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31,456 Article Results

Demographic factors affecting technological pedagogical content knowledge in Generation-Z preservice biology teachers

10.11591/ijere.v13i6.30355
Ahmad Kamal Sudrajat , Ibrohim Ibrohim , Herawati Susilo
Generation-Z dominating preservice biology teachers in this era. Teachers’ TPACK is influenced by factors such as demography. Technological, pedagogical and content knowledge (TPACK) is a framework used in teacher competence in Indonesia. This research will contribute to the training process of Generation-Z preservice biology teachers who have special characteristics. This study aims to determine the effect of demographic factors on the TPACK of Generation-Z preservice biology teachers. A total of 652 Generation Z preservice biology teachers from biggest ten Indonesian educational universities participated in this survey. The research results show that only gender has a relationship with TPACK in every aspect. Motivation to become a teacher influences TPACK, except for the technological knowledge (TK) aspect. In contrast, other aspects do not influence TPACK. Gender and motivation to become a teacher are factors that need attention in the training process for prospective biology teachers. Universities and the government can use these results to formulate suitable training programs for preservice biology teachers in Indonesia.
Volume: 13
Issue: 6
Page: 3796-3805
Publish at: 2024-12-01

Seeding precision: a mask region based convolutional neural networks classification approach for the classification of paddy seeds

10.11591/ijai.v13.i4.pp4138-4146
Rajashree Nambiar , Ranjith Bhat , Varuna Kumara
The generation of sufficient training data that is accurately labelled for a deep neural network involves a significant amount of effort and frequently constitutes a bottleneck in the implementation process. For the purpose of this research, we are training a neural network model to perform instance segmentation and classification of crop seeds for various rice cultivars. Synthetically constructed dataset is used here. The concept of domain randomization, which offers a productive alternative to the laborious process of data annotation, serves as the basis for our methodology. We make use of the domain randomization technique in order to produce synthetic data, and the mask region-based convolutional neural network (Mask R-CNN) architecture is utilized in order to train our neural network models. A cultivar name is used to designate the seeds, and they are differentiated from one another using colors that are comparable to those used in the actual dataset of paddy cultivars. Our mission focuses on the identification and categorization of rice paddy varieties within automatically generated photographs. Farmers are able to accurately sort crop seeds from a variety of rice cultivars with the use of this approach, which is particularly useful for phenotyping and optimizing yields in laboratory settings.
Volume: 13
Issue: 4
Page: 4138-4146
Publish at: 2024-12-01

Performance analysis of field oriented controlled PMSM based electric truck

10.11591/ijpeds.v15.i4.pp2027-2036
Sanskruti Achary , Rutul Chaudhari , Siddharthsingh K. Chauhan
The electric vehicle (EV) concept is considered the ideal solution to save the environment from pollution occurring due to internal combustion engines or ICE-based vehicles. However, electric power trains have found more penetration in the segment of passenger vehicles and electric buses. This paper presents the performance of an electric truck. Field-oriented controlled permanent magnet synchronous motor (PMSM) is used for the powertrain of the proposed electric truck. The performance of the proposed electric truck is analyzed for propulsion as well as regenerative mode of operation using MATLAB. The effect of different gradient conditions of the road surface on the behavior of the proposed truck is observed. The presented simulation results depict the satisfactory operation of the proposed PMSM-driven electric truck for various operating conditions.
Volume: 15
Issue: 4
Page: 2027-2036
Publish at: 2024-12-01

Power quality improvement using PV fed series active power filter with enhanced Jaya optimized PI controller

