Journal of Mechatronics and Artificial Intelligence
The Journal of Mechatronics and Artificial Intelligence (JMAI) (E-ISSN 3048-4227 P-ISSN 3062-729X) serves as a platform for disseminating scholarly research related to the fields of mechatronics and artificial intelligence, as well as related sub-disciplines. We extend an invitation to researchers, engineers, senior researchers, lecturers, and students from Indonesia as well as countries across the globe to disseminate their research findings through our journal's platform. A comprehensive and thorough review process will be implemented to guarantee the production of high-quality articles. Focus and Scope The Journal of Mechatronics and Artificial Intelligence (JMAI) (E-ISSN 3048-4227 P-ISSN 3062-729X) serves as a platform for disseminating scholarly research related to the fields of mechatronics and artificial intelligence, as well as related sub-disciplines. We extend an invitation to researchers, engineers, senior researchers, lecturers, and students from Indonesia as well as countries across the globe to disseminate their research findings through our journal's platform. A comprehensive and thorough review process will be implemented to guarantee the production of high-quality articles. The types of research that can be published on JMAI are: Literature review articles Empirical studies Case studies Theoritical articles The scope of the journal are: Mechatronics Industrial automation Robotics Control and Systems Sensors Electronics Electrical Machines Image processing and pattern recognition Artificial Intelligence Machine learning Instrumentation and Measurement Agents and multi-agent systems Natural language Energy
Efficiency Evaluation of a DC Generator Using Experimental Loss Measurements
This study evaluates the efficiency of a DC generator using experimental loss measurements based on a two-identical-machine method. Two identical DC machines were mechanically coupled, with one machine operated as the driving motor and the other as the generator under test. The total loss was determined from the difference between the motor input power and the generator output power, while the copper loss was calculated using the armature resistance obtained from a locked-rotor test. Since the generator did not have a field winding, only armature copper loss was considered. The friction loss was estimated from the no-load test, and the iron loss was determined using a residual loss method. The experimental results showed that the generator output power increased from 0.0064 W at 1.5 V to 1.8032 W at 5.0 V. The generator efficiency also increased with operating voltage, reaching a maximum value of 63.96% at 5.0 V. Copper loss increased significantly with load current and became a dominant loss component at higher operating conditions. The residual iron loss showed positive values at low voltage levels but became negative at higher voltages, indicating limitations in separating friction and iron losses using the current method. Overall, the proposed experimental approach was effective for evaluating the efficiency trend and identifying the dominant loss behavior of the DC generator, although further refinement is required to separate mechanical and magnetic losses more accurately.
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Savonius Turbine Suitability Analysis Based on Low Wind Speed Characteristics
Wind energy is one of the renewable energy sources with significant potential to be developed as an environmentally friendly power generation alternative. However, the selection of wind turbine types must be adjusted to the wind speed characteristics of the implementation area to ensure optimal performance. This study aims to analyze the suitability of the Savonius turbine based on low wind speed characteristics. The research was conducted at the Faculty of Engineering, Universitas Sultan Ageng Tirtayasa (FT UNTIRTA), by measuring wind speed at three different locations using an anemometer. The measurement locations included an open sports field, a building rooftop, and a green open space area. The measurement results showed that the wind speed ranged from 1.5 m/s to 3.2 m/s, categorized as low wind speed below 4 m/s. The obtained data were analyzed and compared with the wind turbine performance characteristic graph based on the relationship between power coefficient (Cp) and turbine tip speed ratio (TSR). The analysis results indicate that the Savonius turbine has the most suitable characteristics for low wind speed conditions because it can operate at low TSR values and has good self-starting capability and high starting torque. In addition, the Savonius turbine offers simple construction, ease of manufacturing, and the ability to operate under varying wind directions. Based on the research results, the Savonius turbine is recommended as an alternative small-scale wind power generation system for areas with low wind speed characteristics.
