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25,002 Article Results

Five-level three phase cascaded H-bridge inverter using digital signal processor control for renewable energy applications

10.11591/ijece.v15i2.pp1348-1360
Bancha Hiransing , Boonthong Wasuri , Sanya Kuankid , Thanin Muangpool
This article presents a five-level three-phase cascaded H-bridge inverter for renewable energy applications, aimed at reducing total harmonic distortion (THD) and enhancing efficiency. The inverter uses a digital signal processing board, TMS320F28335, to generate pulse-width modulation signals through MATLAB/Simulink, ensuring precise control. The experimental setup includes an 84 VDC input voltage and a 300-watt load. Simulation and experimental results closely align, validating the accuracy of the simulation model. The output voltage shows a stepped pattern characteristic of multilevel inverters, significantly reducing harmonic distortion. THD analysis reveals a substantial reduction at higher modulation indices, with particularly low THD at a modulation index of 0.95. Consistent THD levels across modulation indices of 0.5, 0.8, and 0.95 demonstrate robust performance under varying conditions. Comparative analysis indicates that the proposed inverter achieves lower THD levels than traditional inverters, enhancing power quality and system efficiency. The five-level three-phase cascaded H-bridge inverter offers a promising solution for renewable energy applications by significantly reducing THD and improving power quality. Its robust performance and scalability potential contribute valuable advancements to renewable energy systems.
Volume: 15
Issue: 2
Page: 1348-1360
Publish at: 2025-04-01

Enhancing accuracy in greenhouse microclimate forecasting through a hybrid long short-term memory light gradient boosting machine ensemble approach

10.11591/ijece.v15i2.pp2392-2403
Mokeddem Kamal Abdelmadjid , Seddiki Noureddine , Bourouis Amina , Benahmed Khelifa
Greenhouse cultivation is one of the main methods for improving agricultural yield and quality. With the world needing more and more production, improving greenhouses using innovative technology becomes a must. These high-tech, aka, smart greenhouses depend much on the accuracy and availability of sensor data to perform at their best. In challenging situations such as sensor malfunctions or data gaps, utilizing historical data to predict microclimate parameters within the greenhouse is essential for maintaining optimal growing conditions and effective sustainable resource management control. In this work, and by employing a synthesis technique across various time series models, we forecast internal temperature and humidity, the two main parameters for a greenhouse, by incorporating diverse characteristics as input into a customized forecasting model. The selected architecture integrates deep learning and nonlinear learning models, specifically long short-term memory (LSTM) and light gradient boosting machine (LightGBM) as an ensemble approach, providing a comprehensive framework for time-series prediction, evaluated through mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) metrics. With a focus on improving accuracy in anticipating environmental changes, we have achieved high precision in predicting temperature (98.45%) and humidity (99.61%).
Volume: 15
Issue: 2
Page: 2392-2403
Publish at: 2025-04-01

A novel technique for selecting financial parameters and technical indicators to predict stock prices

10.11591/ijece.v15i2.pp2192-2201
Sneha S. bagalkot , Dinesha H. A. , Nagaraj Naik
Stock price predictions are crucial in financial markets due to their inherent volatility. Investors aim to forecast stock prices to maximize returns, but accurate predictions are challenging due to frequent price fluctuations. Most literature focuses on technical indicators, which rely on historical data. This study integrates both financial parameters and technical indicators to predict stock prices. It involves three main steps: identifying essential financial parameters using recursive feature elimination (RFE), selecting quality stocks with a decision tree (DT), and forecasting stock prices using artificial neural networks (ANN), deep neural networks (DNN), and extreme gradient boosting (XGBoost). The models’ performance is evaluated with root mean square error (RMSE) and mean absolute error (MAE) scores. ANN and DNN models showed superior performance compared to the XGBoost model. The experiments utilized Indian stock data.
Volume: 15
Issue: 2
Page: 2192-2201
Publish at: 2025-04-01

A comprehensive analysis of different models: skin cancer detection

10.11591/ijece.v15i2.pp2404-2415
Amruta Thorat , Chaya Jadhav
Due to fast-growing worldwide air pollution and ozone layer destruction, an alarming number of people are found to have skin cancer, more than any other kind of cancer combined. It is known to be one of the deadliest malignancies; if not identified and cured in its early stages, it is likely to spread to other body parts. Early detection is critical and helps prevent cancer from spreading. This allows for early decisions on diagnostic and treatment options. Early diagnosis and discovery, combined with the right treatment, can save lives. In this paper, we have done a detailed survey on various techniques and models developed for skin cancer detection and also discussed different security-related issues. This work thoroughly explores the several types of models utilized to identify cancer in the skin.
Volume: 15
Issue: 2
Page: 2404-2415
Publish at: 2025-04-01

