PERBANDINGAN MODEL ANALISIS SENTIMEN MENGGUNAKAN MACHINE LEARNING METHOD TERHADAP ULASAN PEMBELIAN PADA SETLER BOSCA LIVING (STUDI KASUS: BOSCA LIVING)

PANGESTU, RYAN (2024) PERBANDINGAN MODEL ANALISIS SENTIMEN MENGGUNAKAN MACHINE LEARNING METHOD TERHADAP ULASAN PEMBELIAN PADA SETLER BOSCA LIVING (STUDI KASUS: BOSCA LIVING). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Bosca Living, a star seller on Shopee and Tokopedia, faces the challenge of customer sentiment analysis. This research evaluates models and methods to strengthen responses to customer feedback. In previous studies, the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction technique was tested. Machine learning models such as Random Forest and Decision Tree were employed, but a more comprehensive comparison was deemed necessary based on Bosca Living's assessment. This research proposes a model comparison through preprocessing, feature extraction, and parameter determination stages using GridSearchCV. Machine learning models like Random Forest and Decision Tree are evaluated with StratifiedKFold to reduce the risk of overfitting. The results of the research provide deep insights, guiding Bosca Living in improving responses to customer feedback. This approach is expected to optimize business strategies, support continuous improvement, and be responsive to market dynamics and evolving customer needs. Keywords: Bosca Living, Customer Sentiment Analysis, Machine Learning Models, Feature Extraction, Customer Feedback Response Bosca Living, star seller di Shopee dan Tokopedia, menghadapi tantangan analisis sentimen pelanggan. Penelitian ini mengevaluasi model dan metode untuk memperkuat respons terhadap tanggapan pelanggan. Pada penelitian sebelumnya, teknik ekstraksi fitur Term Frequency-Inverse Document Frequency (TF-IDF telah diuji. Model-machine learning seperti Random Forest dan Decision Tree telah digunakan, namun perbandingan lebih mendalam diperlukan sesuai penilaian Bosca Living. Penelitian ini mengusulkan perbandingan model melalui tahap pre-processing, ekstraksi fitur, dan penentuan parameter dengan GridSearchCV. Model machine seperti Random Forest dan Decision Tree dievaluasi dengan StratifiedKFold mengurangi risiko overfitting. Hasil penelitian memberikan wawasan mendalam, membimbing Bosca Living meningkatkan respons terhadap umpan balik pelanggan. Pendekatan ini diharapkan mengoptimalkan strategi bisnis, mendukung perbaikan berkelanjutan, dan responsif terhadap dinamika pasar serta kebutuhan pelanggan yang berkembang. Kata Kunci: Bosca Living, Analisis sentimen pelanggan, Model-machine learning, Ekstraksi fitur, Respons terhadap umpan balik pelanggan

Item Type:	Thesis (S1)
Call Number CD:	FIK/SI. 24 045
Call Number:	SIK/18/24/022
NIM/NIDN Creators:	41819120050
Uncontrolled Keywords:	Bosca Living, Analisis sentimen pelanggan, Model-machine learning, Ekstraksi fitur, Respons terhadap umpan balik pelanggan
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 > 000.01-000.09 Standard Subdivisions of Computer Science, Information and General Works/Subdivisi Standar Dari Ilmu Komputer, Informasi, dan Karya Umum 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 > 006 Special Computer Methods/Metode Komputer Tertentu 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 > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	27 Feb 2024 06:23
Last Modified:	27 Feb 2024 06:23
URI:	http://repository.mercubuana.ac.id/id/eprint/86593

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