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Journal : Instal : Jurnal Komputer

Acceleration Of The Learning Process By Implementing E-Learning To Improve The Academic Quality Of The Computer Science Program, Faculty of Science and Technology, State Islamic University of North Sumatra, Medan Sriani; Armansyah; Harahap, Lailan Sofinah
Bahasa Indonesia Vol 16 No 05 (2024): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v16i05.312

Abstract

In today's digital era, a satisfaction-service is needed in the field of education. The satisfaction service is in the form of the quality-of-a-Study Program-which-is-very much determined by-the-quality of the service provided, where-quality-service can be identified through-student-satisfaction when carrying out-learning. One of the-service-breakthroughs-by utilizing information technology that can be applied by the Computer Science-study-program, Faculty of Science-and-Technology, State-Islamic University-of North-Sumatra-Medan today-is by applying-technology-based learning with-the E-Learning program.
Sentiment Analysis of Comments on X Regarding Interactive Videos for Children Using Naive Bayes Harahap, Lailan Sofinah; Salsabila, Awalia; Nasution, Najwa Fadiyah
Bahasa Indonesia Vol 16 No 05 (2024): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v16i05.315

Abstract

This study aims to analyze the sentiment of social media comments on interactive videos for children using the Naive Bayes algorithm, which is known to be effective in text classification and sentiment analysis. Data were collected from social media platforms regarding comments on popular interactive videos for children, and then processed through cleaning, tokenization, stopwords removal, and stemming stages. Naive Bayes algorithm was used to classify the comments into three categories: positive, neutral, and negative. The analysis showed that 48.3% of the comments were positive, 47.2% were neutral, and 4.5% were negative. Positive sentiments indicated more support for the educational aspects and interactivity, while negative sentiments focused more on content quality and concerns about screen addiction and age appropriateness. The accuracy of the analysis reached 55.6%, which demonstrates the effectiveness of the Naive Bayes algorithm. This research provides useful insights for content developers and policymakers to understand the public's response to interactive children's videos and improve content quality to better suit children's educational and developmental needs