Smart Nutrition Monitoring: IoT-Based Rice Soil Health Classification Using Naive Bayes and Fuzzy Expert System
The implementation of Machine Learning (ML) in precision agriculture often stops at probabilistic outputs without producing directly executable (actionable) agronomic guidance. In response to this functional gap, this study created an IoT proof of Concept (PoC) system architecture design that integrates Machine Learning with the Mamdani Fuzzy Expert System. To validate the software before entering the implementation phase, the system was trained and tested using 5000 synthetic datasets. This synthetic data was generated using a Multimodal Gaussian distribution to represent real-world conditions in the field (TDS, pH, humidity, and temperature). The evaluation results obtained proved that the Naive Bayes classification was very efficient for use in system architectures with minimal resources, with an accuracy of 96.65% and a latency of 0.054. In the final stage of the system, the Fuzzy Logic integration successfully produced output in the form of solutions and recommendations based on the values generated by the Machine Learning system. The integration of these three systems successfully became a unified, complete architectural system. This system has proven to be able to bridge the gap between the complexity of the system and the needs of farmers, and is ready to be implemented in the field using real-time data.
Mamdani Fuzzy-Based Soil Fertility Detection Using Moisture and Color Sensors
Soil fertility plays an important role in supporting agricultural productivity and sustainable farming practices. Conventional methods for determining soil fertility, such as visual observation of soil color and manual inspection of soil moisture, are often subjective, inefficient, and less accurate. This study proposes an Internet of Things (IoT)-based soil fertility detection system using a soil moisture sensor and a TCS3200 color sensor to provide real-time and objective soil condition monitoring. The system employs a NodeMCU ESP8266 microcontroller for data acquisition and wireless communication. Sensor data are processed using the Mamdani Fuzzy Inference System (FIS) to classify soil fertility into three categories: fertile, moderately fertile, and infertile. The developed system displays monitoring results locally through an OLED display and remotely through Google Spreadsheet integration for real-time observation. Sensor calibration and field testing were conducted using several soil samples with different moisture and color characteristics. Experimental results showed that the soil moisture sensor achieved an average error rate of 1.57%, indicating good measurement accuracy. Furthermore, the fuzzy-based classification successfully identified soil fertility levels according to the measured parameters. The integration of IoT technology and fuzzy logic provides an effective low-cost solution for precision agriculture applications, particularly for small-scale farming environments. The proposed system is expected to assist farmers in monitoring soil conditions more efficiently, accurately, and continuously.
IoT Implementation in SMEs: A Review and a Proposed Framework for Smart Manufacturing Adoption
Small and Medium Enterprises (SMEs) play an important role in economic growth; however, many SMEs still face challenges related to productivity, operational efficiency, and technological adoption. The development of Internet of Things (IoT) technologies provides opportunities for SMEs to improve operational visibility and automation toward smart manufacturing systems. This study aims to develop a simplified IoT-based smart manufacturing framework for SMEs and validate the framework through laboratory-scale implementation. The research employed a qualitative and experimental approach consisting of literature analysis, framework development, laboratory-scale implementation, and system evaluation. The literature analysis identified that most IoT implementations in SMEs focus on real-time monitoring and basic automatic control using low-cost technologies such as sensors, microcontrollers, and wireless communication platforms. Based on these findings, a five-layer framework consisting of the Data Acquisition Layer, Monitoring Layer, Control Layer, Integration Layer, and Smart Decision Layer was proposed. To validate the framework, a laboratory-scale IoT monitoring and control system was developed using NodeMCU ESP8266, DHT11 sensor, MQ-series gas sensor, ultrasonic sensor, relay module, and IoT dashboard platform. The developed system successfully demonstrated real-time monitoring and automatic control capabilities under laboratory conditions. The results indicate that affordable IoT technologies are feasible for supporting the gradual adoption of smart manufacturing systems in SMEs. The proposed framework provides practical guidance for SMEs to progressively adopt IoT technologies according to their operational capabilities, with future integration of AI/ML-based decision support envisioned to enhance predictive and autonomous functionalities.
Designing Wheeled Robot with Rocker Bogie System for Precision Farming
This study presents the development of a small autonomous robot designed to support precision farming through improved mobility and control efficiency. The robot employs a Rocker-Bogie suspension system to maintain stability and traction across varied terrain, ensuring even load distribution. Six DC motors are controlled using a discrete PID system optimized through MATLAB simulations to achieve precise speed and position control. By integrating mechanical design and control optimization, the proposed system enhances operational efficiency, reduces environmental impact, and contributes to sustainable and intelligent agricultural practices.
