We would like to inform you that Volume 14, Number 2, and earlier issues of Energy: Jurnal Ilmiah Ilmu-ilmu Teknik are available on the old system at the following link: https://ejournal.upm.ac.id/index.php/energy/. This is due to technical issues caused by a recent hacker attack, which has prompted us to create a new, more secure system for future publications. As a result, issues from Volume 15, Number 1 onwards will be published on the new system at: https://e-journal.upm.ac.id/index.php/energy/. We appreciate your understanding and cooperation during this transition, and we sincerely apologize for any inconvenience caused.
Analysis of Fuel Spray Characteristics of Pertalite, Pertamax, and Pertamax Turbo with Ethanol Blending
In the automotive industry sector, technological advances have entered the stage of using alternative fuels, and the principle of fuel injection in gasoline engines (electronic fuel injection), to create efficient fuel use and more environmentally friendly combustion. The method used in this research is an experimental method, namely a method used to test the characteristics of gasoline fuel with ethanol against spray using an injector. In this research, we will compare the characteristics of Pertalite, Pertamax, Pertamax Turbo gasoline spray, a mixture of ethanol and gasoline in different mixture variations. This test aims to analyze spray tip penetration, spray angle, and velocity of spray. The results of collecting spray tip penetration data resulted in the highest data value for Pertamax Turbo fuel mixed with 7% ethanol and the lowest for Pertalite fuel mixed with 7% ethanol. The results of the spray angle data collection had the highest value in the Pertamax 10% ethanol mixture and the lowest in the 5% ethanol mixture with Pertamax Turbo fuel. From the results of the Velocity of spray data processing, the highest value was found in the 10% ethanol mixture with Pertalite fuel and the lowest was the 5% ethanol mixture with Pertamax Turbo fuel. For ethanol mixtures, more attention must be paid to the complete spray or misting of the injected fuel. Mix the fuel with ethanol according to applicable standards. Always pay attention to chamber equipment to avoid leaks when collecting data
Copyright (c) 2024 ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK https://creativecommons.org/licenses/by-nc/4.0
Design and Testing of a Milliampere Scale Leakage Current Sensor Based on a Current Transformer for Substation Lightning Protection
Leakage current in lightning arresters is an important indicator of insulation conditions and protection system performance in substations, so a measurement method capable of detecting currents in milliamperes accurately and stably is required. This study aims to design and test a milliamperes-scale leakage current sensor that can be used for lightning arrester monitoring. The developed sensor is based on a current transformer with an analog signal amplification and filtering circuit, and data acquisition using a microcontroller. Testing was carried out experimentally by varying the resistive load and the number of turns, then the measurement results were compared with an AVO meter as a reference measuring instrument. The test results showed that the sensor was able to measure leakage current with a relatively low error rate, where the lowest error occurred at a load of 33 Ω and increased at larger loads due to a decrease in current. The relationship between current and load variation showed a linear characteristic with a coefficient of determination (R²) value close to 1. In addition, magnetic field analysis showed that the relative permeability value of the ferrite core was in the range of 88.4 to 98, which reflects the stability of the core's magnetic properties under various test conditions. Based on these results, the developed sensor has the potential to be used as part of a lightning arrester leakage current monitoring system to support the maintenance and reliability of the substation protection system.
Copyright (c) 2026 ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK https://creativecommons.org/licenses/by-nc/4.0
An Epistemological Analysis of Metaheuristic MPPT Performance for Photovoltaic Systems under Partial Shading Conditions
Metaheuristic-based maximum power point tracking algorithms are widely used in photovoltaic systems to address nonlinear and multi-peak characteristics under partial shading conditions. However, many reported performance claims rely mainly on numerical simulation and therefore require cautious interpretation. This study presents a simulation-based comparative and epistemological analysis of Particle Swarm Optimization and Differential Evolution for photovoltaic maximum power point tracking. Both algorithms are implemented in an identical buck converter-based photovoltaic framework to ensure fair comparison. Performance is evaluated under uniform irradiance and partial shading conditions using convergence time and tracked power as evaluation metrics. The results show that under uniform irradiance, both algorithms reliably converge to the maximum power point with similar steady-state accuracy, while Particle Swarm Optimization converges faster. Under partial shading conditions, Particle Swarm Optimization consistently tracks the global maximum power point, whereas Differential Evolution shows occasional convergence failure or suboptimal tracking. From an epistemological standpoint, these findings constitute coherent and pragmatically useful model-based knowledge, while remaining provisional due to the absence of experimental validation.
