Salsa Nurahma, - and Rangga Gelar Guntara, - and Syti Sarah Maesaroh, - (2025) PEMANFAATAN ALGORITMA K-MEANS UNTUK KLASTERISASI ULASAN FITUR SPINJAM PADA APLIKASI SHOPEE DI GOOGLE PLAY STORE. S1 thesis, Universitas Pendidikan Indonesia.
Abstract
Perkembangan teknologi informasi dan komunikasi mendorong hadirnya berbagai platform digital yang memfasilitasi interaksi antar pengguna. Salah satu fenomena yang muncul adalah electronic word of mouth (e-WOM) yang menjadi referensi penting bagi calon konsumen dalam menilai kualitas, manfaat, dan risiko suatu layanan. Ulasan pengguna di Google Play Store berperan besar dalam membentuk persepsi dan memengaruhi keputusan, termasuk pada fitur Spinjam di aplikasi Shopee. Namun, ulasan terkait Spinjam masih bercampur dengan ulasan layanan Shopee lainnya sehingga aspek penting mengenai kualitas dan risiko layanan berpotensi tidak teridentifikasi secara jelas. Penelitian ini bertujuan untuk mengelompokkan ulasan pengguna terhadap Spinjam dengan memanfaatkan metode text mining berbasis analisis sentimen menggunakan klasterisasi K-Means. Data penelitian berupa ulasan aplikasi Shopee di Google Play Store pada periode 1 Juni 2024 hingga 1 Juni 2025. Setelah tahap preprocessing, data diubah menjadi representasi vektor menggunakan model embedding IndoSBERT, kemudian direduksi dimensinya dengan Uniform Manifold Approximation and Projection (UMAP) untuk meningkatkan efektivitas klasterisasi. Hasil penelitian menunjukkan nilai silhouette coefficient sebesar 0,521 dengan jumlah klaster optimal k=5, yang menandakan struktur klaster terbentuk dengan cukup baik. Hasil ini membuktikan bahwa kombinasi IndoSBERT, UMAP, dan K-Means dapat meningkatkan performa klasterisasi dalam mengelompokkan ulasan berbasis teks, serta dapat memberikan gambaran yang lebih jelas mengenai pengalaman pengguna terhadap fitur Spinjam. The development of information and communication technology encourages the presence of various digital platforms that facilitate interaction between users. One of the phenomena that has emerged is electronic word of mouth (e-WOM), which is an important reference for potential consumers in assessing the quality, benefits, and risks of a service. User reviews on the Google Play Store play a big role in shaping perceptions and influencing decisions, including the Spinjam feature in the Shopee application. However, reviews related to Spinjam are still mixed with other Shopee service reviews so important aspects regarding service quality and risk are potentially not clearly identified. This study aims to group user reviews of Spinjam by utilizing a text mining method based on sentiment analysis using K-Means clustering. The research data is in the form of reviews of the Shopee application on the Google Play Store in the period from June 1, 2024 to June 1, 2025. After the preprocessing stage, the data is converted into a vector representation using the IndoSBERT embedding model, then the dimensions are reduced with Uniform Manifold Approximation and Projection (UMAP) to increase the effectiveness of clustering. The results showed a silhouette coefficient value of 0.521 with an optimal number of clusters k=5, which indicates that the cluster structure is formed quite well. This result proves that the combination of IndoSBERT, UMAP, and K-Means can improve the performance of clustering in grouping text-based reviews, and can provide a clearer picture of the user experience of the Spinjam feature.
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| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | https://scholar.google.com/citations?hl=en&view_op=list_works&gmla=AH8HC4yL9SlGQKPCIyNIMwqBfB-WCVuT8GAmBJVzJNGTQ0hhdKdriiDLuVIpC2HvbtSNNjivXkc2pFf1CrvpeA&user=Xi5fZB0AAAAJ ID SINTA Dosen Pembimbing: Rangga Gelar Guntara: 6738149 Syti Sarah Maesaroh: 6681118 |
| Uncontrolled Keywords: | Spinjam, Analisis Sentimen, K-Means, IndoSBERT, UMAP Spinjam, Sentiment Analysis, K-Means, IndoSBERT, UMAP |
| Subjects: | L Education > L Education (General) |
| Divisions: | UPI Kampus Tasikmalaya > S1 Bisnis Digital |
| Depositing User: | Salsa Nurahma |
| Date Deposited: | 10 Sep 2025 04:13 |
| Last Modified: | 10 Sep 2025 04:13 |
| URI: | http://repository.upi.edu/id/eprint/137870 |
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