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Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Published by Universitas Panca Marga Probolinggo
ISSN : 20884591     EISSN : 29622565     DOI : https://doi.org/10.51747/energy.vol15no1
Core Subject : Science, Engineering,
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering
Energy Journal serves as a platform for information and communication of various research findings and scientific writings in the field of engineering, contributed by practitioners, researchers, and academics who are involved in and have a keen interest in the development of science and technology. The scope of the Energy Journal covers all branches of engineering, including but not limited to: Electrical Engineering Mechanical Engineering Industrial Engineering Engineering Physics Chemical Engineering Materials and Metallurgical Engineering Environmental Engineering Mining Engineering Civil Engineering Architectural Engineering Computer Engineering Informatics Engineering Geodesy and Geomatics Engineering And other engineering disciplines not explicitly mentioned
Arjuna Subject : Energi (semua kategori) - Energi (Lain-Lain)
Articles 39 Documents
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Comparison of Breakdown Voltage and Aging of Kraft Paper in Transformers Immersed in Mineral Oil, Natural Ester, and Synthetic Ester Hanifiyah Darna Fidya Amaral; Galuh Prawestri Citra Handani; Rahman Azis Prasojo; Reyhan Fadhlur Rahman; Satria Lutfi Hermawan; Ruwah Joto; Afidah Zuroida; Ahmad Hermawan; Irwan Heryanto/Eryk
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 15 No. 2 (2025): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (July-November 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v15i2.15215

Abstract

Transformers are critical components in electrical power systems that rely on insulation oil and paper to ensure reliable and long-lasting operation. This study aims to compare the performance of kraft insulation paper immersed in three types of transformer oil, namely mineral oil, natural ester, and synthetic ester, through the accelerated thermal aging method. Evaluation was carried out based on tensile strength and water content testing in accordance with the IEEE C57.100-2011 standard, as well as breakdown voltage testing in accordance with the IEC 60156-2018 standard. Tests were conducted with samples taken at intervals of 24, 240, 480, and 720 hours at 150°C. The results showed that insulation paper soaked in mineral oil experienced a 34% decrease in tensile strength, while natural ester and synthetic ester oils showed tensile strengths of 66% and 52%, respectively. In the water content test, natural ester showed the best performance with the lowest water content (5.19%), followed by mineral oil (7.47%) and synthetic ester (46.70%). In the breakdown voltage test, natural ester had the highest breakdown voltage of 66.26 kV, followed by mineral oil at 63.96 kV and synthetic ester at 41.14 kV. Estimated insulation life based on lifetime regression showed that natural ester has a lifetime of up to 37.9 years, synthetic ester 25.6 years, and mineral oil 20.5 years. In addition, in transformers operating at 130°C, natural ester insulation oil can increase loading up to 20% higher than mineral oil and has a 15-20°C higher operating temperature. This study recommends the use of kraft insulation paper impregnated with natural ester and synthetic ester oils as an alternative to mineral oil, because both are superior environmentally friendly alternatives compared to mineral oil. Both oils have better mechanical strength and insulation, lower water content, and can slow down transformer aging. Furthermore, these ester oils are non-flammable, biodegradable, and reduce the risk of failure due to excessive heat or discharge, thereby increasing the reliability and lifespan of the transformer. With these properties, ester oils support safer and more sustainable operations for the power system.
Analysis of the Impact of Solar Radiation on the Battery Charging Duration and Operation of a DC Solar-Powered Water Pump Bakti Indra Kurniawan; Mohammad Noor Hidayat; Priya Surya Harijanto; Ananda Setyo Abimanyu; Naufaliawan Abraham
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 15 No. 2 (2025): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (July-November 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v15i2.15204

Abstract

This research examines the effect of solar irradiance on the performance of a solar-powered water pump (SWP). The primary objective is to determine how solar irradiance affects battery charging time and pump operational duration. Using a system consisting of a solar panel, a battery, and a pump in the Malang region, we collected data on both sunny and cloudy days. The results indicate that solar irradiance is a crucial determinant of the system's efficiency. On sunny days, battery charging is faster, and the pump can operate for a longer period. Conversely, on cloudy days, battery charging slows down, and the pump's duration becomes shorter. This study concludes that solar irradiance is a key factor in optimizing SWP systems, and the use of a battery is essential to ensure stable operation.
Implementation of Static Code Analysis to Detect Vulnerabilities in Applications Developed with the Assistance of Large-Language Models (LLM) Arnold Nasir; Kasmir Syariati; Citra Suardi; David Sundoro; Juan Salao Biantong; Reinaldo Lewis Lordianto
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 15 No. 2 (2025): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (July-November 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v15i2.15210