Novel technique to deblurring and blur detection techniques for enhanced visual clarity of ancient images

10.11591/ijece.v15i2.pp2314-2324
Poonam Pawar , Bharati Ainapure
Digital image quality often degrades due to various factors such as noise and blur. Many images are affected by these issues, reducing their clarity and accuracy. This degradation is especially problematic for ancient images, significantly hampers the ability to analyze historical documents and artworks. This paper presents a novel approach to both blur detection and deblur ancient images, enhancing their clarity and readability. This research introduces a technique that combines wavelet transform and convolutional neural networks (CNNs) for effective blur identification and deblurring, specifically aimed at restoring blurred ancient images, regardless of the type of blur degradation. This novel approach demonstrated an average accuracy of 98.3% in blur detection on ancient image datasets. The performance of deblurring algorithms is typically evaluated using metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index (SSIM) which quantify fidelity and quality of the deblurred images. In the deblurring, this approach produced PSNR values of 55.5 to 68.3 dB, MSE values of 2.99 to 11.1, and an SSIM of 0.9 across different types of blurs. These results show significant promise for the restoration of ancient images, providing researchers, historians, and archaeologists with valuable tool for conservation cultural heritage.
Volume: 15
Issue: 2
Page: 2314-2324
Publish at: 2025-04-01

Strategies and techniques for creating educational programs for teachers of natural science subjects

10.11591/ijere.v14i2.28905
Zhanara Nurmukhamedova , Dilara Nurbayeva , Bulbul Yerzhenbek , Diana Nassirova
The relevance of the study is based on the current educational reforms in the Republic of Kazakhstan, which is implemented under the conditions of humanization and integration at different levels. This is based on the development of the informatization and technologization of current science and practice, the trend towards the interpenetration of certain fields of knowledge into others, the exploration of interdisciplinary approaches in explaining the current world view, which is itself multidimensional, persistent, impartial and integrative. The purpose of this study is to provide a theoretical framework, developing a model for the training of modern natural science teachers and investigate the main stages of curriculum development for future teachers. The objectives of the study are aimed at disseminating knowledge about the development of effective educational programs. Objective methods included theoretical analysis, analysis of future teachers’ activities, synthesis of philosophical and educational psychology literature, modelling and observation. The study investigated and systematized approaches to the methodological design of educational programs and identified all types of professional competence using the method of analysis.
Volume: 14
Issue: 2
Page: 1369-1378
Publish at: 2025-04-01

Assessing self-regulated learning abilities of Indonesian students using cognitive diagnostic model

10.11591/ijere.v14i2.29969
Bayuk Nusantara , Samsul Hadi , Heri Retnawati
This study has two objectives: to find out which cognitive diagnostic model (CDM) is suitable for extracting diagnostic information from non-diagnostic measurement data of Indonesian students’ self-regulated learning abilities; and to find out the attributes of self-regulated learning (SRL) abilities that have not been mastered by Indonesian students. This study used a quantitative research method with a retrofitting approach (post-hoc analysis). There were 3,874 respondents, besides there were 22 items of Q-matrix that measure four attributes of SRL ability. The data were analyzed and empirically validated using the R program with the generalized deterministic input and gate (GDINA) package. The results showed that the GDINA model is the most appropriate model for extracting diagnostic information regarding the SRL abilities of Indonesian students. In addition, most Indonesian students have not been able to master the four SRL ability attributes where the planning attribute (A1) is the most difficult attribute for Indonesian students to master.
Volume: 14
Issue: 2
Page: 984-994
Publish at: 2025-04-01

Identifying challenges of cultivating creativity in product design

10.11591/ijere.v14i2.29568
Salwa Suradin , Mohamad Sattar Rasul , Marlissa Omar
Creativity, a crucial skill in the fourth industrial revolution (IR4.0) is highly demanded in the workforce to drive innovation in product design. Therefore, it is important to cultivate creativity in product design through design and technology (D&T) education. However, many past studies encountered challenges regarding D&T teachers’ teaching creativity which might affect creativity cultivation among students. Thus, this article aims to identify the challenges faced by D&T teachers in cultivating student creativity within product design development. Utilizing the systematic literature review (SLR) method using reporting standards for systematic evidence syntheses (ROSES) in three databases, Web of Science, Scopus, and Google Scholar. Based on thematic analysis, this SLR leads to three themes: i) lack of pedagogical competence; ii) different thinking styles; and iii) lack of motivation. Addressing these challenges highlights the importance for educational institutions to align their curricula with current industry demands, ensuring students are well-prepared to tackle the complexities of contemporary product design. This effort requires collaboration among educators, industry leaders, and policymakers to update teaching methods, incorporate practical experiences, and enhance an environment that cultivates creativity.
Volume: 14
Issue: 2
Page: 1488-1495
Publish at: 2025-04-01