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A Comparative Study of Data-Driven Control Tuning: VRFT and FRIT for DC Motor Speed Regulation
This study compares two data-driven control tuning methods Virtual Reference Feedback Tuning (VRFT), and Fictitious Reference Iterative Tuning (FRIT) applied to a DC motor speed control system. Both methods aim to achieve a predefined closed-loop behaviour without explicit plant modelling, relying instead on measured input–output data. For the VRFT method, single-shot open-loop data were collected using a PRBS signal to excite the DC motor, while FRIT used a single-shot closed-loop experiment under an initial PI controller. Each method used the same reference model, a first-order system with a 2 second time constant, to guide the tuning process. The VRFT approach produced a 6-DOF controller through least-squares optimization, whereas the FRIT method refined the parameters of a PI controller by minimizing a defined cost function. Simulations conducted at target speeds of 60 and 100 RPM demonstrated that both controllers delivered comparable tracking performance, despite having different structural designs. These findings confirm that both VRFT and FRIT can generate effective control strategies from limited data, providing design flexibility while still achieving the desired closed-loop behaviour.
Performance Analysis of Dijkstra and A-star Algorithm in Maritime Navigation Pathfinding
This study explores various algorithms and methodologies for path planning and decision-making in diverse environments, including maritime distribution and logistics. The research highlights the importance of COLREG rules in designing collision avoidance algorithms for Maritime Autonomous Surface Ships (MASS), emphasizing the need for algorithms that adapt to specific maritime parameters. A Multiple-Criteria Decision-Making (MCDM) approach combined with Dijkstra’s algorithm is presented to optimize route distribution in logistics, taking into account parameters such as cost, distance, congestion, and risk. Experimental path planning methods using A-star and Dijkstra algorithms are discussed for navigating slag disposal sites that emit natural radiation, demonstrating the adaptability of these algorithms in hazardous environments. The study also investigates dynamic path planning algorithms, such as DAA-Star, which incorporate time and risk cost factors to enhance the safety and efficiency of navigation. Integrating various algorithms and considering specific environmental parameters can significantly improve path planning and decision-making processes in maritime and logistics contexts.
Multiple-Criteria Decision Making (MCDM)
Study of the Effect of the Use of Series Reactive Power Compensators on the Increase in Inductive Load Power Factor with Magnetic Energy Recovery Switches in Household Environments
The use of inductive loads in modern household electrical installations is increasing, particularly in multi-story homes equipped with elevators, water pump drive motors, and generators. Such inductive loads lead to a decrease in power factor due to the dominance of reactive power, which negatively affects the efficiency and cost of electricity consumption. This study aims to improve the power factor in a three-story residential electrical system by implementing a reactive compensation method using a Magnetic Energy Recovery Switch (MERS) circuit. The system design and analysis are based on active power data obtained through the Autodesk Revit 2024 application, with load parameters sourced from the F-H05 elevator, Grundfos pump motor, and Weichai Power generator. Simulation was carried out using PSIM software to determine the optimal capacitor value and triggering angle for the IRF820 MOSFET. The simulation results show that the application of MERS significantly improves the power factor, making the system more efficient and cost-effective.
Design and Implementation of a Digital PID Controller for DC–DC Buck Converter with MATLAB
This study focuses on the design and implementation of a digital Proportional-Integral-Derivative (PID) controller for a DC-DC buck converter using MATLAB. The main issue addressed is the need for better voltage regulation in power electronics applications, which is critical to maintaining system stability and performance. The objective of this study is to develop a robust control strategy that minimizes output overvoltage and improves accuracy under varying load conditions. The methodology used involves the development of a discrete-time PID controller, which is simulated in MATLAB to analyze its performance. The results in MATLAB show that the digital PID controller successfully limits overvoltage and maintains output voltage stability, even with significant load variations. Further experimental validation confirms the simulation results, demonstrating the controller’s ability to adapt to real-world conditions. The successful implementation and validation of the controller using MATLAB underscores its potential to improve the efficiency and reliability of power conversion systems.