Abstract

The emergence of large language models (LLMs), such as ChatGPT and GitHub Copilot, has transformed software development, including in higher education. Students can now easily create PHP code for Laravel web applications. This research implements static code analysis with PHPStan to detect security vulnerabilities in student-developed PHP code that is likely assisted by LLMs. The analysis was performed on the full code of 28 capstone projects, focusing on student projects that demonstrated patterns consistent with heavy LLM output use. The results show that 64.16% of LLM-assisted code often neglects data sanitization, uses raw queries without parameterization, and contains vulnerable authentication logic. This study contributes to web application security literacy for students and recommends static analysis as a pedagogical and preventive tool.
A Conceptual Framework for Human AI Collaboration: Ontological and Epistemological Perspectives Meyti Eka Apriyani; Syaad Patmanthara
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.251

Abstract

Collaboration between humans and artificial intelligence (AI) has become a pivotal phenomenon in the evolution of information systems, yet its philosophical foundations remain underexplored. This study develops an integrative conceptual framework that combines ontological and epistemological perspectives to examine how human–AI collaboration shapes knowledge creation and decision-making within sociotechnical contexts. The proposed framework identifies five ontological levels of AI agency and four epistemological processes underlying hybrid knowledge formation. It further integrates six interrelated dimensions—ontological, epistemological, technical, ethical, social, and organizational—that collectively define the dynamics of human–AI collaboration. The findings contribute to the theoretical discourse by introducing the constructs of quasi-epistemic entities and hybrid epistemology, which reconceptualize AI not merely as a computational artifact but as a participant in epistemic processes, thereby extending existing theories of distributed cognition and epistemic accountability beyond instrumental human–machine models. Practically, the framework informs the design of transparent, adaptive, and ethically aligned human–AI systems within information-intensive environments.
From THD to Causality: Epistemology of Artificial Intelligence-Based Harmonic Analysis in Hybrid Microgrids Ana Nuril Achadiyah; Arif Nur Afandi; Syaad Patmanthara
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.252

Abstract

The increasing penetration of PV, wind turbines, battery storage (BESS), and electric vehicle charging stations (EVCS) in hybrid microgrids complicates the harmonic landscape. Common practices rely on FFT-based measurements and THD/TDD indices, but source attribution and causality assignment are often uncertain. We map how epistemological positions shape how we measure, explain, and justify technical claims about harmonics. We then propose an Epistemically-Informed Harmonic AI (EPI-HAI) framework that combines standardized measurements (IEC/IEEE), physics-constrained AI modeling (KCL/KVL, impedance), XAI (SHAP/Grad-CAM), and uncertainty management to strengthen epistemic trust. A vignette of a PV–BESS–EVCS microgrid demonstrates that triangulation of evidence (n-order patterns, operating logs, line impedance) is more valid than mere spectral correlation. The three main contributions of this article are, the compilation of a map of the relationship between epistemology and methodology in harmonic analysis, the formulation of transparent and accountable physics-based artificial intelligence (AI) design principles and a discussion of pedagogical implications that can be applied in the development of power engineering curricula.
An Epistemological Approach to Explainable Automated Assessment of Open Concept Map Propositions Using SHAP Mega Satya Ciptaningrum; Syaad Patmanthara; Didik Dwi Prasetya
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.255

Abstract

Concept mapping is widely recognized as an effective method for supporting meaningful learning and critical thinking because it allows teachers to assess students’ underlying knowledge structures. However, evaluating concept maps and providing feedback remain challenging, as these processes are time-consuming, increase teachers’ workload, and can reduce instructional efficiency. To address this issue, this study applies Transformer-based architectures, which rely on large-scale pre-training and task-specific fine-tuning, to develop an automated assessment system for concept maps. In addition, Explainable Artificial Intelligence (XAI) is integrated through the SHAP (SHapley Additive exPlanations) framework to generate interpretable explanations of the model’s scoring decisions. Using Transformer models such as BERT and DeBERTa, SHAP values are computed at the token level to show how individual words within each proposition contribute to the final score. The results indicate that tokens with positive SHAP values increase scores in line with correct rubric indicators, whereas negative values reduce them. Tokens that consistently show positive contributions in high-scoring outputs reflect stable and faithful model reasoning. Overall, the findings demonstrate that combining Transformer-based assessment with SHAP explanations improves epistemic transparency by aligning the model’s internal reasoning with expert evaluation criteria, thereby supporting more reliable, interpretable, and trustworthy automated feedback in concept mapping-based learning.
The Application of Machine Learning in Liver Disease Diagnosis: Analysis of Algorithm Performance and Axiological Implications Sri Farida Utami; Syaad Patmanthara
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.253

