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.
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.
Copyright (c) 2025 ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK https://creativecommons.org/licenses/by-nc/4.0
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.
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.
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.
Sentiment Analysis on NU Online Using KNN Method Important Findings of the Article
The large population of Indonesia results in the rapid growth of the mobile economy very quickly, Business of Apps in January 2025 states that more than 200 million Indonesians spend an average of 5.7 hours per day in using cell phones or applications contained on cell phones. One of the applications on PlayStore is NU Online with more than 1 million downloads and there are 27 thousand user reviews with an average rating of 4.8. NU Online App was created by one of the largest organizations in Indonesia, Nahdlatul Ulama, the purpose of making the application is to provide convenience to users, especially among pesantren and NU members. Data Mining is the process of finding patterns, trends, and useful information from large amounts of data. One of the algorithms in data mining is KNN. This research focuses on sentiment analysis on the NU Online application to find out that the review is worth POSITIVE, and NEGATIVE using the KNN method, resulting in 72% showing positive sentiment, 28% showing negative sentiment, this shows the performance and performance of the NU Online love application is very good with the support of the highest accuracy evaluation value at K10 which is 0.83.
Rule-Based System of Ammonia Gas Detection in Tofu Industry Based on Internet of Things and Cloud
Industry of tofu is one of the driving forces of the economy but also has a negative impact because the waste produced can pollute the environment if not handled properly. Tofu industry waste is usually in the form of liquid waste containing several gases that are harmful to human health such as ammonia. Ammonia is a pollutant gas that causes health problems such as respiratory disorders to death. Therefore, a system is needed that is able to detect ammonia levels in tofu industry liquid waste. The purpose of this study is to develop a system that can monitor and provide real-time hazard notifications if ammonia levels are at a safe threshold. This study uses a rule-based system method for grouping ammonia level data, the Internet of Things (IoT) for real-time data collection devices, and the cloud to store data generated from IoT devices. The results of the study showed that ammonia levels were <200 ppm so that they were included in the alert category because the effects caused could cause irritation to the eyes, nose and throat.
Study Analysis of Three Phase Induction Motor Starting Using ETAP at PLN ULPL Indralaya, South Sumatera
Steam Generator of Electricity Power Plant at PT. PLN (Persero) UPDK Keramasan of ULPL Indralaya, South Sumatra Area. This power plant has 3-phase induction motors, which are the most commonly used motors because of their reliability. The 3-phase motor is directly connected to the supply at a high starting current of around 5 to 7 times the motor rating current (nominal current). This large starting current can cause a considerable voltage drop on the supply line, thus affecting other loads connected to the supply line. Because the large current flows for a long time, it causes the motor to heat up, which will damage the insulation. Therefore, when starting the motor, the motor bus voltage must be kept at around 80% of the voltage rating. This study investigates whether the motor could start successfully under the operating conditions. We used ETAP software to simulate motor starting and also analyze these motors is to obtain the values of starting current, starting torque, and voltage drop from the results of using VFD (variable frequency drive) on two motor units in LV Switchgear SUS-1 whose previous existing conditions were using DOL (direct on line). A result of our studies, we found that the system can still maintain a bus voltage of 99.94%, a small starting current when starting the motor of 0% FLA (1 second), and reaches 99% FLA (4 seconds) with a starting torque of 54% & 57% of the load torque.
Application of Inorganic – Organic Eutectic PCM (Phase Change Material) to Improve Coolbox Efficiency
The demand for efficient and environmentally friendly cold storage systems continues to increase, especially in the fisheries industry to maintain the quality of the catch. One alternative cooling medium is Phase Change Material (PCM), which is capable of storing and releasing energy in the form of latent heat during phase transitions. This study aims to evaluate the effectiveness of eutectic PCM composed of inorganic–organic materials in enhancing coolbox efficiency. The PCM was synthesized using food-grade ingredients: water, salt (NaCl), acetic acid, propylene glycol, and tapioca flour, and was then tested using Differential Scanning Calorimetry (DSC) and temperature profile analysis. Testing was conducted using various mass ratios of PCM to fish (from 1:1 to 4:1), and compared to conventional cooling media such as ice. The results showed that PCM with a 4:1 ratio was able to maintain temperatures in the range of -10°C to -0.4°C for 6 hours, with a total heat capacity of 1.4567 kJ/kg, significantly higher than that of ice (0.3120 kJ/kg). Furthermore, organoleptic tests indicated that fish stored with the 4:1 PCM remained fresh, scoring highest in aspects of odor, texture, and appearance. Therefore, the PCM developed in this study has proven to be more efficient in maintaining low temperatures and preserving fish quality compared to conventional cooling methods.