Copyright (c) 2025 ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK https://creativecommons.org/licenses/by-nc/4.0
Sentiment Analysis of YouTube Comments Using the K-Nearest Neighbors (KNN) Method from an Axiological Perspective
The rapid development of social media as a space for digital interaction has increased the need for sentiment analysis to understand public opinion in a systematic and measurable way. This study analyzes YouTube comment sentiment using the K-Nearest Neighbor (K-NN) method while also examining the axiological value of applying this technology in support of a more ethical digital ecosystem. The dataset consists of 8,200 YouTube comments obtained from Kaggle without predefined sentiment labels. The data were preprocessed through case folding, tokenization, stopword removal, stemming, and normalization. Initial sentiment labels were generated automatically using K-Means clustering to form two classes—positive and negative—and were partially verified manually. The labeled data were split into training and testing sets with ratios of 50:50, 60:40, 70:30, and 80:20, and evaluated using K-NN with k values of 3, 5, 7, and 9. Model performance was assessed using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that accuracy ranges from 0.95 to 0.96, with the best performance achieved at a 70:30 split and an optimal k value yielding 0.96 accuracy. Beyond technical contributions, this study highlights the ethical and practical value of sentiment analysis for interpreting public opinion, supporting fairer content moderation, and improving communication quality in social media environments.
Analysis of System Reporting and Validation in DC Microgrid DSC Research: An Ex Post Facto Reproducibility Study
Distributed secondary control (DSC) is essential for voltage regulation and current sharing in DC microgrids with high renewable penetration. However, the diversity of system configurations, control strategies, and validation approaches challenges the reproducibility of published results. This study evaluates reporting and validation practices in 74 DSC-related articles from a reproducibility and research ethics perspective. Using a document-based ex post facto design, we construct a Reproducibility Readiness Index (RRI) based on system configuration reporting, validation completeness, and performance metric clarity. Results show that most studies exhibit low to medium reproducibility readiness, with only about one third achieving high levels. While system descriptions are generally adequate, validation setups and performance metrics are often incomplete or qualitative. Studies including hardware-in-the-loop or prototype experiments tend to score higher, though weak documentation remains a major limitation. These findings emphasize reproducibility as both a technical and ethical concern and support the need for stronger transparency and open science practices in DSC research on DC microgrids.
Risk Factors Causing Occupational Accidents Using the FMEA and Fishbone Methods in the Particle Board Division of PT. Kutai Timber Indonesia
Occupational Health and Safety (OHS) is an essential aspect of industrial operations, aimed at preventing workplace accidents and maintaining a safe and productive environment. The Particle Board Division of PT. Kutai Timber Indonesia faces high accident risks due to heavy machinery and high-temperature processes. This study evaluates potential occupational hazards using the Failure Mode and Effects Analysis (FMEA) and Fishbone Diagram methods. FMEA is used to prioritize risks based on the Risk Priority Number (RPN), while the Fishbone Diagram identifies the root causes of critical hazards. The highest identified risk is the absence of an emergency response team in each shift (RPN = 148.5). Other major risks include inadequate monitoring of corrective actions, poor documentation systems, low employee involvement in hazard identification, and insufficient technical controls. Recommendations include forming emergency response teams, conducting regular safety training, improving documentation systems, and enhancing technical and administrative controls. Implementing these measures is expected to reduce accident rates and strengthen the safety culture in the company.
Geochemical and Mineralogical Analysis of Mining Tailings in Mimika Regency for Reutilization Potential Evaluation
This study characterizes the geochemical, mineralogical, and morphological properties of mine tailings along the Kali Kabur River in Mimika Regency, Central Papua. The tailings, generated by mining activities, contain valuable metallic minerals, including iron (Fe), copper (Cu), and titanium (Ti). Using X-Ray Fluorescence (XRF), X-Ray Diffraction (XRD), and Scanning Electron Microscope–Energy Dispersive X-Ray Spectroscopy (SEM–EDS), we identified major oxides (Fe₂O₃, SiO₂, CaO) and minerals (quartz, magnetite, ilmenite, chalcopyrite). Results indicate that the tailings possess potential for reutilization as secondary resources for metal extraction and as raw materials for iron sand production. This research provides novel insights into sustainable tailings management and offers guidance for local industries in the reutilization of mining waste, contributing to circular economy practices.
