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Nanda Kurnia Wardati
Universitas Muhammadiyah Jember
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Journal : journal of computer science and informatics engineering

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Comparative Analysis of TF-IDF, TF-IDF+WordNet, and Sentence-BERT for News Document Retrieval Using Cosine Similarity Galib Haftha Zuhayir; Wiwik Suharso; Nanda Kurnia Wardati
Journal of Computer Science and Informatics Engineering Vol 5 No 3 (2026): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i3.1835

Abstract

The rapid growth of digital news volume has produced information overload, while exact keyword-matching retrieval remains vulnerable to synonymy and polysemy, causing relevant documents to be missed. Prior studies generally compare only two document representation methods on small-scale datasets, leaving a gap in controlled evaluations that jointly compare statistical, lexical-hybrid, and neural approaches on a large-scale news domain. This study compares three document representation methods, namely Term Frequency-Inverse Document Frequency (TF-IDF), TF-IDF with WordNet-based query expansion, and Sentence-BERT (all-MiniLM-L6-v2), for news document retrieval using Cosine Similarity on the BBC News Dataset (14,305 documents with a hierarchical Ground Truth of 5 Topics and 51 Subtopics). Ten queries were evaluated using Precision@K, Recall@K, F1-Score@K (K=5, 10, 20), and execution time. The results show that Sentence-BERT consistently outperforms the other methods with a Precision@5 of 0.84, compared to TF-IDF (0.56) and TF-IDF+WordNet (0.52), while TF-IDF remains the fastest at online query time (23.86 ms per query). WordNet expansion actually reduces precision and increases execution time without a proportional accuracy gain. These findings confirm that transformer-based semantic representations are superior for news domains with high lexical variation, while TF-IDF remains relevant for computationally constrained real-time systems
Integrating Word Embeddings and IMDb Web Scraping for Keyword-Based Movie Recommendation Andani Chacha Cahya Dewi; Deni Arifianto; Nanda Kurnia Wardati
Journal of Computer Science and Informatics Engineering Vol 5 No 3 (2026): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i3.1839

Abstract

The rapid growth of the film industry and streaming platforms has led to information overload and filter bubbles that make it difficult for users to find content matching their narrative preferences. Prior content-based filtering approaches relying on word-frequency methods (TFIDF) suffer from a semantic gap and commonly depend on a single public dataset and a reference title (seed movie) as input. This study combines a public dataset with IMDb web-scraping results (a maximum population of 5,000 titles) and applies a Skip-gram Word2Vec model to represent movie synopses as 200-dimensional semantic vectors, paired with Cosine similarity to measure the closeness between a user's free-text keyword and movie synopses without requiring a seed movie. Data were split using an 80:20 Holdout method, and algorithm performance was evaluated on a Top-3 Recommendation window using Precision@K, Recall@K, and Mean Reciprocal Rank (MRR), with ground truth validated by two experts through Inter-Annotator Agreement. Testing on 25 queries produced a Precision@3 of 0.5333, Recall@3 of 0.7800, and MRR of 0.7300. These results indicate that integrating word embeddings with web scraping yields semantically relevant movie recommendations from free keyword input, though comparisons with baseline methods are needed for more definitive performance claims
Co-Authors Abdul Jalil Alvan Maulana Azaini Andani Chacha Cahya Dewi Andri Wahyudi Ari Dwi Prasmono Arifianto, Deni Bagus Setya Rintyarna Desta, Senki Ferdian Agung Wahyudi Fitriana, Fitriana Galib Haftha Zuhayir Hari Sukmono Herry Setyawan Heru Bastomi Lolanda Hamim Annisa Luluk Handayani Muhammad Yosi Ashadi Rais Nurwahyudin Sharfina Febbi Handayani Sofia Ariyani Suharso, Wiwik Sutikno Sutikno Wahyu Tri Bakti