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All Journal Jurnal Ilmiah Komunikasi Makna Semantik LITE: Jurnal Bahasa, Sastra, dan Budaya Techno.Com: Jurnal Teknologi Informasi Jurnal Penelitian Ekonomi dan Bisnis JAIS (Journal of Applied Intelligent System) Journal of Information System JOURNAL OF APPLIED INFORMATICS AND COMPUTING Infotekmesin Jurnal Pengabdian Literasi Digital Indonesia
Yuventius Tyas Catur Pramudi
Universitas Dian Nuswantoro
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Other

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Tingkat Keterampilan Literasi Informasi Mahasiswa Berdasarkan Kerangka Shapiro di Era Digital Yuventius Tyas Catur Pramudi; Edi Faisal; Gabriel T.Y. Darmesta
JOINS (Journal of Information System) Vol 10 No 1 (2025): Edisi Mei 2025
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v10i1.12934

Abstract

Perkembangan pesat teknologi digital telah mengubah cara mengakses dan menggunakan informasi. Penelitian ini bertujuan menganalisis tingkat literasi informasi mahasiswa Universitas Dian Nuswantoro (UDINUS) berdasarkan kerangka Shapiro di era digital. Menggunakan metode deskriptif kuantitatif dengan kuesioner daring, data dikumpulkan dari 69 mahasiswa yang sedang mengambil mata kuliah Literasi Informasi. Hasil menunjukkan 92,8% mahasiswa berada pada tingkat menengah, 5,8% tingkat tinggi, dan 1,4% tingkat rendah. Temuan mengindikasikan bahwa meskipun mahasiswa memiliki keterampilan pencarian informasi dasar, tetapi perlu peningkatan dalam evaluasi kritis dan penggunaan informasi secara etis. Penelitian ini memberikan rekomendasi untuk pengembangan kurikulum dan metode pembelajaran guna meningkatkan kompetensi literasi bagi mahasiswa. Penguatan mata kuliah Literasi Informasi sebaiknya mencakup pembelajaran berbasis proyek (Project Based Learning), penggunaan AI tools secara etis, dan latihan berpikir kritis berbasis kasus nyata (Case Based Learning).  
Sentiment Classification of Health Education YouTube Comments Using IndoBERT Embeddings with Logistic Regression and Naïve Bayes Andre Septa Wijaya; Amiq Fahmi; Yuventius Tyas Catur Pramudi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13016

Abstract

Class imbalance is a common issue in sentiment classification of social media data, particularly in mental health–related discussions where certain sentiment classes are underrepresented. This study focuses on sentiment classification of mental health–related YouTube comments by utilizing IndoBERT as a pre-trained language model to generate contextual text embeddings. Sentiment classification is subsequently performed using conventional machine learning algorithms, namely Logistic Regression and Naïve Bayes. The research framework includes data collection through the YouTube Data API, text preprocessing, semi-manual sentiment labeling into positive, neutral, and negative classes, and dataset partitioning using an 80:20 train–test split. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied exclusively to the training data to prevent data leakage. Feature representation is obtained from IndoBERT embeddings with a dimensionality of 768. Model performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results show that Logistic Regression outperforms Naïve Bayes, achieving an accuracy of 78%, compared to 56% for Naïve Bayes. This indicates that Logistic Regression is more effective in handling dense contextual embeddings generated by transformer-based models. Overall, the findings demonstrate that combining contextual embeddings with data balancing techniques can improve sentiment classification performance in mental health–related social media analysis, particularly in low-resource language settings.
Co-Authors Alzami, Farrikh Andre Septa Wijaya Bintang Dewi Fajar Kurniatullah Edi Faisal Edi Noersasongko Edi Sugiarto Fahmi Amiq Fikri Budiman Gabriel T.Y. Darmesta Idham Herlambang Karis Widiyatmoko Lalang Erawan Lisa Mardiana Megantara, Rama Aria Moh Yusuf, Moh Moh. Yusuf Mulyanto, Edy Nurrohim, Novan Nuryana, Ima Putra, Noveryan Satria Ricardus Anggi Pramunendar Saputra, Resha Mahardhika Sudiarti, Ni Made Watimena Widyatmoko Karis