A Text Mining Approach to Analyzing the Omnichannel Retail Business Performance of the KlikIndomaret App
The evolution of Web 2.0 technology has significantly influenced the use of Android applications, enabling users to provide feedback through reviews and star ratings. In managing omnichannel retail businesses, this user-generated content serves as a valuable source of information for performance ev...
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| Format: | Article |
| Language: | English |
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Petra Christian University
2024-08-01
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| Series: | Jurnal Teknik Industri |
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| Online Access: | https://jurnalindustri.petra.ac.id/index.php/ind/article/view/28203 |
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| _version_ | 1841555779448995840 |
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| author | Akhmad Ghiffary Budianto Arief Trisno Eko Suryo Andry Fajar Zulkarnain Gunawan Rudi Cahyono Rusilawati Rusilawati Siti Fatimah Az-Zahra |
| author_facet | Akhmad Ghiffary Budianto Arief Trisno Eko Suryo Andry Fajar Zulkarnain Gunawan Rudi Cahyono Rusilawati Rusilawati Siti Fatimah Az-Zahra |
| author_sort | Akhmad Ghiffary Budianto |
| collection | DOAJ |
| description |
The evolution of Web 2.0 technology has significantly influenced the use of Android applications, enabling users to provide feedback through reviews and star ratings. In managing omnichannel retail businesses, this user-generated content serves as a valuable source of information for performance evaluation and strategic management of both online and offline operations. Large-scale user review data is well-suited for analysis through text mining, particularly in sentiment analysis, when combined with topic and keyword filtering in the business domain. This study utilizes the RoBERTa Transformer model for the sentiment classification of user reviews. Among the 520 user reviews, 211 displayed good emotion, while 309 showed negative sentiment. By applying filtering processes to topics and keywords within the omnichannel retail business domain, the study identifies "economic value" and "delivery and CRM" as priority areas for improvement. This conclusion is drawn based on the significant disparity between positive and negative sentiments. As a result, management can formulate strategies to enhance the performance and user experience of the KlikIndomaret Android application.
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| format | Article |
| id | doaj-art-bff9a6dd79a54d89b0435fad3d0b6f78 |
| institution | Kabale University |
| issn | 1411-2485 2087-7439 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Petra Christian University |
| record_format | Article |
| series | Jurnal Teknik Industri |
| spelling | doaj-art-bff9a6dd79a54d89b0435fad3d0b6f782025-01-08T03:19:56ZengPetra Christian UniversityJurnal Teknik Industri1411-24852087-74392024-08-0126210.9744/jti.26.2.131-144A Text Mining Approach to Analyzing the Omnichannel Retail Business Performance of the KlikIndomaret AppAkhmad Ghiffary Budianto0Arief Trisno Eko Suryo1Andry Fajar Zulkarnain2Gunawan Rudi Cahyono3Rusilawati Rusilawati4Siti Fatimah Az-Zahra5Universitas Lambung MangkuratUniversitas Lambung MangkuratUniversitas Lambung MangkuratUniversitas Lambung MangkuratUniversitas Lambung MangkuratUniversitas Lambung Mangkurat The evolution of Web 2.0 technology has significantly influenced the use of Android applications, enabling users to provide feedback through reviews and star ratings. In managing omnichannel retail businesses, this user-generated content serves as a valuable source of information for performance evaluation and strategic management of both online and offline operations. Large-scale user review data is well-suited for analysis through text mining, particularly in sentiment analysis, when combined with topic and keyword filtering in the business domain. This study utilizes the RoBERTa Transformer model for the sentiment classification of user reviews. Among the 520 user reviews, 211 displayed good emotion, while 309 showed negative sentiment. By applying filtering processes to topics and keywords within the omnichannel retail business domain, the study identifies "economic value" and "delivery and CRM" as priority areas for improvement. This conclusion is drawn based on the significant disparity between positive and negative sentiments. As a result, management can formulate strategies to enhance the performance and user experience of the KlikIndomaret Android application. https://jurnalindustri.petra.ac.id/index.php/ind/article/view/28203Text miningSentiment analysisOmnichannelTransformerWeb scrapping |
| spellingShingle | Akhmad Ghiffary Budianto Arief Trisno Eko Suryo Andry Fajar Zulkarnain Gunawan Rudi Cahyono Rusilawati Rusilawati Siti Fatimah Az-Zahra A Text Mining Approach to Analyzing the Omnichannel Retail Business Performance of the KlikIndomaret App Jurnal Teknik Industri Text mining Sentiment analysis Omnichannel Transformer Web scrapping |
| title | A Text Mining Approach to Analyzing the Omnichannel Retail Business Performance of the KlikIndomaret App |
| title_full | A Text Mining Approach to Analyzing the Omnichannel Retail Business Performance of the KlikIndomaret App |
| title_fullStr | A Text Mining Approach to Analyzing the Omnichannel Retail Business Performance of the KlikIndomaret App |
| title_full_unstemmed | A Text Mining Approach to Analyzing the Omnichannel Retail Business Performance of the KlikIndomaret App |
| title_short | A Text Mining Approach to Analyzing the Omnichannel Retail Business Performance of the KlikIndomaret App |
| title_sort | text mining approach to analyzing the omnichannel retail business performance of the klikindomaret app |
| topic | Text mining Sentiment analysis Omnichannel Transformer Web scrapping |
| url | https://jurnalindustri.petra.ac.id/index.php/ind/article/view/28203 |
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