Implementation of Static Code Analysis to Detect Vulnerabilities in Applications Developed with the Assistance of Large-Language Models (LLM)
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.
Comparison of Energy Obtained from Solar Panels in Partial Shading Condition Using Various MPPT Techniques
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.
Design of a Hydraulic System for a Glycerin Waste Mixer Machine Using Finite Element Method
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.
Solar Powered Street Lighting in Rural Areas: A Value-Use Analysis of Green Technology Axiology
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.
An Epistemological Approach to Explainable Automated Assessment of Open Concept Map Propositions Using SHAP
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
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.
From THD to Causality: Epistemology of Artificial Intelligence-Based Harmonic Analysis in Hybrid Microgrids
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.
epistemology, harmonics analysis, microgrid
Effect of Bran Media Variation on Maggot Growth in Waste Management
This study aims to evaluate the effect of bran variation on the growth of black soldier fly larvae (Hermetia illucens) under controlled initial conditions of 250 g larvae and 250 g organic waste. The experiment was conducted using different doses of bran supplementation (20–200 g), with daily observations of larval weight changes over 11 days. The results indicate that bran addition significantly influenced larval growth rate. Moderate doses (60–100 g) produced higher and more consistent weight gain compared to low doses (20–40 g) or excessive doses (160–200 g). The highest growth was observed in the 80 g treatment, reaching 890 g on day 10 before declining on day 11. In conclusion, moderate bran supplementation provides the most optimal medium conditions for larval growth, while insufficient or excessive amounts tend to be less efficient.
A Conceptual Framework for Human AI Collaboration: Ontological and Epistemological Perspectives
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.
Microplastic Contamination in Drinking Water Treatment Systems: A Case Study of Bedadung River Jember
Microplastic contamination in drinking water systems is an emerging environmental and public health issue. This study investigated the abundance, size distribution, and polymer composition of microplastics across the Bedadung River water treatment chain, from source to consumer taps. Seven sampling points were established, including upstream locations, intake sites, treatment plant reservoirs, and tap water from two water treatment plants (Tegal Gede and Tegal Besar). Microplastic concentrations ranged from 49.33 to 160 particles/L, with particle sizes averaging 0.102–0.233 mm. Contrary to expected treatment outcomes, both plants demonstrated significant increases in microplastic concentrations post-treatment: Tegal Gede saw a 78% increase, while Tegal Besar exhibited a 140% rise. The highest contamination (160 particles/L) was recorded at the Tegal Besar treated water reservoir. These elevated microplastic levels in treated and consumer tap water highlight a critical gap in current water treatment processes, suggesting that material migration from treatment infrastructure, including plastic pipes and filtration components, may contribute to contamination. The predominance of fiber-type microplastics, mainly polyethylene terephthalate (PET), underscores the need for specialized microplastic removal processes and infrastructure upgrades to safeguard public health.
Analysis of the Impact of Solar Radiation on the Battery Charging Duration and Operation of a DC Solar-Powered Water Pump
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.
Comparison of Breakdown Voltage and Aging of Kraft Paper in Transformers Immersed in Mineral Oil, Natural Ester, and Synthetic Ester
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 Power and Efficiency of a Three-Stage Four-Blade Savonius Wind Turbine with Variations in Load and Wind Speed
The wind potential in Indonesia generally has low wind speeds ranging from 3 m/s to 7 m/s, making vertical-axis wind turbines very suitable for use under such low-wind-speed conditions. Therefore, the concept of developing a device emerged—designing a laboratory-scale Savonius wind turbine utilizing a wind tunnel so that a multi-stage windmill can operate using a 3D printing method. The testing was carried out using the observational method, which involved directly observing the object under study—in this case, a four-blade Savonius wind turbine—to determine the power output at specific wind speeds and loads using a wind tunnel. The experiment used two load variations of 0.01 kg and 0.02 kg at wind speeds of 6 m/s and 7 m/s. At wind speeds of 6 m/s and 7 m/s, the turbine produced shaft rotations of 427 rpm, 259 rpm, 359 rpm, and 305 rpm with loads of 0.01 kg and 0.02 kg. For the four-blade configuration, the wind speeds of 6 m/s and 7 m/s generated turbine power outputs of 10.033 W and 6.318 W with loads of 0.01 kg and 0.02 kg, respectively. At wind speeds of 6 m/s and 7 m/s with loads of 0.01 kg and 0.02 kg, the turbine efficiencies were 0.707%, 0.682%, 1.193%, and 1.611%. For the four-blade configuration at 6 m/s and 7 m/s with loads of 0.01 kg and 0.02 kg, the generator power outputs were 7.08 W, 0 W, 3.29 W, and 0 W, respectively. Based on the test results and data analysis—including wind turbine rotational speed, turbine power, generator power, and turbine efficiency—the findings show a directly proportional relationship. Therefore, the higher the number of blades, the greater the turbine’s rotational speed, turbine power, generator power, and efficiency.