10.11591/ijpeds.v15.i4.pp2199-2213
Ravikanth Mallajoshula , I. E. S. Naidu
In this paper, improvement of power quality (PQ) in a distribution system using a PV-series active power filter (PV-SAPF) investigated. The performance of the proposed filter is increased using an enhanced Jaya optimized proportional integral (PI) with indirect current controller (ICC) technique system. In existing Jaya optimization technique best and worst cases were obtained from the whole solutions which degrades the output of the PI controller results more iteration. In case of enhanced Jaya, the proposed one, population from best to worst is shorted first and divide them as two groups best and worst solutions. The best solution is chosen from the best solution group as “Best,” and the best solution from the worst solution group is chosen as “Worst”. This procedure decreases the number of iterations needed to reach the optimal solution. The effectiveness of the enhanced Jaya optimization and ICC developed controller is implemented in the proposed PV-SAPF. To proof its effectiveness different types of loads are taken as case study. MATLAB/Simulink platform is used to do the above research work.
Volume: 15
Issue: 4
Page: 2199-2213
Publish at: 2024-12-01

A holistic approach of stability using material parameters of manipulators

10.11591/ijra.v13i4.pp380-390
Shabnom Mustary , Mohammod Abul Kashem , Mohammad Asaduzzaman Chowdhury , Jia Uddin
The demand for a comprehensive method to assess stability using manipulator material parameters is high. Various material parameters, such as the Young modulus, which represents stiffness, damping, and deflection, influence the material of the robot manipulator. The correlation between robot stability and these characteristics remains unclear, as prior studies have not yet examined the collective impact of these parameters on robot manipulators. This work considers two sophisticated manipulators, namely ABB and FANUC. The main objective of this research is to construct a stability model that considers the material properties of stiffness, damping, and deflection to assess the manipulator’s stability level, which may be categorized as low, medium, or high. Furthermore, the presented stability model examines and employs numerous modified and conventional formulas for material properties to determine the level of stability. The findings show that stiffness significantly influences the stability of robot manipulators, a relationship that applies to all the examined manipulators. We also emphasize that the choice of manipulator materials significantly impacts stability maintenance. These findings are expected to enhance the design and advancement of novel robot manipulators within the industry.
Volume: 13
Issue: 4
Page: 380-390
Publish at: 2024-12-01

Accuracy based-stacked ensemble learning model for the prediction of coronary heart disease

10.11591/ijai.v13.i4.pp4516-4525
Santosini Bhutia , Bichitrananda Patra , Mitrabinda Ray
Coronary heart disease (CHD) is the primary cause of silent and noncommunicable deaths. Early detection is essential for slowing the progression of death and saving lives. Medical researchers use machine learning techniques to predict CHD. This article proposes an accuracy based-stacked ensemble learning (AB-SEL) model to predict CHD while minimizing computational time (CT). The dataset undergoes the logistic regression recursive feature elimination (LR-RFE) method to identify the important features. The three strong classifiers, logistic regression (LR), random forest (RF), and AdaBoost, are chosen to build ensemble machine-learning models, including techniques like bagging, majority voting, and stacking, for the Cleveland dataset accessible from Kaggle. Data scaling was done using the normal scalar method, and hyperparameter optimization was done using random search and grid search. Effectiveness is measured by accuracy, precision, recall, F1 score, and CT is validated through 5-fold cross-validation. The suggested approach achieved a 90.16% accuracy rate, required only 0.2 seconds of CT, and yielded an area under the curve (AUC) of 0.892.
Volume: 13
Issue: 4
Page: 4516-4525
Publish at: 2024-12-01

Innovative machine learning approaches for prediction of hypoglycemia in patients with type 2 diabetes

10.11591/ijai.v13.i4.pp4453-4471
Sachin Ramnath Gaikwad , Seeta Devi , Sameer Shekhar , Dipali Dumbre
Medical data science advances using machine learning, which predicts glucose levels. A supervised machine learning technique is employed in which regression and classification methods are used to check the prediction performance. The unsupervised machine learning technique makes clusters based on variables' similarities. Furthermore, the prediction accuracy of conventional machine learning techniques is improved by proposing a transfer learning technique. Based on a median value of 67 mg/dL, the data set is divided into two groups: group 1 (BSL 57 mg/dL to 67 mg/dL) has 50.67% of the samples, and group 2 (with BSL 68 mg/dL to 79 mg/dL) has 49.33% of the samples. In regression analysis, 5-fold cross-validation is performed. The decision tree (DT) and gradient boosting (GB) individually provide a prediction accuracy of 18.2%. Regarding classification analysis, a 10-fold cross-validation configuration is used for training and testing the model. AdaBoost, GB, random forest, and neural network achieve an accuracy rate of 66.3% and an area under curve (AUC) score of 0.731. In unsupervised learning, the datasets are divided into three clusters. The clustering result is used in regression and classification models using transfer learning. The accuracy and precision of the AdaBoost and GB are as follows: 69.6%, 0.696 with f1 0.661 and 69.6%, 0.708 with f1 0.708, respectively.
Volume: 13
Issue: 4
Page: 4453-4471
Publish at: 2024-12-01

