complaint tweet, NLP technique being used with accuracy 76.8% which is pretty good to predict and get the location. This Real-time Company Decision Support System using Microblogs is very useful for BOLT! in order to support the decision making based on the data retrieved from the system. With Real-time Data Streaming, Real-time Map visualization, and data analytics tools, this decision support system will be needed for BOLT! or other companies that used microblogs as media for customer service.
Keywords: Twitter, Text Mining, Naïve Bayes, Microblog, Location Extraction</description><date>2018-11-28</date><type>Thesis:Thesis</type><type>PeerReview:NonPeerReviewed</type><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/1/1.%20Title.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/2/2.%20Abstract.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/3/3.%20Statement%20Sheet.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/5/5.%20Validation%20Sheet.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/4/4.%20Acknowledgement.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/6/6.%20Table%20of%20Contents.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/7/7.%20List%20of%20Tables.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/8/8.%20List%20of%20Figure.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/9/9.%20List%20of%20Equation.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/10/10.%20Chapter%201.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/11/11.%20Chapter%202.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/12/12.%20Chapter%203.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/13/13.%20Chapter%204.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/14/14.%20Chapter%205.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.mercubuana.ac.id/45720/15/15.%20References.pdf</identifier><identifier> HIDAYATULLAH, TAUFIK (2018) REAL-TIME COMPANY DECISION SUPPORT SYSTEM USING MICROBLOGS STUDY CASE : BOLT! 4G ULTRA LTE (Indonesian Mobile Broadband Service). S1 thesis, Universitas Mercu Buana Jakarta. </identifier><recordID>45720</recordID></dc>
|
| language |
eng |
| format |
Thesis:Thesis Thesis PeerReview:NonPeerReviewed PeerReview Book:Book Book |
| author |
HIDAYATULLAH, TAUFIK |
| title |
REAL-TIME COMPANY DECISION SUPPORT SYSTEM USING MICROBLOGS STUDY CASE : BOLT! 4G ULTRA LTE (Indonesian Mobile Broadband Service) |
| title_sub |
BOLT! 4G ULTRA LTE (Indonesian Mobile Broadband Service) |
| publishDate |
2018 |
| topic |
005.5 General Purpose Application Programs Program Aplikasi dengan Kegunaan Khusus 651 Office Services Layanan Kantor 651.8 Computer Application for Office Management Aplikasi Komputer untuk Manajemen Perkantoran 658.01-658.09 [Management of Enterprises of Specific Sizes Scopes Forms; Data Processing]/[Pengelolaan Usaha dengan Ukuran Lingkup Bentuk Tertentu; Pengolahan Data] 658.05 Data Processing Computer Applications Pengolahan Data Aplikasi Komputer |
| url |
http://repository.mercubuana.ac.id/45720/1/1.%20Title.pdf http://repository.mercubuana.ac.id/45720/2/2.%20Abstract.pdf http://repository.mercubuana.ac.id/45720/3/3.%20Statement%20Sheet.pdf http://repository.mercubuana.ac.id/45720/5/5.%20Validation%20Sheet.pdf http://repository.mercubuana.ac.id/45720/4/4.%20Acknowledgement.pdf http://repository.mercubuana.ac.id/45720/6/6.%20Table%20of%20Contents.pdf http://repository.mercubuana.ac.id/45720/7/7.%20List%20of%20Tables.pdf http://repository.mercubuana.ac.id/45720/8/8.%20List%20of%20Figure.pdf http://repository.mercubuana.ac.id/45720/9/9.%20List%20of%20Equation.pdf http://repository.mercubuana.ac.id/45720/10/10.%20Chapter%201.pdf http://repository.mercubuana.ac.id/45720/11/11.%20Chapter%202.pdf http://repository.mercubuana.ac.id/45720/12/12.%20Chapter%203.pdf http://repository.mercubuana.ac.id/45720/13/13.%20Chapter%204.pdf http://repository.mercubuana.ac.id/45720/14/14.%20Chapter%205.pdf http://repository.mercubuana.ac.id/45720/15/15.%20References.pdf http://repository.mercubuana.ac.id/45720/ |
| contents |
With 3 million users and stable customer growth, BOLT! is a service with a high potential to be extracted information using Data Mining approach. By the end of February 2018, BOLT!’s Twitter account reached more than 54,000 followers, 220,000 tweet and replies, and no fewer than 100 complaints per day. In social media, content analysis and text mining are often used to analyze user-generated text and support decision-making. It is an opportunity for the government or even company, to obtain information that is important to improve public satisfaction and enhance the performance of their products and services.
This study proposed the Real-time Company decision support system that handles the complaint from the user by using microblogs data. Data collection in this application is using real-time tweet data which fetched from Streaming API services that provided by Twitter. After that, the raw data tweet being processed using text mining techniques, including preprocessing, the classifying process and extracting location methods. To detect and classify the complaint from the user, this research applied Naïve Bayes classifier. With a total of 15808 tweets data are trained which is balance in the number annotated as a complaint or either non-complaint. Afterward, the model is evaluated by using K-Fold Cross Validation. The result shows the classifier gets the value of Accuracy 94.86%, High precision with 99.18%, and Recall 91.29%. To extract the location from a complaint tweet, NLP technique being used with accuracy 76.8% which is pretty good to predict and get the location. This Real-time Company Decision Support System using Microblogs is very useful for BOLT! in order to support the decision making based on the data retrieved from the system. With Real-time Data Streaming, Real-time Map visualization, and data analytics tools, this decision support system will be needed for BOLT! or other companies that used microblogs as media for customer service.
Keywords: Twitter, Text Mining, Naïve Bayes, Microblog, Location Extraction |
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IOS5909.45720 |
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Universitas Mercu Buana |
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134 |
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Perpustakaan Universitas Mercu Buana |
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Econmics/Ilmu Ekonomi Communication/Komunikasi Engineering/Ilmu Teknik Measurement/Teknik Desain, Pengujian, Pengukuran, Kualitas, Perawatan, Pemeliharaan, Perbaikan |
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Jakarta Barat |
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DKI JAKARTA |
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