A qualitative case study of constructivist teaching at a high school in a northern area of Vietnam

10.11591/ijere.v14i2.32123
Pham Thi Kieu Oanh , Nguyen Van-Trao , Nguyen Huong Thi Mai
This study aims to identify the English high school teachers’ beliefs at a high school in the northern region of Vietnam regarding constructivist teaching (CT) and their actual classroom practices (CP). This paper summarizes results from a five-year Ph.D. study conducted with seven high-school English teachers within a qualitative case study research design with the help of three data collection instruments, including semi-structured and Stimulated recall interviews and direct classroom observations. Thematic analysis using MAXQDA was chosen for data analysis. The findings uncovered themes that emerged from the data, which might lay the foundation for classifying teachers into three groups: adaptive originators (AOs), neutral pragmatists (NPs), and traditional conservers (TCs). Most TCs’ teaching philosophies were teacher-centered and supportive of conventional teaching techniques. Still, the NPs acknowledged that instructional strategies needed to be changed. The AOs sought to change how teachers used CT; they actively put strategy into place to bring about meaningful change. These results were valuable references for high school teachers to assist their students with better constructivist instruction, thus enhancing the quality of teaching and learning English in the 4.0 era.
Volume: 14
Issue: 2
Page: 1436-1446
Publish at: 2025-04-01

Development and instrument validation of Indonesian achievement motivation scale using the Rasch model

10.11591/ijere.v14i2.29374
Bambang Dibyo Wiyono , Nur Hidayah , Muhammad Ramli , Adi Atmoko , Amin Al-Haadi Shafie
This research involves the development of items on an achievement motivation scale that is used in improving the achievement motivation of senior high school students. No research on instrument development has been carried out on measuring the level of achievement motivation of senior high school students in Indonesia. Participants tested the development of 38 items and consisted of 1,909 respondents as students from senior high schools in the City of Surabaya. The utilized analysis technique was the Rasch model. Results of applying the Rasch analysis indicated that achievement motivation scale was good, proper, and appropriate in items to the model. The accomplishment motivation scale is a valid and dependable instrument for precisely determining pupils’ levels of accomplishment motivation. In light of the achievement motivation scale results, this study explores the consequences and suggests directions for future research on the use of guidance and counseling.
Volume: 14
Issue: 2
Page: 1427-1435
Publish at: 2025-04-01

Prototype of alternate wetting and drying rice cultivation using internet of things for precision agriculture

10.12928/telkomnika.v23i2.26529
Akkachai; Rajamangala University of Technology Isan Phuphanin , Metha; Rajamangala University of Technology Isan Tasakorn
This study introduces a semi-automatic system for alternating wet and dry rice cultivation using internet of things (IoT) technology to enhance precision agriculture and address critical challenges in water resource management. The prototype consists of node and master devices powered by ESP32 microcontrollers integrated with sensors to monitor air temperature, humidity, and water levels. Communication between the devices is achieved through the low-latency, low-power encrypted secure protocol-network over wireless (ESP-NOW) protocol, enabling real-time monitoring and remote control of water pumps. Data collected by the system is displayed on ThinkSpeak servers and Nextion touch screens, aiding efficient irrigation and environmental management for farmers. Performance testing demonstrates that the system achieves reliable communication up to 115 meters with efficient energy consumption, operating for approximately two hours with a 3,000 mAh battery. By optimizing irrigation practices, the system reduces water waste while ensuring adequate crop hydration, promoting sustainable farming practices. This scalable IoT solution not only enhances productivity and resource efficiency but also contributes to broader efforts in agricultural sustainability by supporting precise environmental control and minimizing dependency on manual labor.
Volume: 23
Issue: 2
Page: 455-465
Publish at: 2025-04-01

Dual band antenna design for 4G/5G application and prediction of gain using machine learning approaches

