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Freddy Kurniawan
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Department of Electrical Engineering Institut Teknologi Dirgantara Adisutjipto, Jl. Janti, Blok R, Lanud Adisutjipto, Yogyakarta
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Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC)
Published by Institut Teknologi Dirgantara Adisutjipto
ISSN : 26852381     EISSN : 27152626     DOI : 10.28989/avitec
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Engineering
This journal is the scientific publications journal published by Department of Electrical Engineering, Sekolah Tinggi Teknologi Adisutjipto. It aims to promote and disseminate the research finding in the development of management theories and practices. It will provide a platform for academicians, researchers, and practitioners to share their experience and solution to problems in different areas of journal scopes. Every submitted paper will be blind-reviewed by peer-reviewers. Reviewing process will consider novelty, objectivity, method, scientific impact, conclusion, and references.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 2 Documents
 1 
Search results for , issue "Vol 8, No 2 (2026): August" : 2 Documents clear
Multimodal Machine Learning for Maize Disease Detection: A Systematic Review of Architectures and Deployment Challenges Tonui, Mercy Chepkoech; Kamau, John Wachira; Ongus, Raymond Wafula
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 2 (2026): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i2.3942

Abstract

Maize diseases continue to threaten agricultural productivity and food security, particularly in developing regions where early diagnosis remains constrained by limited expert access. While deep learning has enabled automated disease detection systems, most existing approaches rely on unimodal image datasets and cloud-dependent architectures, limiting robustness and deployment feasibility in resource-constrained environments. This study presents a structured systematic review of 38 peer-reviewed studies published between 2020 and 2025, focusing on multimodal machine learning approaches integrating visual and environmental data for maize disease detection. Quantitative synthesis reveals that 58% of reviewed studies employ image-only deep learning models, 26% implement multimodal frameworks, and only 29% conduct validation under real or semi-real field conditions. Furthermore, 32% explicitly address deployment considerations, including edge computing and mobile inference. The findings demonstrate that multimodal architectures improve robustness and contextual modeling compared to unimodal systems by integrating phenotypic and environmental drivers of disease expression. However, increased computational complexity, synchronization challenges, and limited edge optimization remain barriers to scalable deployment. This review advances scientific knowledge by providing a computing-centered synthesis of multimodal architectures, fusion strategies, deployment constraints, and explainability gaps, identifying key research priorities in edge efficiency, real-world validation, and interpretable intelligent systems.
Elevator Energy Consumption and Upward Travel Load Patterns in A University Lecture Building Wulandari, Erika; Rosyady, Phisca Aditya
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 2 (2026): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i2.3949

Abstract

This study analyzes passenger elevator operation patterns and their contribution to building electricity consumption in a university lecture building. Previous research has mainly focused on system simulations or control optimization, resulting in limited empirical studies that integrate large-scale directional passenger movement data with aggregated building-level electricity consumption, especially in academic settings. To address this gap, the study examines elevator usage patterns based on 31,265 observed trips and links directional travel with building-level electricity consumption. Data were collected over a two-week period (13–24 October 2025) through direct observation and MDP-based energy measurements, then analyzed using Pearson correlation and linear regression. Results show that 44.6% of total traffic occurred in the morning, with 83.0% concentrated during peak periods. Upward trips accounted for 52.7% of movements, indicating directional asymmetry associated with increased traction motor load during peak hours. Pearson correlation analysis revealed a significant positive relationship between elevator usage intensity and daily electricity consumption (r = 0.813, p = 0.004, 95% CI [0.35–0.96]). Linear regression showed that 66.1% of variation in daily energy consumption could be explained by elevator usage intensity. This study provides a context-specific empirical analysis by integrating directional elevator travel data with aggregated building-level electricity consumption in a university lecture building, based on real-world observations. These findings demonstrate that dominant upward travel during academic transition periods is measurably associated with overall building energy consumption dynamics.

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