Mamdani Fuzzy Logic-Based Room Temperature Monitoring and Control System
Automatic room temperature control is a crucial need for maintaining comfort and energy efficiency, especially in enclosed environments such as offices, laboratories, and homes. This research aims to design and develop a room temperature monitoring and control system based on Mamdani fuzzy logic. The system utilizes a temperature sensor to monitor real-time environmental conditions and actuators (fans or heaters) as outputs to adjust the temperature. The Mamdani fuzzy logic method is chosen for its ability to handle uncertainty and provide decisions that resemble human reasoning. Test results show that the system can maintain the room temperature within the desired range responsively and efficiently. By implementing this system, thermal comfort can be achieved automatically without manual intervention, while also supporting energy savings in the operation of cooling and heating devices.
Analysis of Energy-Saving Opportunities at SMKN 3 Kuningan
This research presents the results of an energy audit at SMKN 3 Kuningan, which aims to identify energy saving opportunities in the school's electrical system. The audit focused on two main energy consumption systems, lighting and air conditioning (AC). The analysis was conducted based on electricity consumption data and Energy Consumption Intensity (IKE) standards. The results show that the lighting system still uses conventional TL and incandescent lamps, which waste energy. Replacing with energy-saving LED lamps can save around 571 kWh per month. In addition, the AC units used mainly have low efficiency. Replacing with energy-saving AC and adjusting operating hours results in additional savings of 5,718.29 kWh per month. Overall, the school has the potential to reduce electricity consumption by 6,289.29 kWh per month. The implementation of this recommendation is also able to reduce the IKE value from 105,321 kWh/m²/year to 103,924 kWh/m²/year, closer to the national efficiency standard according to SNI 03-6197-2000 and the regulations of the Ministry of Energy and Mineral Resources. This research emphasizes the importance of regular energy audits in educational institutions as a strategic step in operational cost efficiency and contribution to environmental sustainability.
The Effect of AI-Generated Content on Brand Identity Consistency in Social Media: A Systematic Literature Review
The consistency of brand identity on social media platforms is pivotal for fostering customer recognition, trust, and engagement. This research endeavor aims to investigate the impact of AI-generated content on the consistency of brand identity through the application of a systematic literature review methodology. The research methodology adheres to the PRISMA 2020 guidelines in order to identify, screen, and analyze pertinent scholarly articles. This inquiry poses three primary questions: in what manner does the utilization of AI in the creation of visual content influence the consistency of brand identity on social media; how does the consistency of brand identity produced by AI-generated content compare to that of content created through traditional methods; and what strategies can be implemented to ensure visual aesthetic coherence when employing AI-generated content in branding efforts? The findings indicate that although AI possesses the capacity to enhance efficiency and produce high-quality content, challenges pertaining to authenticity and consumer perception persist. Consequently, it is imperative for brands to adopt an ethical and transparent methodology concerning the deployment of AI technologies. This study advocates for companies to take a more proactive stance in incorporating human oversight and relevant instruments to guarantee that AI-generated content remains consistent, authentic, and aligned with the fundamental values of the brand.
Social Media Dynamics: Twitter Users’ Responses to the Presence of Naturalized Players in Indonesia's National Football Team
Public discourse on the naturalization of players in Indonesia’s national football team continues to grow, with social media platforms such as Twitter becoming primary outlets for expressing opinions. This study employs advanced sentiment analysis techniques, utilizing IndoBERT—a deep learning model specifically designed for the Indonesian language—to analyze the sentiments of Twitter users. The analytical process includes data preprocessing, sentiment distribution visualization, and model performance evaluation. Findings reveal that IndoBERT captures public sentiments more comprehensively and accurately than conventional sentiment analysis models. Sentiment polarity is significantly influenced by factors such as player performance, public expectations, and media narratives. This study offers practical insights for policymakers in Indonesian football to support data-driven strategic decision-making. Furthermore, it underscores the value of natural language processing (NLP) and sentiment analysis in understanding complex socio-cultural dynamics in the digital era.
