Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Journal Collabits
Hidayah, Rafif Syari
Unknown Affiliation
Computer Science & IT Engineering

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

Found 2 Documents
Search

 1 
CONSTRUCTION AND MANAGEMENT OF A COSMOS-BASED OPERATING SYSTEM USING VISUAL STUDIO DEVELOPMENT AND VMWARE VIRTUALIZATION TECHNOLOGY Yusuf, Mohamad; Fahrezi, Zidane; Hidayah, Rafif Syari; Istanto, Yudha Andika; Saputra, Gilas Adi
Journal Collabits Vol 1, No 1 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i1.25559

Abstract

Operating systems play an important role in bridging hardware and software on various computing devices. This research focuses on building an operating system based on Cosmos, an open source project that allows the creation of operating system kernels quickly and efficiently. In the process, we leverage Visual Studio development tools to develop and maintain the kernel, while VMware virtualization technology is used to test and manage development. This research contributes to further understanding of the development of Cosmos-based operating systems with optimal use of Visual Studio development tools and VMware virtualization technology
Neural Network Classification to Determine the Likelihood of Diabetes Using Python Programming Language Hidayah, Rafif Syari; Istanto, Yudha Andika
Journal Collabits Vol 1, No 3 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i3.27300

Abstract

Diabetes is a global health problem that affects millions of people worldwide. Predicting a person's risk of developing diabetes can be an important first step in disease prevention and management. In this study, we propose the development of a predictive model for diabetes using Neural Network (NN) technique with implementation using Python. The data used in this study consists of clinical information that includes factors such as pregnancy, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, and age. The model development process involves data pre-processing, selection of relevant features, model training, and performance evaluation using appropriate metrics. The experimental results show that the developed NN model has a good ability in predicting diabetes risk. The main contribution of this research is the use of NN techniques and Python coding in the development of predictive models for diabetes, which can provide useful guidance for medical practitioners in supporting disease prevention and management efforts. Future studies can extend this research by considering additional factors and improving the accuracy of the model by using more complex approaches. Keywords: Diabetes, Prediction, Neural Network, Python coding, Predictive model, Model development, Data pre-processing, Performance evaluation, Disease prevention, Disease management
Co-Authors Fahrezi, Zidane Istanto, Yudha Andika Mohamad Yusuf Saputra, Gilas Adi