Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Journal of Internet and Software Engineering
Octava, Muhammad Qois Huzyan
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Application of the outlier detection method for web-based blood glucose level monitoring system Nurhaliza, Rachma Aurya; Octava, Muhammad Qois Huzyan; Hilmy, Farhan Mufti; Farooq, Umar; Alfian, Ganjar
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7717

Abstract

Recent advancements in biosensors have empowered individuals with diabetes to autonomously monitor their blood glucose levels through continuous glucose monitoring (CGM) sensors. Nevertheless, the data collected from these sensors may occasionally include outliers due to the inherent imperfections of the sensor devices. Consequently, the identification of these outliers is critical to determine whether blood glucose levels deviate significantly from the norm, necessitating further action. This study employs an outlier detection approach based on the 3-sigma method and the interquartile range (IQR), along with the application of the Winsorizing technique to correct the identified outliers. Additionally, a web-based system for visualizing blood glucose levels is developed, utilizing both outlier detection methods. In order to assess the system's performance, two types of testing are conducted: black box testing and load testing. The results of black box testing indicate that all test scenarios operate as anticipated. As for the load testing response times, it is observed that the 3-sigma visualization page loads an average of 606.75 milliseconds faster compared to the IQR visualization page. This study's outcomes are expected to enhance data quality, enhance the precision of analyses, and facilitate more informed decision-making by identifying and addressing extreme data points.
Perancangan Federated Learning Berbasis Homomorphic Encryption untuk Perangkat Internet of Things Saputra, Yuris Mulya; Alfian, Ganjar; Octava, Muhammad Qois Huzyan
Journal of Internet and Software Engineering Vol 4 No 1 (2023): Journal of Internet and Software Engineering
Publisher : Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jise.v4i1.6378

Abstract

Semakin berkembangnya pasar big data yang digunakan oleh pengguna khususnya Internet of Things (IoT) berbasis kecerdasan buatan telah menarik banyak pihak baik dari industri maupun akademisi. Melalui penggunaan data lokal dari berbagai perangkat IoT, pemberi layanan aplikasi dapat menghasilkan informasi berguna melalui pendekatan machine learning (ML) seperti centralized learning dengan menggunakan cloud server dan local learning pada perangkat IoT langsung. Namun, dengan adanya risiko bocornya privasi pengguna ketika mengirim data lokal ke cloud server dan sumber daya komputasi yang terbatas pada IoT, penggunaan federated learning (FL) dapat menjadi solusi efisien. Pendekatan FL merupakan sebuah pendekatan ML kolaboratif di mana setiap perangkat IoT dapat melakukan proses training secara independen dan kemudian hanya mengirimkan model local kepada cloud server tanpa melakukan data sharing. Secara khusus, penggunaan FL untuk layanan aplikasi pada perangkat IoT tidak hanya memperbaiki kinerja untuk proses training, namun juga dapat melindungi privasi data bagi penggunanya. Penelitian ini berfokus pada perancangan sistem FL dengan privacy-awareness yang dapat digunakan oleh para pengguna perangkat IoT. Dalam hal ini, teknik enkripsi yang berbasis homomorphic encryption untuk mengenkripsi data dari perangkat IoT ketika proses training dari FL dapat diimplementasikan sebagai bentuk perlindungan privasi pengguna IoT dari malicious attackers. Dari penelitian ini, dapat dianalisis perbandingan tingkat akurasi model dari berbagai pendekatan baik tanpa dan dengan teknik enkripsi tersebut.
Co-Authors Alfian, Ganjar Farooq, Umar Hilmy, Farhan Mufti Nurhaliza, Rachma Aurya Saputra, Yuris Mulya