Design and Fabrication of an Automatic Pet Food Dispenser
This study presents the design and fabrication of an automatic pet food dispenser system, aimed at providing controlled, precise, and timely feeding for pets. The system utilizes a 12V DC motor, a load cell for weight measurement, a 4x4 keypad for user input, an LCD for real-time feedback, and a real-time clock (RTC) for scheduling. The screw feeder mechanism driven by the DC motor ensures controlled dispensing, halting automatically when the load cell confirms the desired weight. An emergency function allows feeding outside regular intervals. Tests with two pet food types (small and large kibbles) showed a ±10% margin of error, demonstrating reliable functionality. This solution offers practical, energy- efficient automation for pet care, with potential for enhancements such as IoT integration and advanced power management.
Design and Simulation of a Hybrid Solar and Wind Power Plant System in South Garut Using HOMER Software
The increasing demand for sustainable energy highlights the urgency of integrating renewable energy sources into power generation systems. South Garut, Indonesia, offers significant potential for solar and wind energy, particularly in the coastal area of Cibalong District. This study aims to design and simulate a hybrid solar and wind power plant system optimized using the HOMER software. Field observations, energy consumption data collection, and analysis of local renewable energy resources were conducted to establish the system parameters. The HOMER software was utilized to perform simulation, optimization, and sensitivity analysis, considering factors such as Net Present Cost (NPC), Cost of Energy (COE), Return on Investment (ROI), and Internal Rate of Return (IRR). The simulation results indicate that integrating photovoltaic panels and wind turbines within an on-grid hybrid configuration significantly enhances system reliability and cost-efficiency. The optimal system configuration achieves a competitive COE while ensuring long-term energy sustainability. This research provides a reference model for the development of renewable energy-based power systems in remote coastal areas, supporting national initiatives to increase the share of renewable energy in Indonesia’s energy mix.
Cage Environment Monitoring System in Modern Livestock: A Literature Review
Livestock in Indonesia is one of the sectors that can have a considerable economic impact. However, livestock in Indonesia still implements conventional livestock models so that livestock in Indonesia can still be developed into modern livestock, one of the things that can improve the Indonesian livestock system is through a monitoring system or cage monitoring. Therefore, the author conducted a literature review to find out how the cage monitoring system on the livestock can work. Based on the study conducted by the author, the author can conclude that temperature, humidity, air quality, water quality and livestock movement are the parameters controlled by most researchers. These various parameters can be monitored so that the system can control gas levels, temperature & humidity. With this monitoring system, researchers believe that it can increase the level of effectiveness in terms of cost and time so that it can also indirectly increase the economic value of the livestock sector.
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Implementation of Artificial Intelligence in Energy Exploration and Management: A Literature Review
This research examines the utilization of artificial intelligence (AI) in the management and exploration of wind energy potential. Three main areas are discussed: 1) Household Energy Management, where AI-based fuzzy logic systems have proven effective in optimizing the use of electrical appliances and reducing energy consumption, 2) Power Generation Energy Management, where artificial neural network (ANN)-based prediction models are capable of accurately estimating fluctuations in electricity demand to enable better supply planning, and 3) Energy Potential Prediction, where AI algorithms such as Backpropagation Neural Network (BPNN) can predict wind speed with a high degree of accuracy, enabling more reliable estimation of the potential for wind power generation. Overall, this research demonstrates that the integration of artificial intelligence technology has great potential in enhancing energy efficiency and management in the future.
Optimizing Energy-Efficient Home Electrical Systems through Capacitor Integration to Improve Future Energy Efficiency
This research discusses the optimization of energy-efficient home electrical systems through the integration of capacitors to improve future energy efficiency. The main objective is to analyze the impact of installing power capacitors in parallel with electrical loads such as fans, refrigerators, and computers to improve power factor and reduce energy consumption. An experimental approach is used, installing capacitors with different values (2μF, 6μF, 8μF) on the test loads and measuring parameters such as voltage, current, power factor, and active power. The results show that the installation of optimal capacitors (e.g., 2μF for fans, 8μF for refrigerators) significantly improves the power factor, from around 0.55-0.61 without capacitors to near unity with capacitors. This power factor improvement reduces the current flowing through the system, leading to lower active power losses and increased energy efficiency. For example, the fan current is reduced from 0.197A to 0.109A with a 2μF capacitor. The active power consumption also decreased for some loads, such as fans experiencing a 4.8% reduction, indicating energy savings. The capacitor integration provides economic benefits through reduced electricity costs and environmental benefits by lowering carbon emissions from reduced electricity generation. The key is to carefully select the right capacitor size to avoid over-compensation, requiring an analysis of the reactive power requirements for each load.
