Wijokumoro, Mohammad Rival (2024) ANALISIS SENTIMEN ULASAN PRODUK SEPATU VENTELA DI SHOPEE MENGGUNAKKAN PCA, SVM, DAN LOGISTIC REGRESSION. S1 thesis, Universitas Mercu Buana - Menteng.
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Abstract
Penelitian ini mengkaji analisis sentimen terhadap ulasan konsumen produk sepatu Ventela di platform Shopee. Dengan menggunakan metode Support Vector Machine (SVM) dan Logistic Regression, serta teknik reduksi dimensi Principal Component Analysis (PCA), tujuan penelitian ini adalah untuk memperoleh pemahaman yang lebih mendalam mengenai sentimen konsumen. Penelitian bertujuan untuk mengklasifikasikan ulasan konsumen sebagai positif atau negatif, serta menginvestigasi efektivitas analisis sentimen ulasan produk dalam menunjukkan potensi pembelian oleh konsumen. Selain itu, penelitian ini membandingkan keefektifan metode SVM dan Logistic Regression yang diperkuat dengan PCA untuk menentukan metode yang lebih efektif dalam analisis sentimen. Salah satu aspek penting penelitian adalah integrasi feature selection melalui PCA, yang bertujuan untuk mengidentifikasi atribut ulasan yang paling relevan dalam memprediksi sentimen dan meningkatkan keakuratan analisis. Hasil yang diharapkan adalah memberikan wawasan berharga bagi merek Ventela, memungkinkan perusahaan untuk memanfaatkan umpan balik konsumen untuk peningkatan kualitas produk dan pelayanan. Hal ini, pada akhirnya, akan membantu dalam merumuskan strategi pemasaran yang lebih efektif bagi Ventela dan pihak yang tertarik pada analisis sentimen dan belanja online. This study focuses on the sentiment analysis of customer reviews for Ventela shoe products on the Shopee platform. By employing Support Vector Machine (SVM) and Logistic Regression analytical methods in conjunction with Principal Component Analysis (PCA) for dimension reduction, the study aims to gain a deeper understanding of consumer sentiments. The research intends to classify consumer reviews as either positive or negative and to explore the effectiveness of product review sentiment analysis in indicating the potential for consumer purchases. Additionally, the study compares the efficacy of SVM and Logistic Regression methods, enhanced by PCA, to determine which method is more effective in sentiment analysis. An integral aspect of the research is the incorporation of feature selection through PCA, aimed at identifying the most relevant review attributes in predicting sentiment and improving analysis accuracy. The expected result is to provide valuable insights for the Ventela brand, enabling the company to leverage consumer feedback for the enhancement of product quality and service provision. This, in turn, will help in formulating more effective marketing strategies for Ventela and others interested in the intersection of sentiment analysis and online shopping.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41520110072 |
| Uncontrolled Keywords: | Sentimen Analisis, Konsumen Review, Support Vector Machine (SVM), Regresi Logistik, Principal Component Analisis (PCA) Sentiment Analysis, Consumer Review, Support Vector Machine (SVM), Logistic Regression, Principal Component Analisis (PCA) |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | SILMI KAFFA MARISKA |
| Date Deposited: | 07 Aug 2024 04:25 |
| Last Modified: | 07 Aug 2024 04:25 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/90068 |
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