Tsaabitah Anggraini, . (2024) ANALISIS SENTIMEN PENGGUNA APLIKASI MOBILE QUICK COMMERCE ASTRO MENGGUNAKAN METODE SUPPORT VECTOR MACHINE. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
The COVID-19 pandemic has reshaped shopping patterns and propelled the growth of digital platforms to meet consumer needs. Quick commerce (q-commerce) has emerged as a solution for fast delivery of everyday items through digital platforms. In Indonesia, q-commerce has experienced increased popularity during the pandemic. However, by the end of 2022, many q-commerce companies faced challenges, with some even having to close their services. Despite this, several companies like Astro have managed to survive. Nonetheless, to remain competitive in the q-commerce industry, sustained efforts are needed to improve responsiveness and service quality. Sentiment analysis is crucial for understanding user perspectives and responses to Astro's service. This study analyzes user sentiment toward Astro using the Support Vector Machine (SVM) method. The results show a majority of positive reviews (91.57%) and a minority of negative ones (8.43%). Positive reviews highlight user satisfaction with various aspects of the service, such as the ease of app usage, availability of comprehensive product selections, fast delivery of items, and good service from the provider. Meanwhile, negative reviews point out several issues faced by users, including late deliveries, technical or administrative problems related to user accounts or addresses, and dissatisfaction with customer service. The SVM evaluation demonstrates satisfactory performance, with an accuracy rate of 97%, precision of 98%, recall of 99%, f1-score of 99%, and AUC of 91%.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2010512047] [Pembimbing 1: Ika Nurlaili Isnainiyah] [Pembimbing 2: Nindy Irzavika] [Penguji 1: Ruth Mariana Bunga Wadu] [Penguji 2: Zatin Niqotaini] |
| Uncontrolled Keywords: | Sentiment Analysis, Q-commerce, Astro, Support Vector Machine |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Sistem Informasi (S1) |
| Depositing User: | TSAABITAH ANGGRAINI |
| Date Deposited: | 09 Sep 2024 08:05 |
| Last Modified: | 09 Sep 2024 08:05 |
| URI: | http://repository.upnvj.ac.id/id/eprint/30857 |
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