Feasibility Analysis of Lightning Grounding System at SAMSAT Soekarno-Hatta Building Bandung City Based on PUIL Standard
Protection from surges caused by lightning is an important concern in public buildings, including in SAMSAT Soekarno-Hatta Building, Bandung City, which functions as a service center with high electricity consumption and various sensitive electronic equipment. An adequate grounding system is required to conduct excess current to ground to protect electronic devices and ensure occupant safety. This study aims to evaluate the feasibility of the lightning grounding system at the Bandung SAMSAT Building based on the PUIL 2000 safety standard, which stipulates that the grounding resistance value must be below 5 ohms to achieve optimal protection. The grounding resistance measurement is carried out using an earth tester, which is able to measure the actual resistance of the building grounding system. Based on the measurement results, the resistance value obtained is 4.44 ohms. This value meets the requirements of PUIL 2000 and indicates that the grounding system in the building is within safe limits. In addition to resistance measurements, visual observations were also made to ensure the physical condition of grounding components, such as lightning rods and connecting conductors, which were found to be in good condition although there were signs of light corrosion on the conductors. The conclusion of this research shows that the grounding system in the Soekarno-Hatta SAMSAT Building meets the eligibility standards for lightning protection. This has positive implications for the protection of buildings and electronic equipment from the risk of lightning strikes. The suggested recommendation is routine maintenance to maintain the performance of the grounding system, especially in the area of corroded conductors, so that the resistance value remains within safe limits.
Air Quality Classification System using Random Forest Algorithm using MQ-7 and MQ-135 Sensors with IoT-based
Air is one of the essential elements in human life besides water and soil. However, currently the air quality in Indonesia is getting worse. Therefore, an air quality classification system using Random Forest algorithm based on CO and CO2 levels using IoT-based MQ7 and MQ13 sensors is needed as a smart solution. The workflow of this system begins with detecting air quality using the MQ-7 sensor for CO gas and the MQ-135 sensor for CO2 gas. Then, the classification process is carried out with the Machine Learning Random Forest algorithm by utilizing a number of training data that has been stored in the program to classify the gas sensor detection results into three types of classes, namely “Good”, “Bad”, or “Toxic”. The final output of this system is a website display that can be accessed on a PC/Laptop monitor in real time. From the results of the Random Forest machine learning algorithm classification testing process, 1 unsuitable data was found from a total of 100 trials that have been carried out. Therefore, the Random Forest machine learning algorithm can be said to be successful in detecting air levels in the surrounding environment well because it provides an accuracy value of 99%.
Prototype of Monitoring System for Lithium-Ion Battery Charging and Discharging on UPS Using Atmega 328P
An Uninterruptible Power Supplies (UPS), is required as a backup source of power in a railroad signalling power supply system. The battery is an essential part of a UPS. The battery functions to provide or supply electrical energy without having to be connected to electricity. Therefore, it is necessary to monitor battery conditions such as state of charge (SOC) to check battery capacity and depth of discharge (DOD) to calculate lost battery capacity. This research made a prototype of a battery monitoring system using IC ATMega328p as a microcontroller that will monitor the condition of the battery. Connecting the battery with the voltage divider circuit, the current sensor ACS712 as a current value reader at the time of charge and discharge. The results of the study showed that for the process of charging a new battery for 2 hours and 30 minutes using a 12 volt DC adapter with a current of 1.5 amperes, the current generated when the battery voltage reaches a maximum of 12,01 volts is 2,824 amperes and SOC 100%. Whereas the old battery filling took 1 hour and 30 minutes with a maximum battery voltage of 12,01 volts, 2,032 amperes, and a SOC of 100%. Whereas when the process is carried out using a battery discharge using a motor load of DC 10 volts, the time required for the discharging of the new battery is 14 hours and 30 minutes, with the current produced when the voltage of the battery reaches the minimum voltage of 8,98 volts, or 2,138 ampers, and DOD 0%.