Abstract

Liver disease remains a significant global health challenge, requiring accurate and timely diagnosis to improve patient outcomes and reduce healthcare costs. This study investigates the application of four machine learning classification algorithms—Decision Tree, Random Forest, Naïve Bayes, and K-Nearest Neighbors (KNN)—to predict the presence of liver disease using a dataset sourced from Kaggle. These algorithms were evaluated based on performance metrics such as accuracy, precision, recall, and F1 score. Both Decision Tree and Random Forest achieved the highest accuracy rate of 72.41%, demonstrating their robustness in classifying liver disease cases. However, these models showed some limitations in identifying patients without liver disease. Naïve Bayes, with an accuracy of 60.34%, exhibited an impressive recall rate of 96.97%, indicating its potential in detecting liver disease cases, though at the cost of lower precision. KNN, with an accuracy of 70.69%, proved to be a competitive option in the classification task. Beyond technical performance, the study also explores the ethical and axiological implications of using machine learning in healthcare, emphasizing the importance of fairness, transparency, and human oversight. The research highlights the need for responsible deployment of machine learning technologies, ensuring they are aligned with ethical standards to avoid biases and enhance healthcare outcomes. This study demonstrates that machine learning can significantly support liver disease diagnosis, though it must be integrated with a comprehensive ethical framework to ensure equitable and transparent decision-making in clinical practice.
Solar Powered Street Lighting in Rural Areas: A Value-Use Analysis of Green Technology Axiology Didik Riyanto; Syaad Patmanthara; Arif Nur Afandi
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.254

Abstract

This study aims to analyze the utility value and axiological implications of the application of green technology, namely Solar Powered Street Lighting (PSL), in Duri Village, Slahung District, Ponorogo Regency. The main problem in the village is the lack of a public street lighting system due to the limited PLN electricity network on the connecting roads between villages. Through an axiological review, this solar power plant technology is analyzed not only from a technical aspect, but also from its beneficial value for community life. The research method includes field studies, planning, implementation of independent Public Street Lighting technology equipped with automatic sensors, implementation testing, and mentoring. The results of the implementation of one Public Street Lighting unit using solar electricity using Smart Bright Solar cell technology with 4000 lm lighting show that this technology provides an independent lighting solution for the general public, improves security, and supports environmental sustainability. The application of solar power plant on Public Street Lighting in rural areas realizes the axiological value of science as a means to improve the quality of life and create energy independence in remote areas.
Design of a Hydraulic System for a Glycerin Waste Mixer Machine Using Finite Element Method Rosalina Amanda; Ahmad Syifa Ubaidillah; Fipka Bisono; Widya Emilia Primaningtyas
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 15 No. 2 (2025): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (July-November 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v15i2.15212

Abstract

This study presents the design of a hydraulic-based glycerin waste mixer machine, intended for use in the cement industry as a sustainable alternative to coal. Glycerin waste, a byproduct of biodiesel production, has a calorific value of 25,175.98 kJ/kg, making it a viable substitute fuel. However, its high viscosity and tendency to solidify at low temperatures pose significant challenges for processing. This research addresses these challenges by designing a hydraulic mixer with structural strength verified through Finite Element Method (FEM) analysis. The design follows the Ulrich & Eppinger product design approach, including concept development, technical specifications, and hydraulic system planning. Using Autodesk Fusion 360, the machine's frame, made of ASTM A36 steel, withstood loads with a maximum stress of 87.5 MPa and a safety factor of 2.83, ensuring its structural integrity. The hydraulic system, employing a double-acting cylinder operating at 10 bar, requires a motor power of 4.09 kW and achieves a fluid flow rate of 235.5 L/min. Cost analysis revealed a 30.44% reduction in manufacturing costs compared to similar commercial machines, totaling IDR 16.7 million. These findings demonstrate the efficiency, safety, and economic viability of the mixer, offering a promising solution for glycerin waste utilization in the cement industry.
Comparison of Energy Obtained from Solar Panels in Partial Shading Condition Using Various MPPT Techniques Mohammad Jasa Afroni; Efendi S Wirateruna
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 15 No. 2 (2025): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (July-November 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v15i2.15211

Abstract

Partial shading conditions (PSC) create multiple peaks on the power–voltage (P–V) curve of photovoltaic (PV) systems, making it difficult for conventional Maximum Power Point Tracking (MPPT) algorithms to accurately identify the global maximum power point (GMPP). This study compares the performance of three MPPT techniques—Perturb and Observe (PNO), Global Maximum Power Point Detection (GMPPD), and the Four-Section (4S) method—by analyzing the electrical energy obtained during sudden changes in irradiance and shading. Experiments were conducted on two series-connected polycrystalline modules equipped with bypass diodes under three shading scenarios, with measurement data processed using an Arduino-based system. The novelty of this work lies in its experimental, energy-based comparison of PNO, GMPPD, and the recently developed 4S method under sequential irradiance transitions, providing a practical performance assessment that goes beyond instantaneous tracking evaluation commonly reported in previous studies. The results show that the 4S method significantly outperforms both PNO and GMPPD by providing faster tracking, lower computational demand, and superior accuracy under dynamic shading conditions. The total energy obtained using the 4S, GMPPD, and PNO methods was 4203.08 Wh, 3551.69 Wh, and 3091.60 Wh, respectively. These findings demonstrate that the 4S method offers the most efficient and reliable MPPT performance for PV systems operating under rapidly fluctuating environmental conditions.

Page 3 of 4 | Total Record : 39

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