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All Journal International Journal Software Engineering and Computer Science (IJSECS) Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Eldina, Ratih
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Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science

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Implementation of Augmented Reality to Enhance Alphabet Letter Understanding at Paud Bintang Jonggol Lestari, Sri; Eldina, Ratih; Sumartha, Divaretta K.; Apipah, Nida
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 1 (2024): APRIL 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i1.2179

Abstract

Implementing Augmented Reality (AR) in Early Childhood Education (PAUD) promises children an interactive and immersive learning experience. In this study, we evaluate the potential of AR in improving the quality of early childhood education. AR can help PAUD educators create a more engaging learning environment where children can learn through games that support the development of motor skills and conceptual understanding. However, challenges such as access to technology, adequate teacher training, and appropriate curriculum planning need to be addressed. Policy support, comprehensive teacher training, and selecting the proper AR application are critical factors in implementing AR in PAUD. This technology has great potential to change how children learn and better prepare them for the future.
Sentiment Analysis of Cigarette Use Based on Opinions from X Using Naive Bayes and SVM Tundo; Eldina, Ratih; Setiawan, Kiki; Fajri, Raisah
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.947

Abstract

The research employs Naive Bayes and Support Vector Machine (SVM) classification techniques to analyze attitudes toward cigarette consumption based on Twitter user opinions. Twitter, being one of the most popular social media platforms, serves as an excellent source for gauging public sentiment on various issues, including cigarette smoking, referred to here as "X." The diverse array of opinions poses a challenge for accurate sentiment classification. This study evaluates the effectiveness of the Naive Bayes and SVM algorithms in categorizing sentiment as positive, negative, or neutral. Data is collected through web scraping, and preprocessing steps such as text cleaning, tokenization, and stemming are implemented. The performance of the classification is assessed using metrics like accuracy, precision, recall, and F1-score. The results indicate that SVM outperforms Naive Bayes in sentiment analysis related to cigarette use. These findings provide new insights into public opinion and aim to assist policymakers in developing effective tobacco control strategies.
Co-Authors Apipah, Nida Aula, Raisah Fajri Setiawan, Kiki Sri Lestari Sumartha, Divaretta K. Tundo, Tundo