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All Journal Building of Informatics, Technology and Science
F. Safiesza, Qhairani Frilla
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Computer Science & IT

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Journal : Building of Informatics, Technology and Science

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Sentimen Analisis Social CRM Pada Media Sosial Instagram Menggunakan Machine Learning Untuk Mengukur Retensi Pelanggan F. Safiesza, Qhairani Frilla; Afdal, M; Novita, Rice; Mustakim, Mustakim
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5269

Abstract

To create and maintain a superior competitive advantage in a knowledge-based economy, businesses must be able to utilize data and manage customer relationships through the implementation of Customer Relationship Management (CRM), particularly Social CRM. Social CRM is a renewal of business strategy that is created to engage customers in a collaborative conversation and create mutually beneficial value in a trusted and transparent business environment. Seeing this development as one of the successful culinary companies in the Souvenir sector in Pekanbaru, the company must be able to process all the information obtained. Currently, the company has never analyzed comments on social media, especially the Instagram account. These comments are useful for evaluation material and can be a parameter of customer satisfaction and to see the potential for customer retention. To assess positive and negative comments on the Instagram account, sentiment analysis can be carried out using machine learning, namely 3 classification algorithms, namely Naive Bayes Classifier (NBC), Support Vector Machine (SVM) and Random Forest (RF). The sentiment results show that the SVM and NBC algorithms obtain the best accuracy of 74.26% compared to RF, and the results of the social CRM analysis show that customers are more satisfied with the company in terms of products, services, and actions taken by the company, so that the company is considered capable of retaining its customers.
Co-Authors M Afdal Mustakim Mustakim Rice Novita