Forecasting Electrical Energy Loads at PT Krakatau Daya Electric Using the Linear Regression Method
The importance of the role of electrical energy at this time cannot be denied and it is difficult to imagine how life would be without electricity, not only as a source of light at night in Cilegon City because it is rich in resources, especially in the industrial sector. Therefore, the existence of a guaranteed power supply is very important. PT Krakatau Daya Listrik, as the main provider and distributor of electrical energy in the KIEC Area (Krakatau Industrial Estate Cilegon), indirectly becomes the backbone for the economy of the people in the trading area of PT Krakatau Daya Listrik. The method used in making predictions is the linear regression method which is a method to test how accurate the relationship between x and y is. In addition, to do forecasting or similarity testing, use Google Colab. The results of the two show a correlation coefficient of 0.4 which is enough to have a relationship between x and y, the more years the more power or electrical energy is needed. This is very relevant considering that electrical energy has become a necessity, so this forecast can help electricity service providers meet consumer needs.
Performance Improvement of Hydraulic Excavator Efficiency: A Literature Review
<span style="font-size: 9.0pt; mso-bidi-font-size: 10.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-ID; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Excavators dominate heavy-duty jobs worldwide. As a major construction machine, their enhanced productivity in work has led to a strong demand for them. Concerns for the environment, increased efficiency, and energy conservation all reflect this goal. Several studies on these matters Automation in construction equipment, particularly hydraulic excavators, has gained popularity among producers and academics. This article examines articles about environmental concerns, efficiency enhancements, and energy (storage and evolving) issues in hydraulic excavators from a number of databases. This article reviews the technology of hydraulic excavators, covering their performance, related components, energy use, efficiency, and future opportunities. Research questions addressed include: How do hydraulic excavators work, what are the components, what is the role of maintenance in maintaining the performance of hydraulic excavator systems, what are the latest innovations in development for hydraulic excavator systems that can improve efficiency and reliability, and how can new technologies help reduce the impact on the environment. The method used to answer the research questions is SLR. The results of this article illustrate that excavator efficiency and performance depend on the architecture of the component layout, technology, systems and operational machinery used. The energy regeneration system serves to capture and store the potential energy generated during excavator operation. This stored energy can be reused to help power the hydraulic system, reducing the need for additional energy input.
Comparison of Naïve Bayes Classifier and Support Vector Machine Methods for Sentiment Classification of Responses to Bullying Cases on Twitter
The rapid dissemination of information related to the K-Pop world, facilitated by social media, has made it easier to follow developments and controversies. One notable case that sparked extensive discussion on Twitter was the bullying allegations against Kim Garam of LE SSERAFIM. Researchers, using Twitter data, sought to analyze Indonesian public sentiment regarding this case through sentiment analysis, which classifies opinions as positive or negative. For processing textual data, text mining methods, particularly classification techniques, are employed. Two popular algorithms in text mining are the Naive Bayes classifier and the support vector machine (SVM). The Naive Bayes classifier is favored for its speed, simplicity, and high accuracy, while the SVM excels at identifying a hyperplane that maximizes the margin between classes. In this study, sentiment classification results were labeled as either positive or negative. The comparison between the Naive Bayes classifier and SVM for classifying responses to Kim Garam's bullying case on Twitter showed high accuracy rates: 93% for Naive Bayes and 97% for SVM. The higher accuracy of the SVM algorithm indicates its superiority over the Naive Bayes classifier in this context.
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Analyzing Loss Components in DC Generator for Wind Turbine Applications
Direct current (DC) generators have gained attention for their simplicity and reliability in wind turbine applications. However, DC generators are subject to energy losses, including ohmic, magnetic core, and mechanical losses, which impact system performance. This research investigates these losses in DC generators used in wind turbines through experimental testing. The methodology integrates laboratory experiments with simulations to quantify and understand loss mechanisms. Findings offer insights for optimizing wind turbine efficiency and reliability. Results show significant total power loss of 99%, emphasizing the need for meticulous loss assessments. Friction, copper, and iron loss coefficients are analyzed with the value of 0.217 and 0.709, respectively. However, with the rise in input voltage levels, a noticeable pattern becomes evident, where the significance of iron loss decreases relative to other influencing factors. Understanding these dynamics is crucial for enhancing wind turbine performance and advancing sustainable energy solutions.