Low-voltage DC-DC off-grid PV system: various irradiance studies

10.11591/ijpeds.v15.i4.pp2651-2659
A. Razi , Syahar Azalia Ab Shukor , Wei Wen Lee , M. Nabil Hidayat
The off-grid photovoltaic (OGPV) system, also referred to as a stand-alone power system, generates electricity from solar energy independently of the main electricity grid. However, photovoltaic (PV) modules face limitations in consistently providing the necessary voltage. Thus, a DC-DC converter is employed to adjust the voltage to match the requirements of the load. This paper proposes a detailed analysis of irradiance patterns resembling in-lab and real-world conditions to address the challenge of selecting the optimal DC-DC OGPV system capable of functioning effectively under standard test condition (STC) and dynamic test condition (DTC), respectively. Three proposed irradiance analyses, tailored to different environmental scenarios, aim to identify output performances for DC-DC OGPV system which are aligning with standards set by the International Electrotechnical Commission (IEC) 61727. The DC-DC OGPV system is simulated using MATLAB Simulink. Furthermore, comparative studies involving different PV module types and several switching techniques (pulse generator and maximum power point tracking or MPPT implementations), where findings are instrumental in advancing the development and projection planning for medium to high-voltage OGPV systems, contributing to the global initiative for accessible and sustainable energy solutions aligned with sustainable development goal (SDG 7).
Volume: 15
Issue: 4
Page: 2651-2659
Publish at: 2024-12-01

Fuzzy logic assessment of X-ray tube risks in robotic C-arm angiography: a failure mode and effect analysis study

10.11591/ijra.v13i4.pp506-514
Ade Firdaus , Andi Adriansyah , Nanda Ferdana , Rahmalisa Suhartina , Rino Ferdian Surakusumah , Jakfat Haekal , Zulhamidi Zulhamidi , Abu Ubaidillah Shamsudin
This research examines the integration of robotic C-arm technology in angiography, a critical tool for treating cardiac conditions. The robotic C-arm, which includes an X-ray tube, is essential for scanning patients during procedures. The study also investigates the associated risks, specifically in Indonesian hospitals with cardiac facilities. Angiography is used to diagnose and treat heart disease by visualizing blood vessels and facilitating catheterization procedures. However, its mobility poses hazards and can impact the process. To address potential risks, failure mode and effect analysis (FMEA) is utilized. Traditionally, risk assessment using risk priority numbers (RPN) is conducted, but these may not accurately reflect failures due to complex evaluating processes. To overcome this limitation, fuzzy logic is employed, enhancing risk assessment accuracy. Through this approach, twenty-seven failure modes are identified across two brands, with ten major ones prioritized using fuzzy logic. These findings facilitate the development of preventive measures to mitigate future failures and enhance patient safety during angiography in hospitals. In conclusion, the study underscores the importance of robust risk management in medical equipment, particularly in dynamic environments. By integrating fuzzy logic into risk assessment, the study improves prioritization accuracy, enabling effective allocation of resources for preventive actions.
Volume: 13
Issue: 4
Page: 506-514
Publish at: 2024-12-01

