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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Teknologi Terpadu JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Informasi dan Teknologi Prosiding National Conference for Community Service Project G-Tech : Jurnal Teknologi Terapan Telcomatics Journal of Information System and Technology (JOINT) Conference on Business, Social Sciences and Technology (CoNeScINTech) Social Engagement: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Firmansyah, Muhamad Dody
Univeristas Internasional Batam
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Other

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Acquiring Knowledge from Data Analytics and Performance-Boosting on Multimedia Content Jed Wan; Hendi Sama; Muhamad Dody Firmansyah
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

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Abstract

In gaining meaningful and actionable insights from complex and diverse multimedia content, many studies have applied data analytics approaches—particularly data mining and machine learning—to uncover patterns, relationships, and hidden knowledge. This systematic literature review synthesizes 26 studies conducted over the past decade on acquiring knowledge from multimedia content using data analytics and performance-boosting techniques. Across domains such as social media, education, healthcare, e-commerce, and public safety, most works integrate text–image or audio–video pairs and increasingly adopt attention-based architectures and transformer models with early fusion strategies. To ensure comparability, each study’s evidence is recorded by considering the reported performance improvement over the authors’ baseline using the same dataset and evaluation metric. The most frequently used metrics include Accuracy, the F1-score (a harmonic mean of Precision and Recall), Precision, Recall, and the Area Under the Receiver Operating Characteristic Curve (AUC), which provides a threshold-independent measure of classification quality. The most common challenges identified include modality integration and alignment, data noise and quality, limitations of datasets and benchmarks, and domain shift, with fewer studies reporting class imbalance, computational cost, and interpretability or privacy issues. At the same time, promising opportunities emerge in the development of standardized multimodal benchmarks, efficient transformer-based and hybrid fusion pipelines, integration of external knowledge, domain-robust learning, and privacy-preserving explainable multimodal artificial intelligence. Overall, this review contributes a consolidated map of modalities, methods, and metrics, a performance-gain versus baseline table for quick comparability, a quantified challenge landscape, and a practical roadmap for guiding future research in multimodal sentiment analysis and related fields.
Co-Authors Anderson, Atnan Ari Bong, Valenteeno Bryan Aditya Chanda Vedalla Putra Chandra, Jefriyanto Christian Christian Christopher Khomali Dhafa Firgana Ellwan Edy Wei Fredian Simanjuntak Hafizh akmal Herman Herman Jecelyn Jed Wan Jevin Leon Kelven Liong Kelven LIong Kelvin Kelvin Kelvin Melati, Dini Sari Mungkap Mangapul Siahaan Nabila Arinov Mey Nagano Nagano Nathania Jocelyn Rince Monica Hutagalung Ryandy Sonata Saaya, Zurina Sama, Hendi Sama, Hendi Shermay Syaeful Anas Aklani, Syaeful Tito Adrian Pribadi Valenteeno Bong WAN, Jed Wan Wirren Chang Zhilla Syauqina Hafsa Zulkarnain Zulkarnain