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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 158 Documents
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Adaptive Kernel Probability Model (AKPM) for Interpretable and Reliable Diabetes Prediction using Clinical Diagnostic Data Marselina Endah Hiswati; Izattul Azijah; Yeyen Subandi; Mohammad Diqi
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3689

Abstract

Diabetes mellitus poses a growing global health concern, particularly in low- and middle-income countries where early detection remains limited, demanding classification models that balance accuracy, interpretability, and adaptability to heterogeneous clinical data. This study proposes and evaluates the Adaptive Kernel Probability Model (AKPM), a novel nonparametric probabilistic classifier designed to enhance diabetes prediction by performing localized kernel density estimation with adaptive bandwidth selection via k-nearest neighbors. Implemented and tested on the Pima Indians Diabetes Dataset, AKPM outperformed conventional classifiers—Naïve Bayes and Gaussian Mixture Models (GMM)—across all evaluation metrics, achieving 87.5% accuracy, 83.3% precision, 76.9% recall, and an F1-score of 80.0% for the diabetic class, alongside 89.3% precision and 92.6% recall for the normal class. These results surpassed GMM (83.0% accuracy, 71.6% F1-score) and Naïve Bayes (80.0% accuracy, 66.6% F1-score), confirming AKPM’s superior capability to detect diabetic cases while minimizing false negatives. Offering transparent posterior inference and a modular design, AKPM emerges as a reliable and interpretable solution for clinical decision support systems and real-world healthcare applications.
Tilt Angle and Inverter Input Voltage Optimization for Rooftop Photovoltaic Systems using Whale Optimization Mulyana Mulyana; Muhamad Haddin
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3792

Abstract

Optimizing the performance of rooftop photovoltaic (RTPV) systems is crucial maximize energy production, especially in limited urban spaces. The main problem with the 122,040 Wp RTPV system is a significant performance gap, where the peak output power only reaches 95.93 kW, suggesting that the operational configuration may not be at the optimal point. The novelty of this research lies in the simultaneous optimization of two critical parameters: the geometric tilt angle (β) and the electrical inverter input voltage (VDC), a dual-parameter approach that contrasts with prior studies focusing on single-parameter optimization. This study aims to determine the optimal power output by employing the Whale Optimization Algorithm (WOA). The WOA method was selected for its superior ability to navigate complex search spaces by mimicking the bubble-net hunting strategy of humpback whales through a spiral model and a shrinking encircling mechanism to identify the global optimum. Simulation results show that convergence is achieved at the 75th iteration. The optimization results demonstrate a significant performance improvement, increasing the output power from 95.93 kW to 105.01 kW, which represents a 9.46% efficiency gain. This simultaneous optimization, resulting in a panel β of 26.26° and VDC of 629.66 V, proves to be a robust technical contribution for shifting the operating point toward the global maximum power point (GMPP) in industrial-scale RTPV systems.
Voltage Drop and Power Loss Mitigation on SGN-14 via SGN-15 Feeder Design in Distribution System ULP Magelang Haqrodji Prabu Yasya; Deria Pravitasari; Agung Trihasto; Andriyatna Agung Kurniawan
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3686

Abstract

Feeder SGN-14 of PT PLN (Persero) ULP Magelang operates under overload conditions, significantly degrading voltage quality and increasing technical losses. PLN (Perusahaan Listrik Negara) is Indonesia’s State Electricity Company, while ULP (Unit Layanan Pelanggan) refers to a customer service unit. This study designs Feeder SGN-15 as a 20 kV load-splitting feeder supplied from Sanggrahan Substation and terminating near KH. Maksum Street (Tempuran). The feeder is 20.7 km long and routed close to the load centre to reduce line losses. Network performance is assessed using ETAP load-flow simulations and independent GNU Octave calculations of voltage profile, current, and power/energy losses, referenced to SPLN T6.001:2013 with a 10% voltage-drop limit. The proposed feeder uses 8,152 m of insulated MVTIC and 12,584 m of AAAC conductors, supported by 238 concrete poles, together with required switching devices, line accessories, and four CSP transformers. After reconfiguration, the maximum voltage drops on SGN-14 decreases from 12.82% to 6.5%, while SGN-15 operates at about 4.95%, ensuring all buses comply with SPLN T6.001:2013. Technical losses on SGN-14 fall from 388.711 to 112.337 (W/kWh), and SGN-15 contributes 81.130 (W/kWh), giving total post-reconfiguration losses of 195.467 (W/kWh). The reduction in energy-loss cost yields an estimated saving of Rp228.82 million per month, lowering losses from Rp460.32 million/month to Rp231.44 million/month. Unlike studies that optimize only switch states or voltage-regulator placement, this work shows that adding a new 20 kV feeder can jointly improve voltages, reduce losses, and deliver tangible benefits for the distribution utility.
Multimodal Machine Learning for Maize Disease Detection: A Systematic Review of Architectures and Deployment Challenges Mercy Chepkoech Tonui; John Wachira Kamau; Raymond Wafula Ongus
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 Erika Wulandari; Phisca Aditya Rosyady
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.
Log Anomaly Detection with Conformal Alert Control and Evidence-Grounded Incident Ticket Generation Qi Xin
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.v8i2.3974

