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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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Performance and Exhaust Emissions of Four Stroke Gasoline Engine Variations of Injection Duration Mapping with Ethanol Fuel E75 Muhamad Khoirul Anam; Mohammad Lutfiyanto; Adi Mulyadi; Wisnu Kuncoro
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 15 No. 1 (2025): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (January-May 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v15i1.15110

Abstract

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.
Design and Construction of an ECG Simulator with Bipolar Leads Based on the Arduino Nano Microcontroller Ibnu Riyanto; Dio Alif Pradana; Agoes Santika Hyperastuti; Inayah Lailatul
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.v15i2.15214

Abstract

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 Turmuzi; Kusrini
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.v15i2.15201

Abstract

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.
Low-Cost Fabrication of a Rear-Projection Display Assembly Using Consumer-Grade Components for Pseudo-Holographic Applications Kasmir Syariati; Citra Suardi; Reinaldo Lewis Lordianto; Andrey Hartawan Suwardi; Muhammad Aditya Ridwan; Enrico Kevin Ariantho; Javin Erasmus Clementino
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.v15i2.15202

Abstract

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.
Effect of Bran Media Variation on Maggot Growth in Waste Management Mawan Eko Defriatno; Siti Muyasaroh; Wahyu Nur Achmadin
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.v15i2.15206

Abstract

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.
Exploring 3D Convolutional Neural Network Models for Alzheimer’s Disease Classification Based on 3D MRI Images Titus Batlayeri; Subairi Subairi; Rahman Arifuddin; Bagas Martinus Rianu
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.v15i2.15208

Abstract

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.
Microplastic Contamination in Drinking Water Treatment Systems: A Case Study of Bedadung River Jember Siti Muyasaroh; Adi Mustika; Wahyu Nur Achmadin
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.v15i2.15205

Abstract

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.
Design of an IoT-Based Aquaculture Monitoring System Based on IoT in Fish Farming Ari Subowo; Nanang Pradita
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.v15i2.15203

Abstract

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.
Applications of Convolutional Neural Networks and Transfer Learning for Enhancing the Accuracy of Dragon Fruit Classification Adi Mulyadi; Fuad Ardiyansyah; Muhammad Zainal Roisul Amin; Budi Liswanto; Widhi Winata Sakti
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.v15i2.15209

Abstract

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.
Analysis of Power and Efficiency of a Three-Stage Four-Blade Savonius Wind Turbine with Variations in Load and Wind Speed Gerald Adityo Pohan; Gerardus Dharma Sandiawan; Febi Rahmadianto; Tito Arif Sutrisno
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.v15i2.15207

Abstract

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.

Page 2 of 4 | Total Record : 39

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