Applications of Convolutional Neural Networks and Transfer Learning for Enhancing the Accuracy of Dragon Fruit Classification
This paper discusses the application of Convolutional Neural Network (CNN) and Transfer Learning (TL) methods to improve the accuracy of dragon fruit classification. The application of the CNN method in real-time testing for classifying three types of dragon fruit only achieved an accuracy rate of 33.3%. Therefore, the CNN and TL methods using the Stochastic Gradient Descent (O-SGD) optimizer and the Root Mean Square Propagation (O-RMSProp) optimizer are proposed to improve the accuracy rate in classifying three types of dragon fruit: ripe, unripe, and rotten. The results of applying the CNN method with O-SGD at epoch 100 yielded an accuracy of 27.18%, val accuracy of 27.27%, loss of 1.407, and val loss of 1.405, while O-RMSProp at epoch 100 yielded an accuracy of 99.11%, val accuracy of 100%, loss of 0.073, and val loss of 0.058. Meanwhile, the application of the TL method with O-SGD at epoch 100 yielded an accuracy of 89.35%, val accuracy of 91.82%, loss of 0.462, and val loss of 0.443. TL with O-RMSProp at epoch 100 yielded an accuracy of 100%, val accuracy of 100%, loss of 0.002, and val loss of 0.003. The performance of the TL method with O-SGD and O-RMSProp is more accurate in classifying three types of dragon fruit compared to the CNN O-SGD and O-RMSProp models. This research contributes to improving the accuracy level of the CNN classification method to ±99-100%, and the application of this technology is an effort to enhance production quality and support smart agriculture in Banyuwangi Regency.
Design of an IoT-Based Aquaculture Monitoring System Based on IoT in Fish Farming
Maintaining optimal water quality is essential for achieving high productivity in aquaculture. However, conventional monitoring practices among small-scale fish farmers remain manual and inefficient. This study presents the design and evaluation of a low-cost Internet of Things (IoT)-based water quality monitoring system tailored for rural aquaculture applications. The prototype integrates an ESP8266 microcontroller with pH, Total Dissolved Solids (TDS), and temperature sensors to enable real-time data collection and automated control. Field testing was conducted in a 2 × 3 × 1 m fish pond for seven days with data sampling every 5 minutes. The system achieved an average transmission latency of 4.2 seconds and 99 % data-delivery reliability. Measurement accuracy compared to manual instruments showed deviations of ±0.08 pH, ±0.4 °C, and ±15 ppm TDS. With a total hardware cost of approximately Rp 950,000 (≈ USD 60), the proposed system demonstrates a practical, reliable, and affordable solution for continuous water-quality monitoring, supporting sustainable fish farming in rural areas.
Exploring 3D Convolutional Neural Network Models for Alzheimer’s Disease Classification Based on 3D MRI Images
Alzheimer’s disease is a common form of progressive dementia, especially among the elderly, and is characterized by a decline in cognitive function. Classifying this disease using 3D brain imaging through MRI is challenging due to the complexity of the data and the similarity of features across classes. This study develops a classification model based on a 3D Convolutional Neural Network (3D CNN) architecture, specifically using ResNet-18. The dataset used is obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), consisting of 1,281 samples evenly distributed across three classes: Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). The data undergo several preprocessing steps, including skull stripping, normalization, and augmentation. The model is tested in two configurations: without dropout and with a dropout rate of 0.3. The results show that the model with dropout performs better, achieving a classification accuracy of 62.0% and a macro F1-score of 0.604. The model outperforms ADNet and Vision Transformer, and approaches the accuracy of Vision Mamba. Nevertheless, this approach still requires further development, particularly in improving accuracy for the CN class and reducing performance imbalance across classes.