Abstract

Operational logs are a primary source of evidence for reliability engineering, incident response, and security operations, but log anomaly detection is useful only when scores can be translated into controlled alerts and auditable incident evidence. This paper presents a reproducible end-to-end AIOps pipeline that normalizes raw logs into templates, aggregates them into sliding windows, scores anomalies with representative detectors, calibrates alerts with conformal prediction, and generates evidence-grounded incident tickets. The revised evaluation includes BGL_2k and two additional public sequence benchmarks, HDFS and OpenStack, and adds representative LogAnomaly-style and LogBERT-lite baselines to the original TF-IDF+LR, Isolation Forest, DeepLog-style LSTM, and Transformer comparisons. On BGL_2k, Isolation Forest provides the best ranking performance among the original four detectors (test PR-AUC = 0.750), while the additional HDFS experiment shows that the masked-context LogBERT-lite baseline obtains the strongest sequence-level result (PR-AUC = 0.947, F1 = 0.905). OpenStack remains difficult because the available normal training sample is very small, producing low F1 across all added baselines. We also report inference latency, throughput, memory footprint, conformal alpha sensitivity, window-size sensitivity, model-strategy ablations, and structured false-positive/false-negative patterns. The results should be interpreted as reproducible operational validation of the detection-calibration-ticket workflow rather than a claim of state-of-the-art detector accuracy. The pipeline demonstrates how calibrated scores and template-level evidence can support practical alert control and ITSM-ready ticket generation.
A Design-Science-Based Technical-Security Framework for IoMT: Empirical Validation in Resource-Constrained Hospitals Arinaitwe Winfred; Kareyo Margaret; Shefiu Olusegun Ganiyu
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.v8i2.3969

Abstract

The rapid diffusion of Internet of Medical Things (IoMT) technologies has improved healthcare delivery through real-time monitoring and data exchange, yet secure information sharing remains challenging in resource-constrained healthcare environments. This study develops and empirically validates a technical-security systems engineering framework integrating device, network, application, and governance layers for information-sharing-enabled IoMT systems in low-resource hospitals. The study adopted a Design Science Research (DSR) approach involving two phases: (1) qualitative derivation of security requirements from literature and standards, and (2) quantitative empirical validation using multiple regression analysis based on hospital survey data (n = 148). The study contributes a technical-security systems engineering framework that integrates device security, network protection, interoperability mechanisms, governance structures, and contextual adaptation requirements into a unified architectural model. Empirical findings indicate that secure IoMT implementation depends on the coordinated interaction of technical, organizational, and contextual controls rather than isolated cybersecurity mechanisms. By translating socio-technical interdependencies into operational security requirements aligned with standards promoted by the National Institute of Standards and Technology, the study provides a validated design-science artifact that supports secure IoMT deployment in emerging digital health ecosystems, particularly within resource-constrained healthcare systems.
Multimodal CNN–LSTM Framework for Real-Time Maize Disease Detection Mercy Chepkoech Tonui; John Kamau; Raymond Wafula Ongus
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.v8i2.3970

Abstract

Maize diseases present a major challenge to agricultural productivity and food security, particularly in low-resource settings in sub-Saharan Africa. Timely detection plays an important role in reducing yield losses and enabling effective farm management. This research introduces and validates a multimodal machine learning–based system for real-time maize disease detection in Bomet County, Kenya. The system integrates maize leaf image data, environmental sensor data, and farmer-reported observations to develop a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to automatically identify and categorize maize diseases. A mixed-methods research design was adopted, combining machine learning experiments with surveys and interviews involving farmers and agricultural officers. The findings revealed that Maize Lethal Necrosis (MLN) was the most prevalent disease (41%), followed by Gray Leaf Spot (33%) and Northern Leaf Blight (26%). Environmental variables such as humidity and temperature demonstrated strong associations with disease occurrence. The proposed multimodal CNN–LSTM framework integrates maize leaf images, environmental sensor data, and farmer observations, achieving an accuracy of 94.2%, which outperforms conventional image-only CNN models (87.5%) and environmental-data-based LSTM models (81.3%). Additionally, 78% of farmers reported faster disease diagnosis using the developed system. The findings demonstrate that the proposed system supports real-time maize disease detection through an edge-enabled architecture, enabling deployment on mobile devices and facilitating practical intelligent system integration in agricultural environments.

Page 16 of 16 | Total Record : 158

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