PCM, Eutectic, Cooling, Coolbox, Energy Efficiency, and Fish Storage
Technical Feasibility Study and Design of 60 kWP On-Grid Solar Power Plant at Joint Lecture Building-5, University of Muhammadiyah Malang
The problem of dependence on fossil energy which has an impact on the energy crisis and the environment. The current renewable energy mix is still at 14% which is far from the 23% target in 2025, encouraging the need for the development of Solar Power Plants (SPP) as part of the New Renewable Energy (NRE) mix in Indonesia. This research aims to design an On-Grid Solar PV system with a capacity of 60 kWp at the Joint Lecture Building-5 (JLB) of Muhammadiyah University of Malang (UMM) and analyze the technical and economic feasibility. The methods used include electrical load survey, climate and radiation data collection, and system design simulation using PVsyst software. The simulation results show an annual Performance Ratio (PR) value of 0.839 with an annual energy production of 105,000 kWh. Energy production analysis shows stable system performance with total losses below 2 kWh/kWp/day. However, based on the economic analysis, the 20-year Net Present Value (NPV) is negative at - IDR 646,841,211, indicating the project is not financially viable without incentive support. Nonetheless, the project is considered technically feasible and can serve as a reference for the implementation of higher education-scale solar power plants.
Enhancing Combustion Characteristics of Callophyllum Inophyllum Fuel Using Magnetic Fields
The challenges of energy security and environmental sustainability have driven the development of eco-friendly alternative fuels, among which second-generation biodiesel has gained significant attention. This study aims to analyze the combustion quality of biodiesel derived from Calophyllum inophyllum oil (CIME) under the influence of an external magnetic field on the laminar flame speed in a premixed flame system. CIME biodiesel was produced through a transesterification process following degumming and esterification stages. Experimental tests were conducted using a stainless-steel Bunsen burner with a T-junction configuration and integrated heating system, in which magnetic fields with strengths ranging from 7000 to 10000 gauss were applied directly to the flame region. The equivalence ratio (ϕ) of the fuel-air mixture was varied between 0.4 and 1.4. Laminar flame speed was calculated from flame visualization data based on the observed flame angle. The results indicate that the magnetic field significantly enhanced the laminar flame speed, with the highest value observed at ϕ = 0.8 and a magnetic field strength of 10000 gauss. This phenomenon is attributed to the increased oxygen concentration in the reaction zone, induced by the magnetic attraction of paramagnetic O₂ molecules. These findings suggest that the integration of magnetic fields into biodiesel combustion processes can potentially improve energy efficiency and reduce emissions, offering an innovative approach for advancing renewable energy systems.
Study of Viscosity and Density Changes of Waste Cooking Oil Biodiesel Due to Gradual Heat Treatment
Biodiesel from used cooking oil is a renewable fuel for diesel engines that has the potential to be developed as a substitute for fossil fuels. However, the physical properties of biodiesel such as viscosity and density are affected by heat treatment. This will certainly affect engine performance, combustion quality and diesel engine efficiency. This study aims to analyze the heat treatment of used cooking oil biodiesel in stages with temperature values of 40°C, 50°C, 60°C, 70°C, and 80°C. Biodiesel is obtained from used cooking oil by a synthesis process using transesterification with ethanol and KOH catalyst. The results of the study were the viscosity and density values of used cooking oil biodiesel. The results showed that heat treatment of biodiesel reduced the viscosity and density values. The viscosity value continued to decrease along with the increase in the heating value from 4.52 cSt to 2.84 cSt. For the density value also decreased from 867 kg/m³ to 827 kg/m³. This study shows that heat treatment on biodiesel before use has the potential to improve atomization characteristics and fuel flow rates, so that combustion efficiency and diesel engine performance can be optimal.