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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) JIEET (Journal of Information Engineering and Educational Technology) Journal of Electrical Engineering and Computer (JEECOM)
Deviani, Rini
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Physics

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Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption Maulyanda, Maulyanda; Deviani, Rini; Afdhaluzzikri, Afdhaluzzikri
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10875

Abstract

Protecting the privacy of medical data while enabling sophisticated data analysis is a critical challenge in modern healthcare. Fully Homomorphic Encryption (FHE) emerges as a powerful solution, enabling computations to be performed directly on encrypted data without exposing sensitive information. This study delves into the use of FHE for neural network inference in medical applications, investigating its role in safeguarding patient confidentiality while ensuring computational accuracy and efficiency. Experimental findings confirm the practicality of using FHE for medical data classification, demonstrating that data security can be preserved without significant loss of performance. Furthermore, the research explores the balance between computational overhead and model precision, shedding light on the complexities of deploying FHE in real-world healthcare AI systems. By emphasizing the significance of privacy-preserving machine learning, this work contributes to the development of secure, ethical, and effective AI-driven medical solutions.
Rancang Bangun Aplikasi Bank Sampah Universitas Syiah Kuala Berbasis Web Deviani, Rini; Aflah, Tsani; Misbullah, Alim
Journal of Electrical Engineering and Computer (JEECOM) Vol 6, No 1 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v6i1.8245

Abstract

Bank Sampah Universitas Syiah Kuala merupakan solusi dari Universitas Syiah Kuala untuk mengatasi dampak negatif dari peningkatan produksi sampah. Kesadaran masyarakat terhadap pengelolaan sampah yang terus berkembang telah mendorong peningkatan jumlah nasabah Bank Sampah USK. Pertumbuhan nasabah ini memunculkan masalah diantaranya sistem administrasi dan transaksi yang belum terstruktur, serta tidak adanya platform untuk menyebarluaskan informasi dan berita mengenai Bank Sampah USK kepada masyarakat. Adapun cara untuk mengatasi tantangan ini adalah dengan membangun aplikasi Bank Sampah USK berbasis web. Aplikasi ini dikembangkan menggunakan model waterfall sehingga tahapannya dikerjakan secara berurutan,mulai dari tahap identifikasi masalah yang dialami pengguna, tahap pengerjaan kode dengan bahasa pemrograman PHP hingga tahap dilakukannya pengujian untuk memastikan sistem berjalan dengan baik serta memberikan pengalaman pengguna yang optimal. Fitur-fitur aplikasi yang dihasilkan mencakup kemampuan pengunjung web untuk melihat informasi/berita oleh Bank Sampah USK. Administrator dapat mengelola informasi/berita, nasabah, setoran, jenis sampah, dan konfirmasi penarikan dana. Nasabah dapat mengelola profil, melakukan penarikan saldo, serta melihat riwayat setoran dan penarikan. Hasil analisis pengujian fungsionalitas menunjukkan bahwa aplikasi ini berhasil menjalankan semua skenario black box testing dan mendapatkan penilaian usability "Good" oleh 30 responden, dengan grade scale ‘B’ dan acceptability range berada pada kategori ‘Acceptable’.
The Application of Fully Homomorphic Encryption on XGBoost Based Multiclass Classification Deviani, Rini
JIEET (Journal of Information Engineering and Educational Technology) Vol. 7 No. 1 (2023)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v7n1.p49-58

Abstract

Fully Homomorphic Encryption (FHE) is a ground breaking cryptographic technique that allows computations to be performed directly on encrypted data, preserving privacy and security. This paper explores the application of Fully Homomorphic Encryption on Extreme Gradient Boosting (XGBoost) multiclass classification, demonstrating its potential to enable secure and privacy-preserving machine learning. The paper presents a framework for training and evaluating XGBoost models using encrypted data, leveraging FHE operations for encrypted feature engineering, model training, and inference. The experimental results showcase the feasibility of applying Fully Homomorphic Encryption to XGBoost-based multiclass classification tasks while maintaining data confidentiality. The findings highlight the trade-off between computation complexity and model accuracy in FHE-based approaches and provide insights into the challenges and future directions of utilizing Fully Homomorphic Encryption in practical machine learning scenarios. The study underscores the significance of privacy-preserving machine learning techniques and paves the way for secure data analysis in sensitive domains where data privacy is of utmost importance.
Implementation of Augmented Reality (AR) Animation Media to Enhance Learning Outcomes and Interest in the Excretory System Topic Oktavianda, Nanda; Rahmatan, Hafnati; Huda, Ismul; Pada, Andi Ulfa Tenri; Safrida; Deviani, Rini
Jurnal Penelitian Pendidikan IPA Vol 10 No 11 (2024): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i11.8816

Abstract

The excretory system material requires visualization to enhance understanding. To help students grasp the concepts of the excretory system more effectively, the various organs of the excretory system must be illustrated in the learning material. Therefore, learning animations are needed that can display images created with augmented reality media, appearing almost lifelike in 3D form. This study aims to analyze the application of augmented reality animation media in improving learning outcomes and the difference in students' interest before and after learning about the excretory system with augmented reality animations. The method used is a quasi-experiment with a pretest-posttest control group design. The sample consists of 54 students from SMA Negeri 1 Ingin Jaya Aceh Besar, selected using random sampling techniques. Data were collected through multiple-choice tests and a questionnaire on students' interest in the excretory system material. The data were analyzed using a t-test, showing a significant difference in learning outcomes in the experimental class with a p-value < 0.05 (0.022 < 0.05), and a significant difference in students' interest before and after the learning process in the experimental class with a t-test result of p < 0.05 (0.037 < 0.05). It can be concluded that the application of augmented reality media can enhance learning outcomes and affect students' interest in the excretory system material.
Co-Authors Afdhaluzzikri, Afdhaluzzikri Aflah, Tsani Andi Ulfa Tenri Pada Hafnati Rahmatan Ismul Huda Maulyanda, Maulyanda Misbullah, Alim Oktavianda, Nanda Safrida