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All Journal Irfani SINTECH (Science and Information Technology) Journal
Ristiana, Ina
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Computer Science & IT Education Social Sciences

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Komparasi Naïve Bayes dan SVM Analisis Sentimen RUU Kesehatan di Twitter Widyanto, Tetrian; Ristiana, Ina; Wibowo, Arief
SINTECH (Science and Information Technology) Journal Vol. 6 No. 3 (2023): SINTECH Journal Edition December 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i3.1433

Abstract

This research focuses on sentiment analysis regarding the plan to ratify the Health Bill which has become a hot topic of conversation on social media, especially Twitter. This research aims to classify tweets that reflect various opinions regarding the Health Bill, including support, rejection and neutrality. In this research, the author uses two types of classification algorithms, namely the Multinomial Naïve Bayes Algorithm and the Support Vector Machine (SVM) Algorithm. Previously, tweets were labelled using the Lexicon InSet dictionary. The research was conducted in the Python programming language and using Google Collaboratory. Data validation was carried out using the K-fold cross-validation method. The research results indicate that both algorithms predominantly produce positive sentiments over negative ones. However, SVM with a linear kernel achieves a higher accuracy rate of 0.87, compared to Multinomial Naïve Bayes, which has an accuracy of 0.82. SVM also records a precision of 0.87, recall of 0.97, and an F1-score of 0.91, while Multinomial Naïve Bayes shows a precision of 0.81, recall of 0.98, and an F1-score of 0.89. Overall, this research confirms that SVM excels in text sentiment classification, while Multinomial Naïve Bayes also provides good results in recognising positive and negative sentiment. These results have important implications for understanding public sentiment regarding the Health Bill on the Twitter platform.
Transformasi Pendidikan Bisnis di Era Digital: Analisis Studi Pustaka Mutmainah; Ristiana, Ina; Rokimin
Irfani Vol. 22 No. 1 (2026): Irfani
Publisher : LP2M IAIN Sultan Amai Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30603/irfani.v22i1.7499

Abstract

Digital transformation has brought significant changes in various aspect of life, including higher education, particularly in business education. The transformation of business education in the digital era has become a necessity to address the increasingly complex and dynamic global challenges. The shift in the learning paradigm from conventional methods to digital requires adjustments in the curriculum, teaching methods, and the competencies of both lecturers and students to remain relevant to the needs of business and industry. The purpose of this literature review is to identify and synthesize existing literature on the adoption of digital technology in business education, as well as its impact on the curriculum, teaching methods, and learning outcomes. The methodology used in this study is a qualitative approach with a library research method, analyzing recent research from various journals, books, and scholarly articles. The main findings indicate that digital transformation provides broad access to education, more interactive learning, and improved digital competencies for students, although challenges such as resistance to change and limited infrastructure remain. This study concludes that a holistic and sustainable strategy for implementing digital transformation is crucial, including digital competency training for educators and the development of adequate information technology infrastruktur. Recommendations focus on strengthening collaboration between educational institutions, business practitioners, and technology developers to support adaptive and innovative business education in the digital era.
Co-Authors Arief Wibowo MUTMAINAH Rokimin Widyanto, Tetrian