Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan Green Engineering: Journal of Engineering and Applied Science
Wahyu Caesarendra
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Nursing Public Health

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Implementation of Machine Learning and Deep Learning Models Based on Structural MRI for Identification Autism Spectrum Disorder Dimas Chaerul Ekty Saputra; Yusuf Maulana; Thinzar Aung Win; Raksmey Phann; Wahyu Caesarendra
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26094

Abstract

Autism spectrum disorder (ASD) is a developmental disability resulting from neurological disparities. People with ASD frequently struggle with communication and social interaction, as well as limited or repetitive interests or behaviors. People with ASD may also have unique learning, movement, and attention styles. ASD sufferers can be interpreted as 1 in every 100 individuals in the globe having ASD. Abilities and requirements of autistic individuals vary and may change over time. Some autistic individuals are able to live independently, while others have severe disabilities and require lifelong care and support. Autism frequently interferes with educational and employment opportunities. Additionally, the demands placed on families providing care and assistance can be substantial. Important determinants of the quality of life for persons with autism are the attitudes of the community and the level of support provided by local and national authorities. Autism is frequently not diagnosed until adolescence, despite the fact that autistic traits are detectable in early infancy. This study will discuss the identification of Autism Spectrum Disorders using Magnetic Resonance Imaging (MRI). MRI images of ASD patients and MRI images of patients without ASD were compared. By employing multiple machine learning and deep learning techniques, such as random forests, support vector machines, and convolutional neural networks, the random forest method achieves the utmost accuracy with 100% using confusion matrix. Therefore, this technique is able to optimally identify ASD through MRI.
Multi-Objective Optimization of Green Building Retrofit Strategies Considering Thermal Comfort, Energy Efficiency, and Indoor Air Quality in Tropical Climate Zones Efvy Zamidra Zam; Wahyu Caesarendra; Nopriadi Nopriadi
Green Engineering: International Journal of Engineering and Applied Science Vol. 1 No. 4 (2024): October: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v1i4.255

Abstract

This study investigates optimal retrofit strategies for buildings in tropical climates, focusing on energy efficiency, thermal comfort, and indoor air quality (IAQ). Given the unique challenges of high temperatures, humidity, and energy demands in tropical regions, traditional retrofitting methods often fall short of achieving a balance between these critical factors. By employing a multi-objective optimization approach, this research identifies the most effective combination of retrofit solutions, including insulation, natural ventilation, and high-performance window treatments. The results show that the proposed retrofit strategy significantly reduces cooling energy consumption, while maintaining or improving occupant comfort and IAQ. Insulation, particularly external insulation, proved to be the most effective in reducing heat transfer, while natural ventilation strategies and advanced materials further contributed to improving thermal regulation. The study demonstrates that integrating passive and active retrofit measures, tailored specifically to tropical climates, leads to optimal building performance. The multi-objective optimization algorithm (NSGA-II) allowed for the generation of Pareto-optimal solutions, offering a set of trade-offs between energy efficiency, thermal comfort, and IAQ. These findings are particularly relevant for policymakers and building professionals seeking sustainable retrofit solutions in tropical regions. The study also highlights the importance of integrating energy efficiency and IAQ considerations in retrofit strategies to avoid compromising occupant health. Further research is recommended to explore the integration of advanced materials, such as phase change materials (PCMs), and to enhance IAQ management in retrofitted buildings, ensuring long-term sustainability and occupant well-being in tropical environments.
A Scoping Review of Machine Learning Applications in Nursing Practice: Clinical Decision Support, Risk Prediction, and Workflow Optimization Anton Suhendro; Wahyu Caesarendra; Purwono, Purwono
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2222

Abstract

Machine learning (ML) is rapidly transforming nursing practice by enabling advancements in clinical decision support, risk prediction, and workflow optimization. This scoping review synthesizes evidence from empirical studies, reviews, and implementation reports published between 2018 and 2025, identified through Scopus and ScienceDirect. The findings indicate that supervised learning algorithms, deep learning, and natural language processing are widely utilized for risk assessment, early detection of patient deterioration, and enhancement of administrative efficiency. Natural language processing (NLP) also supports automation of nursing documentation and improved data quality. Despite favorable performance metrics, including AUROC values above 0.85 in many applications, most studies are limited by single-institution data, insufficient external validation, and heterogeneous reporting standards. Major barriers include ethical and legal concerns, data quality issues, algorithmic bias, infrastructural limitations, and limited nurse involvement in model development. Enhancing AI literacy and fostering nurse engagement in system design are highlighted as critical for successful clinical integration. Future research priorities include multicenter validation, development of explainable AI, adoption of standardized reporting guidelines, and interdisciplinary collaboration to address ethical, technical, and regulatory challenges. Overall, this scoping review demonstrates that machine learning offers substantial potential to improve patient outcomes and nursing operations, but responsible adoption requires rigorous validation, transparent governance, and active participation of nursing professionals throughout the technology lifecycle
Co-Authors Anton Suhendro Dimas Chaerul Ekty Saputra Efvy Zamidra Zam Nopriadi Nopriadi Purwono Purwono, Purwono Raksmey Phann Thinzar Aung Win Yusuf Maulana