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All Journal BERKALA SAINSTEK Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA Jiko (Jurnal Informatika dan komputer) Jurnal Ilmu-Ilmu Kesehatan Jurnal ABDINUS : Jurnal Pengabdian Nusantara Community Empowerment EXPLORER Jurnal Ilmu-Ilmu Kesehatan Sekolah Tinggi Ilmu Kesehatan Garuda Putih Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Journal of Hospital Administration and Management (JHAM) Jurnal Manajemen Sistem Informasi Nuansa Informatika ABDIMAS CSRID ARUMAS International Journal of Computer Technology and Science Systematic Literature Review Journal JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of New Trends in Sciences KREATIF: Jurnal Pengabdian Masyarakat Nusantara Global Science: Journal of Information Technology and Computer Science
Rudolf Sinaga
Universitas Dinamika Bangsa
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : International Journal of Computer Technology and Science

 1 
Contextual Data Fusion and Explainable Analytics for Supporting Strategic Decision Making in Smart Information Systems Environments Priyo Wibowo; Rudolf Sinaga
International Journal of Computer Technology and Science Vol. 1 No. 1 (2024): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i1.357

Abstract

The increasing complexity and heterogeneity of data in Smart Information Systems pose significant challenges for effective decision-making. While data fusion techniques have been widely adopted to integrate multiple data sources, traditional fusion approaches often fail to consider contextual information, resulting in limited interpretability and reduced decision relevance. This study proposes a contextual data fusion approach that integrates heterogeneous data sources with contextual attributes, including temporal, spatial, and operational context, to enhance decision accuracy and robustness. The research employs a computational and experimental methodology involving data preprocessing, context encoding, multi-level data fusion, and performance evaluation. Experimental results demonstrate that the proposed approach outperforms single-source analysis and non-contextual data fusion in terms of accuracy, precision, recall, and F1-score, with only a marginal increase in computational cost. The findings confirm that incorporating context into the data fusion process significantly improves the quality and reliability of analytical outcomes. This study contributes to the development of intelligent and data-driven systems by highlighting the critical role of contextual awareness in supporting transparent and effective decision-making in Smart Information Systems.
Co-Authors Adha Taufik, Anvika Afriany, Renny Errissya Rasywir Fatima, Soomal Frangk Frangky Frangky Frangky Frangky Frangky Frangky Frangky Frangky Taan Frangky, Frangky Ika Widyastuti Irwandi Irwandi M. Yogi Riyantama Isjoni N, Renny Afriany Nurhadi Nurhadi Priyo Wibowo Raihansyah, M. Renny Afriany Renny Afriany Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Uswatun Kasanah Yovi Pratama Zhenya, Zhenya Zeldha Elyzvania