cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Diky Zakaria
Contact Email
dikyzak@upi.edu
Phone
+6281321439833
Journal Mail Official
jmai@upi.edu
Editorial Address
Jl. Veteran No. 8 Kabupaten Purwakarta Jawa Barat, 41115
Location
Kota bandung,
Jawa barat
INDONESIA
Journal of Mechatronics and Artificial Intelligence
Published by Universitas Pendidikan Indonesia
ISSN : 3062729X     EISSN : 30484227     DOI : https://doi.org/10.17509/jmai.v1i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering
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 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 and Theoretical 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.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 10 Documents
 1 
Implementation of Artificial Intelligence in Energy Exploration and Management: A Literature Review Nur Elah
Journal of Mechatronics and Artificial Intelligence Vol 1, No 2 (2024): JMAI: December 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i2.76906

Abstract

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
Forecasting Electrical Energy Loads at PT Krakatau Daya Electric Using the Linear Regression Method Krisna Bayu; Dhea Rahmalia Henidar; Fahmi Hermastiandi; Galih Prasetya; Adi Nugraha
Journal of Mechatronics and Artificial Intelligence Vol 1, No 1 (2024): JMAI: June 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i1.69977

Abstract

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 to meet consumer needs.
Cage Environment Monitoring System in Modern Livestock: A Literature Review Yohanes Adi Nugroho; Muhamad Fajar Imanul Haq; Reyhan Praditya Bagaskara; Diky Zakaria
Journal of Mechatronics and Artificial Intelligence Vol 1, No 2 (2024): JMAI: December 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i2.77243

Abstract

Livestock in Indonesia is one of the sectors that can have a considerableeconomic impact. However, livestock in Indonesia still implementsconventional livestock models so that livestock in Indonesia can still bedeveloped into modern livestock, one of the things that can improve theIndonesian livestock system is through a monitoring system or cagemonitoring. Therefore, the author conducted a literature review to find outhow the cage monitoring system on the livestock can work. Based on thestudy conducted by the author, the author can conclude that temperature,humidity, air quality, water quality and livestock movement are theparameters controlled by most researchers. These various parameters canbe monitored so that the system can control gas levels, temperature humidity. With this monitoring system, researchers believe that it canincrease the level of effectiveness in terms of cost and time so that it canalso indirectly increase the economic value of the livestock sector.
Prototype of Monitoring System for Lithium-Ion Battery Charging and Discharging on UPS Using Atmega 328p Dedi Riswandi; Andriana Andriana; Irvan Budiawan
Journal of Mechatronics and Artificial Intelligence Vol 1, No 1 (2024): JMAI: June 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i1.69861

Abstract

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%.
Air Quality Classification System using Random Forest Algorithm using MQ-7 and MQ-135 Sensors with IoT-based Aisyah Aira Putri Maharani; Rizky Hamdani Sakti; Muhamad Fajar Imanul Haq; Muhamad Ajis; Abdu Malikh Silaban
Journal of Mechatronics and Artificial Intelligence Vol 1, No 2 (2024): JMAI: December 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i2.75591

Abstract

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%.
Analyzing Loss Components in DC Generator for Wind Turbine Applications Elysa Nensy Irawan; Kai Shibuya; Nguyen Hong Minh Giang; Rizky Hamdani Sakti
Journal of Mechatronics and Artificial Intelligence Vol 1, No 1 (2024): JMAI: June 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i1.69864

Abstract

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.
Feasibility Analysis of Lightning Grounding System at SAMSAT Soekarno-Hatta Building Bandung City Based on PUIL Standard Mochmamad Raihan Febriana; Linda Faridah
Journal of Mechatronics and Artificial Intelligence Vol 1, No 2 (2024): JMAI: December 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i2.76024

Abstract

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.
Comparison of Naïve Bayes Classifier and Support Vector Machine Methods for Sentiment Classification of Responses to Bullying Cases on Twitter Firda Millennianita; Ummi Athiyah; Arif Wirawan Muhammad
Journal of Mechatronics and Artificial Intelligence Vol 1, No 1 (2024): JMAI: June 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i1.69959

Abstract

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.
Optimizing Energy-Efficient Home Electrical Systems through Capacitor Integration to Improve Future Energy Efficiency Adi Nugraha; Felycia Felycia
Journal of Mechatronics and Artificial Intelligence Vol 1, No 2 (2024): JMAI: December 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i2.76571

Abstract

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
Performance Improvement of Hydraulic Excavator Efficiency: A Literature Review Geralda Livia Nugraha; Muhamad Ajis; Himmawan Sapta Adhi; Diky Zakaria
Journal of Mechatronics and Artificial Intelligence Vol 1, No 1 (2024): JMAI: June 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i1.69947

Abstract

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. 

Page 1 of 1 | Total Record : 10

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 1, No 2 (2024): JMAI: December 2024 Vol 1, No 1 (2024): JMAI: June 2024