Low-Cost Fabrication of a Rear-Projection Display Assembly Using Consumer-Grade Components for Pseudo-Holographic Applications
Rear-projection surfaces are integral to pseudo-holographic and immersive display systems, yet commercial solutions often demand expensive materials and precision hardware. This paper presents the design, fabrication, and preliminary evaluation of a low-cost rear-projection display assembly constructed from consumer-grade components. A transparent rear-projection film of unknown specification was mounted onto a custom 3D-printed frame and affixed to a flat acrylic sheet to form a projection screen. Assembly relied on readily available materials and hobbyist fabrication techniques. Qualitative projection tests using a standard classroom projector demonstrate that the DIY screen produces clear images with wide viewing angles in dark environments, suitable for pseudo-holographic effects. Under moderate ambient light, the image remains visible with slight contrast loss and minimal hot-spotting. The entire assembly was built at a fraction of the cost of commercial rear-projection screens. These results suggest that the proposed workflow can enable affordable, functional rear-projection displays for educational demonstrations, prototyping, and artistic installations without specialized equipment.
Design and Construction of an ECG Simulator with Bipolar Leads Based on the Arduino Nano Microcontroller
Electrocardiogram (ECG) devices require periodic testing and calibration to ensure diagnostic accuracy, especially in clinical settings where reliable patient monitoring is critical. However, commercially available ECG simulators remain relatively expensive and difficult to access in low-resource environments, creating a gap in the availability of affordable calibration tools. This study aims to design an economical and practical ECG simulator based on the Arduino Nano microcontroller using PWM pins to generate bipolar lead signals. The simulator features an LED indicator that mimics the human heartbeat and provides four operating modes: Mode I (Normal sinus rhythm) 80 BPM, Mode II (Bradycardia) 40 BPM, Mode III (Normal sinus rhythm) 120 BPM, and Mode IV (Tachycardia) 120 BPM. Testing was conducted by collecting data 10 times for each mode across Lead I, Lead II, and Lead III to verify BPM readings and PQRST waveform outputs on both ECG and patient monitor devices. The results demonstrated average accuracies of 98.70% on the ECG and 99.37% on the patient monitor, with deviations of 1.3% and 0.63%, respectively—well within the tolerance limits of the ECRI 410-20010301 standard (±5%). These findings indicate that the proposed simulator offers a reliable, low-cost alternative for internal calibration of patient monitors with bipolar leads, providing a practical and accessible solution for healthcare facilities with limited resources.
Optimization of the Recurrent Neural Network (RNN) Model for SQL Injection Intrusion Detection In Databases
This study aims to optimize the Recurrent Neural Network (RNN) model, particularly using Long Short-Term Memory (LSTM) layers, for detecting SQL injection attacks in database systems. SQL injection remains a critical cybersecurity threat, capable of compromising sensitive data and damaging organizational integrity. Using a dataset sourced from Kaggle, the research explores various hyperparameter combinations, including activation functions (Softmax, Tanh, ReLU, PReLU, and LReLU), optimizers (Adam, SGD), learning rates (0.001, 0.0001), and regularization methods (Dropout and L2). Experimental results show that the highest model performance was achieved using the Tanh activation function, Adam optimizer, a learning rate of 0.001, and 100 epochs—resulting in an accuracy of 98.17% and a loss value of 0.4084. Tanh also performed well with slightly lower accuracy. The findings demonstrate that using adaptive optimizers like Adam and non-saturating activation functions significantly improves model performance. The study also addresses challenges such as class imbalance and emphasizes the importance of comprehensive evaluation metrics beyond accuracy. These results confirm that an optimized RNN-LSTM model offers high detection accuracy with low false positive rates, suitable for real-time intrusion detection systems. This research contributes to strengthening database security and serves as a foundation for further development in intelligent threat detection models.