10.12928/telkomnika.v23i2.26233
Narinderjit; INTI International University Singh Sawaran Singh , Md. Ashraful; Daffodil International University Haque , Redwan; Daffodil International University A. Ananta , Md. Sharif; Daffodil International University Ahammed , Md. Abdul; Friedrich Schiller University Jena Kader Jilani , Liton; Pabna University of Science and Technology Chandra Paul , Rajermani; INTI International University Thinakaran , Malathy; INTI International University Batumalay , JosephNg; INTI International University Poh Soon , Deshinta; INTI International University Arrova Dewi
In this research, we disclose our findings from exploring a machine learning (ML) approach to enhancing the antenna’s performance in Industrial and Innovation contexts, particularly for4G and 5G (n77, n78) contexts. Methods for evaluating antenna performance utilizing simulation, the resistor, inductor, and capacitor (RLC) equivalent circuit model, and ML are discussed. Gain is a maximum of 6.56 dB and efficiency is about 97% for this antenna. The predicted antenna gain is calculated using an alternative supervised regression ML technique. Multiple measures, including as the variance score, R-square (R2), mean square error (MSE), and mean absolute error (MAE), can be used to assess an ML model’s performance. The linear regression (LR) model predicts profit with the fewest errors and highest accuracy of the five ML models. Finally, computer simulation technology (CST) and advanced design system (ADS) modeling findings, along with ML results, show that the proposed antenna is a promising option for 4G and 5G applications.
Volume: 23
Issue: 2
Page: 543-552
Publish at: 2025-04-01

Digital learning models: experience of online learning during the pandemic

10.11591/ijere.v14i2.30032
Umi Muzayanah , Moch Lukluil Maknun , Faidus Sa'ad , Mustolehudin Mustolehudin , Mulyani Mudis Taruna
During the global pandemic of COVID-19, the learning model has been “forced” to transition from conventional to distance learning. At the beginning of its implementation, digital-based learning received many complaints from teachers, parents, and students. Gradually, they can adapt to distance learning that utilizes many digital devices. Through quantitative and qualitative research approaches, this paper aims to describe the online learning model in schools and Islamic boarding school (pesantren) based on their experience during COVID-19. From these studies, several digital-based learning models can be identified. First, social media-based learning. Social media-based learning is carried out by optimizing the use of WhatsApp as the main media in learning. Second, learning through virtual classrooms, which is face-to-face learning between teachers and students in a digital space. Third, education platform-based learning, where the learning process is conducted through internal school or government platforms. Fourth is blended learning, which is learning partly online and offline. This fourth lesson aims to accommodate the learning needs of students or teachers who have obstacles such as signal difficulties and weak economies. The findings contribute to the availability of references for digital learning models that can be applied in the future.
Volume: 14
Issue: 2
Page: 1196-1206
Publish at: 2025-04-01

Predicting personalized mathematics learning among pre-university students in the Maldives

10.11591/ijere.v14i2.31174
Mausooma Mohamed , Ahmad Fauzi Mohd Ayub , Maizura Yasin , Nur Raihan Che Nawi
The use of learning management systems (LMS), such as Moodle, for personalized mathematics learning (PML) is successful; nonetheless, its performance depends on several factors. This study investigates the factors influencing the utilization of LMS for PML at the Maldives National University (MNU). A correlational study was conducted involving 120 randomly selected pre-university students using an online questionnaire to measure performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and student commitment (SC) toward LMS usage behavior (UB). Data analysis with IBM statistical package for the social sciences (SPSS) statistics version 25.0 revealed significant positive relationships between LMS UB and PE (r=0.624**), EE (r=0.644**), FC (r=0.533**), and SC (r=0.696**), with SC being the strongest predictor. SI showed a weaker positive relationship (r=0.204). The study also discovered a multiple correlation (R) value of 0.807 and an analysis of variance (ANOVA) (F (5, 114)=42.497, p=0.000). The study’s findings underscore the significance of these factors in promoting LMS adoption and effective use concluding that focusing on these key predictors can enhance PML and improve student engagement and performance.
Volume: 14
Issue: 2
Page: 1141-1149
Publish at: 2025-04-01

Enhancing engineering education through virtual reality: a systematic study on immersive engineering education practices

10.11591/ijece.v15i2.pp1889-1899
Tarek Riaji , Sanae El Hassani , Fatima Ezzahrae M'hamdi Alaoui
This article explores the integration of virtual reality (VR) and associated technologies in engineering education, focusing on the pedagogical approaches adopted in this integration, which we refer to as immersive engineering education. This study considers the application possibilities and the transformative impact of VR on engineering education. The article addresses the critical collection and analysis of VR applications in engineering education. It covers main VR-related papers published from 2015 to February 2024 and indexed in Scopus, Web of Sciences, or both, and discussing design, development challenges, and collaborative tools. Empirical evidence showcases improved engagement, motivation, and learning outcomes. The findings offer modern insights for educators and researchers on leveraging VR for impactful learning experiences, while also noting the need for further research in this evolving field.
Volume: 15
Issue: 2
Page: 1889-1899
Publish at: 2025-04-01
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