The LLCL filter-based synchronverter with adaptive fuzzy logic controller

10.11591/ijpeds.v15.i4.pp2115-2127
Tan Thien Tran , Quoc Dung Phan , Bao Anh Nguyen , Weng Kean Yew
Global warming has emerged as a significant issue, primarily due to the use of fossil fuels for power generation. Renewable energy adoption has surged. However, stability issues arise when integrating the primary source and inertia-less nature of converter-interfaced renewable energy sources (RESs) into the traditional power system, due to its intermittent and uncertain nature. The proposed solution involves controlling converter-interfaced RES to emulate the characteristics of a conventional synchronous generator (SG), commonly known as a virtual synchronous generator or synchronverter. As it fundamentally functions as a power converter, it is crucial to consider the appropriate filter design for the synchronverter. Additionally, by emulating the characteristics of SG, the values of “virtual” inertia and droop coefficients must be chosen accurately to provide a robust response under different operating scenarios of the synchronverter. Hence, this paper proposes a novel LLCL filter-based synchronverter system along with an adaptive control strategy using a fuzzy logic controller (FLC). The system is simulated under different operating scenarios in MATLAB/Simulink. The proposed system demonstrated improved frequency response and reduced total harmonic distortion compared to the conventional synchronverter model for all the different operating scenarios, thus enhancing grid stability and power quality.
Volume: 15
Issue: 4
Page: 2115-2127
Publish at: 2024-12-01

The future of artificial intelligence-driven robotics: applications and implications

10.11591/ijra.v13i4.pp361-372
Tole Sutikno
Artificial intelligence (AI)-driven robotics is a rapidly evolving field that is transforming various industries, including healthcare, manufacturing, transportation, logistics, security, retail, agri-food, and construction. The integration of artificial intelligence algorithms and machine learning techniques has propelled robotics beyond mere automation, enabling machines to modify, alter, adjust, learn, and interact with the world in ways previously deemed science fiction. The relentless pursuit of creating intelligent robotic systems has led to a symbiotic relationship between human inventiveness and AI, with AI-driven autonomous cars, drones, and robots transforming transportation, healthcare, and exploration. It offers flexibility and learning capabilities, transforming the way machines interact with humans. The integration of AI and robotics marks a transformative era in which machines become companions and cognitive extensions of human capabilities. In the future, we expect AI-driven robotics to bring significant changes to employment and societal well-being. However, the development of AI-driven robotics, which is the integration of AI and robotics, faces numerous challenges, including ethical concerns, legal issues, regulations, societal implications, and job market impacts for the proliferation of intelligent machines. Furthermore, it also presents challenges in terms of technical complexities in its development.
Volume: 13
Issue: 4
Page: 361-372
Publish at: 2024-12-01

Family functions to increase breastfeeding self efficacy of adolescent primigravida

10.11591/ijphs.v13i4.23956
Ratna Hidayati , Moch. Maftuchul Huda , Dhiana Setyorini , Wahyu Sri Astutik
Primigravida in adolescence will experience changes in situations that require the ability to adapt to pregnancy, thereby affecting their confidence in breastfeeding their babies. The function of the family is to help prepare the pregnant mother's needs to face the changes of pregnancy, prepare for childbirth and overcome difficulties in breastfeeding. This research aims to determine the function of the family in increasing breastfeeding self-efficacy of adolescent primigravida. Correlational research design, population and sample of 124 teenage primigravida respondents using total sampling technique. The independent variable family function was measured using the family assessment device (FAD) questionnaire and the dependent variable breastfeeding self-efficacy using the breastfeeding self-efficacy scale short form (BSES-SF) questionnaire which was analyzed using the Rho Spearman rank test. The research results showed that almost all respondents (90.3%) had healthy family functions and almost all respondents (83.9%) had high self-confidence. The statistical test results showed a significant p-value of 0.000 (α<0.05) with r=0.746. The function of the family in providing a sense of security and comfort creates a positive attitude among adolescent primigravida towards breastfeeding self-efficacy. Breastfeeding self-efficacy needs to increase breastfeeding success and prevent stunting.
Volume: 13
Issue: 4
Page: 1848-1854
Publish at: 2024-12-01

A comprehensive evaluation of machine learning algorithms for precise energy consumption forecasting in smart homes

