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Enhancing Cross-Organizational Healthcare Analytics Through Blockchain-Enabled Federated Learning Mutiara S. Simanjuntak; Aji Priyambodo; Elshad Yusifov
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 2 (2025): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i2.176

Abstract

This study explores the integration of blockchain technology with federated learning (FL) to enhance cross-organizational healthcare analytics while ensuring privacy and data security. Federated learning allows multiple institutions to collaboratively train machine learning models without sharing sensitive patient data. Instead, local data is used to train models, and only model parameters are exchanged. However, privacy concerns and data sharing inefficiencies have hindered broader healthcare collaboration. Blockchain, a decentralized ledger technology, addresses these concerns by ensuring data integrity and transparency, providing an immutable and tamper-proof record of all transactions. This study investigates how the combination of blockchain and federated learning can overcome these challenges, facilitating secure and efficient data sharing between healthcare institutions. The study uses synthetic multi-institution healthcare datasets to simulate real-world collaboration scenarios. The blockchain-enabled federated learning system ensures that no raw patient data is shared, significantly reducing the risk of privacy breaches while still allowing healthcare institutions to collaborate on predictive model development. The results show that while there is a slight decrease in model accuracy compared to centralized methods, the trade-off is outweighed by the privacy and security benefits. Blockchain’s integration ensures that model updates are transparent, enhancing trust between institutions and reducing concerns about data integrity. Moreover, the use of blockchain’s smart contracts automates and enforces compliance, further streamlining collaboration. This research contributes to the field by demonstrating how blockchain-integrated federated learning can create a secure, scalable, and privacy-preserving framework for collaborative healthcare analytics. The findings underscore the potential for this approach to enhance healthcare outcomes and improve decision-making across institutions while ensuring patient data protection.
Analisis Ketepatan Model CNN dalam Deteksi Asap Berbasis Citra Juanto Simangunsong; Mutiara S. Simanjuntak; Nurmala Dewi Simanjuntak
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15908

Abstract

Deteksi asap merupakan tahap kritis dalam sistem peringatan dini kebakaran, karena keberadaan asap biasanya muncul lebih dahulu sebelum api terlihat. Penelitian ini bertujuan untuk menganalisis ketepatan model Convolutional Neural Network (CNN) dalam mengklasifikasikan citra asap dan non-asap menggunakan Smoke Detection Dataset. Proses penelitian meliputi praproses citra, pelatihan model CNN, serta evaluasi performa menggunakan metrik akurasi, precision, recall, F1-score, dan Area Under Curve (AUC). Hasil penelitian menunjukkan bahwa model CNN mencapai akurasi sebesar 0.994, precision 0.69, recall 0.78, dan F1-score 0.73, sementara nilai AUC sebesar 0.992 menegaskan kemampuan diskriminatif model yang sangat tinggi. Confusion matrix mengungkapkan bahwa model efektif dalam mengidentifikasi citra non-asap maupun asap, meski masih terdapat kesalahan pada citra dengan intensitas asap rendah dan kondisi visual menyerupai asap. Secara keseluruhan, CNN terbukti menjadi metode yang andal dan efisien untuk deteksi asap berbasis citra, serta berpotensi dikembangkan lebih lanjut untuk aplikasi sistem deteksi kebakaran berbasis visi komputer.
Content Marketing Strategy in Increasing Student MSME Brand Awareness Pius Deski Manalu; Dedi Irawan; Mutiara S. Simanjuntak; Dody Hidayat
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.209

Abstract

This study is motivated by the low level of brand awareness among student-owned micro, small, and medium enterprises (MSMEs), which is mainly caused by the suboptimal implementation of digital marketing strategies, particularly content marketing. The main problem addressed in this research is how content marketing strategies can improve brand awareness among student MSMEs. This study aims to analyze the effect of content marketing on brand awareness and to identify effective content strategies that support such improvement. The research employs a quantitative approach using a survey method involving 100 student MSME actors in the Padang Bulan area. Data were collected through questionnaires using a Likert scale and analyzed using simple linear regression and t-test. The results indicate that content marketing has a positive and significant effect on brand awareness, with a regression coefficient of 0.68 and a significance value of 0.000 (less than 0.05). Additionally, about 78% of respondents actively use social media for marketing purposes, while only 65% ​​have structured content strategies. This finding suggests that improving content quality, creativity, and consistency can significantly enhance brand awareness. This study is expected to provide practical insights for student MSMEs in optimizing content-based digital marketing strategies.