Performance and Exhaust Emissions of Four Stroke Gasoline Engine Variations of Injection Duration Mapping with Ethanol Fuel E75
Ethanol is a biofuel in which its storage and physical condition are almost the same as gasoline (fuel oil). It is still possible to drive a gasoline engine with ethanol in low concentrations. However, applying high concentrations to gasoline engines will require modifications, such as changing injection duration and compression ratio. This is done to get better performance and emissions than the use of gasoline fuel. In this study, the gasoline engine used was a 177cc single-cylinder four-stroke engine with E75 (75% Ethanol and 25% Pertalite), where the test engine was modified on the injection duration section with the replacement of standard ECU components into a programmable ECU. The replacement aims to facilitate changes in engine parameters, such as injection duration. In injection duration mapping, basic mapping values are added by 2%, 4%, 6%, and 8%. Then, the compression ratio is changed from 11:1 to 13:1. In comparison, testing is performed under standard machine conditions using Pertalite (E0). To test engine performance, a Prony brake dynamometer is used, while to test exhaust emissions are used exhaust gas analyser. E75 fuel use in the study resulted in torque and power increased by 30% and 19% with additional injection duration (8%) and (6%). However, in E75 use the duration of injection (2%) and (4%) decreased. This is related to AFR values, where injection duration (2%) and (4%) run on lean AFR. Then the SFC result increases, which is affected by the low heat value of ethanol fuel. And the use of ethanol E75 can reduce CO and HC emissions by 69.5% and 17% respectively.
Finding the Harmonic Contents of Periodic Square Wave
This paper presents an analysis of the harmonic content in a periodic square wave. Three methods are employed to evaluate the harmonic characteristics: Fourier series decomposition, Crest Factor (CF), and Total Harmonic Distortion (THD). The Fourier series method decomposes the square wave signal into its direct current (DC) component, cosine, and sine terms. The second method, CF, is used as an initial indication of the presence of harmonics in the signal, where a CF value greater than 1 suggests a signal with harmonics. Finally, the THD method is utilized, which includes both the Fundamental THD (THDF) and the Root Mean Square THD (THDR), quantifying the degree of harmonic distortion in the signal. For the case studied, where the square wave has an amplitude of 1, the CF is found to be 1, the THDF is 46.1%, and the THDR is 41.9%. These results suggest that the square wave contains significant harmonic distortion. The paper highlights the importance of understanding harmonic content in power systems, particularly in the context of nonlinear loads that introduce distortion in voltage and current waveforms. The findings contribute to the broader understanding of harmonic effects on system performance, which can lead to issues such as overheating, protection malfunctions, and reduced power quality.
Deep Learning-Based Implementation of Convolutional Neural Networks for Skin Disease Detection Through Image Classification on Mobile Platforms
Maintaining skin health is essential, as poor skin conditions can lead to various diseases. To address this, early detection and classification of skin disorders is crucial. This study presents a deep learning-based Android application that enables users to detect and classify types of skin diseases through image input. The application integrates a Convolutional Neural Network (CNN) trained on labeled image datasets. The model achieved a training accuracy of 96% and validation accuracy of 83%. To provide a more comprehensive performance evaluation, metrics such as precision (87.75%), recall (84.29%), and F1-score (85.20%) were calculated. The evaluation was conducted using confusion matrix analysis based on eight skin disease classes. The implementation of CNN into an Android-based platform provides a practical and accessible tool for early skin disease detection and classification for the general public.
Application of Averrhoa Blimbi, L and Activated Carbon to Measure the Characteristics of No-Load Output Voltage, Output Voltage with LED Load and LED Voltage Drop
This paper discusses the use of Averrhoa bilimbi L. (blimbing wuluh) liquid and powdered activated carbon to examine the output voltage characteristics and voltage drop in LEDs. Previous studies using various fruits and fermentation methods showed inconsistent output voltages, prompting the use of Averrhoa bilimbi due to its natural citric acid content, which can produce a small but measurable voltage (0–499 mV). To improve this voltage, activated carbon (MES size 70329-06950) was added to the mixture. The experimental setup included Zn and Cu electrodes, a 30V 5A power supply, and liquid volumes of 75 ml, 100 ml, and 125 ml tested over durations of 1, 5, and 10 minutes. Results showed that without activated carbon, the output voltage ranged from 350–499 mV, while LED voltage dropped slightly (8–0 mV). With activated carbon, output voltage significantly increased to 434–660 mV, while the LED voltage remained stable and the drop minimal. These findings indicate that the addition of activated carbon enhances the electrical output of Averrhoa bilimbi and reduces voltage loss, suggesting its potential use as a simple, renewable bioenergy source for low-power applications.