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All Journal International Journal of Language and Ubiquitous Learning J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Apriansyah Hutagalung, Carli
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Computer Science & IT Languange, Linguistic, Communication & Media Social Sciences Other

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Strengthening English Language Learning through Artificial Intelligence-Based Mobile Applications: A Comparative Study in Formal and Informal Contexts Nurmala, Ira; Reza, Ali; Apriansyah Hutagalung, Carli; Pattiasina , Petrus Jacob; Malintang, Jimmy
International Journal of Language and Ubiquitous Learning Vol. 2 No. 4 (2024)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijlul.v2i4.1764

Abstract

Background. AI-based mobile applications are revolutionizing language learning by offering personalized, adaptable tools. Their impact differs across various learning environments, prompting the necessity for a comparative study to understand these variances better. Purpose. This study aims to compare the effectiveness of AI-based mobile applications for English language learning in formal classroom settings versus informal self-study environments. The goal is to determine how these tools perform across different contexts and identify the optimal conditions for their use. Method. A comparative study was conducted involving high school and university students in formal educational settings and adult learners in informal self-study settings. Over three months, participants utilized AI-based language learning applications. Data collection involved pre- and post-tests to measure learning outcomes, alongside surveys and interviews to gauge user experiences and preferences. Results. Analysis revealed significant improvements in both learning environments, with formal settings showing a 12% increase in test scores and informal settings an 8% increase. Students in formal settings more frequently engaged with interactive features, whereas informal learners gravitated towards self-study tools. Engagement and interaction levels were notably higher in formal educational settings compared to informal ones. Conclusion. AI-based mobile applications significantly enhance English language learning, particularly in structured, formal environments. The findings underscore the importance of tailoring these tools to fit different learning contexts. While both formal and informal settings benefit from these applications, formal education environments seem to leverage their interactive features more effectively, resulting in higher engagement and better learning outcomes. This study highlights the need for educators and developers to consider context-specific strategies to maximize the benefits of AI-based language learning tools.
Evaluation of TF-IDF Extraction Techniques in Sentiment Analysis of Indonesian-Language Marketplaces Using SVM, Logistic Regression, and Naive Bayes Budi Lestari, Verra; Apriansyah Hutagalung, Carli
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 1 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/j-koma.v8i1.05

Abstract

This study evaluates the application of TF-IDF feature extraction in sentiment analysis of Indonesian-language marketplace product reviews using Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) algorithms. The dataset, sourced from Kaggle, comprises 831 reviews (385 positive, 446 negative), processed through preprocessing steps including text cleaning, tokenization, stopword removal, and stemming. The data was split into 80% training and 20% testing sets. Results show that Logistic Regression with TF-IDF achieved the highest performance, with 90.4% accuracy, 91.8% precision, 90.4% recall, and 90.9% F1-measure, outperforming Naïve Bayes (87.4% accuracy) and SVM (89.8% accuracy). Logistic Regression effectively captures linear relationships in TF-IDF features, while Naïve Bayes struggles with emotional context, and SVM requires complex parameterization. TF-IDF is efficient for explicit reviews but limited in handling complex semantic contexts like sarcasm. This study confirms that Logistic Regression combined with TF-IDF is the most effective approach for sentiment analysis of Indonesian marketplace reviews, with recommendations for future exploration of methods like word embedding.
Co-Authors Budi Lestari, Verra Ira Nurmala Malintang, Jimmy Petrus Jacob Pattiasina Reza, Ali