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All Journal Journal of Applied Geospatial Information Jurnal Mantik Penusa JOURNAL OF SCIENCE AND SOCIAL RESEARCH CSRID (Computer Science Research and Its Development Journal) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer International Journal of Economics Development Research (IJEDR) Journal of Intelligent Decision Support System (IDSS) Jurnal Teknik Informatika (JUTIF) International Journal of Basic and Applied Science Jurnal Mandiri IT Jurnal Minfo Polgan (JMP) Jurnal Pengabdian Kepada Masyarakat Transformasi Masyarakat : Jurnal Inovasi Sosial dan Pengabdian Venus: Jurnal Publikasi Rumpun Ilmu Teknik Indonesia Bergerak: Jurnal Hasil Kegiatan Pengabdian Masyarakat Jurnal Bisnis Digital Global Science: Journal of Information Technology and Computer Science Jurnal Teknik Informatika dan Teknologi Informasi
Mutiara S. Simanjuntak
Politeknik Negeri Lhokseumawe
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Analisis Ketepatan Model CNN dalam Deteksi Asap Berbasis Citra Simangunsong, Juanto; Simanjuntak, Mutiara S.; Simanjuntak, Nurmala Dewi
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.
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.
Co-Authors Aji Priyambodo Allwine Arnold Pakpahan Baehaqi Chairil Umri Elshad Yusifov Ginting, Syahril Hongjie Dai Iskandar Iskandar Joel Panjaitan Juanto Simangunsong Juanto Simangunsong Junifa, Dira LUBIS, IRNA TRIANNUR Manalu, Pius Deski Manullang, Evi Yanti Nissa, Fachrun Nurafni Damanik Pieter Leuvanggi Hutagalung Pius Deski Manalu Pulungan, Wahyu Aji Regina Sirait - Rico Wijaya Riduan, Suwardy Rika Rosnelly Sairun, Aryani Selvi Aristantya Simangunsong, Juanto Simanjuntak, Andrean Simanjuntak, Nurmala Dewi Siti Aisyah Syofyan Anwar Syahputra Syofyan Anwar Syahputra Teddy Surya Gunawan Wanayumini