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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 153 Documents
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Adaptive Kernel Probability Model (AKPM) for Interpretable and Reliable Diabetes Prediction using Clinical Diagnostic Data Hiswati, Marselina Endah; Azijah, Izattul; Subandi, Yeyen; Diqi, Mohammad
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; Haddin, Muhamad
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 Yasya, Haqrodji Prabu; Pravitasari, Deria; Trihasto, Agung; Kurniawan, Andriyatna Agung
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.

Page 16 of 16 | Total Record : 153

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