10.11591/ijpeds.v15.i4.pp2138-2144
Lakshmana Phaneendra Maguluri , M. Shankar , R. Aruna , D. Chitra Devi , M. J. Suganya
Energy is one of the most critical and costly resources, playing a vital role in our daily lives. As technology advances, the demand for energy also increases. This work proposes a model for predicting energy consumption in smart homes, consisting of data preprocessing, performance evaluation, and application. Once the data is processed, it is fed into the prediction module, where various machine-learning algorithms are applied to forecast energy consumption. As smart home environments grow in complexity, selecting the most effective machine learning algorithm becomes increasingly crucial. The persistent challenge lies in manually discerning the best-performing algorithm, given their potential variance in efficacy across diverse use cases or datasets. In the dynamic landscape of energy conservation and cost-effective power generation, precise forecasting of energy consumption is essential, playing a pivotal role in advancing energy sustainability and bolstering economic stability. This introduction explores the intricate terrain of predicting energy utilization within smart homes, a domain that has seen increased interest due to the integration of machine learning algorithms. The primary focus of this exploration is the rigorous evaluation of these algorithms, using key performance metrics such as mean absolute error (MAE), mean squared error (MSE), and R-squared.
Volume: 15
Issue: 4
Page: 2138-2144
Publish at: 2024-12-01

Forecasting hourly short-term solar photovoltaic power using machine learning models

10.11591/ijpeds.v15.i4.pp2553-2569
Sravankumar Jogunuri , F. T. Josh , J. Jency Joseph , R. Meenal , R. Mohan Das , S. Kannadhasan
Forecasting solar photovoltaic power ensures a stable and dependable power grid. Given its dependence on stochastic weather conditions, predicting solar photovoltaic power accurately demands applying intelligent and sophisticated techniques capable of handling its inherent nonlinearity and volatility. Controlling electrical energy sources is an important strategy for reaching this energy balance because grid operators often have no control over use patterns. Accurately forecasting photovoltaic (PV) power generation from highly integrated solar plants to the grid is essential for grid stability. This study aims to improve forecasting accuracy and make accurate predictions of solar power output from the selected grid-connected PV system. In this study, the weather data was collected on-site and recorded PV power from a 20 kW on-grid system for one year, and different machine learning techniques like deep neural networks, random forests, and artificial neural networks were evaluated and benchmarked against reference support vector regression model. With improvements in forecasting accuracy of 2 to 37% over the reference model at study location (22.780 N, 73.650 E), College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra, India, simulation results showed that the random forest technique is effective for the forecasting horizons of 1 to 4 hours.
Volume: 15
Issue: 4
Page: 2553-2569
Publish at: 2024-12-01

Active balancing charging using ANFIS to reach longest lifetime for lithium ion

10.11591/ijpeds.v15.i4.pp2168-2179
Farid Dwi Murdianto , Indhana Sudiharto , Azzahra Farah Andraeni
This research aims to address challenges in efficient energy storage in electric vehicles, particularly those using lithium-ion batteries. The research focuses on voltage imbalances in battery packs with cells connected in series, which can reduce the battery pack's lifespan. We investigated active balancing charging methods using bidirectional fly-back converters in both pack-to-cell and cell-to-pack modes. Current charging methods face limitations such as prolonged balancing times, circuit complexity, and low efficiency. To overcome these challenges, this research proposes integrating an artificial intelligence approach using adaptive neuro-fuzzy inference system (ANFIS) control. This approach allows for dynamic adjustment of duty cycles, expediting the balancing process. Experimental results show that employing ANFIS control can significantly reduce balancing times. In pack-to-cell mode, balancing time decreased from 660 seconds to 600 seconds, while in cell-to-pack mode, it decreased from 660 seconds to 580 seconds. This research makes a significant contribution to the development of more efficient charging technology for lithium-ion batteries in electric vehicles. Its potential implications include improved battery lifespan and electric vehicle performance in the future.
Volume: 15
Issue: 4
Page: 2168-2179
Publish at: 2024-12-01
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