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25,002 Article Results

Tree-based models and hyperparameter optimization for assessing employee performance

10.11591/ijeecs.v38.i1.pp569-577
Rendra Gustriansyah , Shinta Puspasari , Ahmad Sanmorino , Nazori Suhandi , Dewi Sartika
The Palembang city fire and rescue service (FRS) is encountering challenges in adhering to national standards for fire response time. Hence, the Palembang city FRS is committed to enhancing employee performance through quarterly performance assessments based on various criteria such as attendance, work targets, behavior, education, and performance reports. This study proposes tree-based models in machine learning (ML) and hyperparameter optimization to assess the performance of Palembang city FRS employees. Tree-based models encompass decision trees (DT), random forests (RF), and extreme gradient boosting (XGB). The predictive performance of each model was evaluated using the confusion matrix (CM), the area under the receiver operating characteristic (AUROC), and the kappa coefficient (KC). The results indicate that RF performs better than DT and XGB in the sensitivity, AUROC, and KC metrics by 1.0000, 0.9874, and 0.8584, respectively.
Volume: 38
Issue: 1
Page: 569-577
Publish at: 2025-04-01

Implementation of a prototype to prevent childhood accidents in dangerous domestic environments using ESP 32 Wi-Fi module

10.11591/ijeecs.v38.i1.pp88-98
Jenner Lavalle-Sandoval , Paul Córdova-Cardenas , Sheyla Rivera-Quispe , Laberiano Andrade-Arenas
Robotics has significantly advanced human evolution by optimizing tasks in fields such as medicine, engineering, and mechanics, enhancing daily life through various robotic prototypes. These innovations help prevent accidents and injuries, whether at home or in hazardous environments. For instance, sensors can detect gas leaks, fires, and other potential disasters. This research aims to design a prototype adaptable to any home environment that poses risks to infants, such as kitchens, bathrooms, or stairs. The proposed prototype incorporates gas, motion, and sound sensors connected to a Wi-Fi ESP 32 module, which alerts parents to any potential danger to their children. The research is developed in six phases: component selection, circuit simulation, prototype design, three-dimensional (3D) printing, code programming, and final testing. The results demonstrate a positive impact, improving the control and care of infants by alerting parents to hazards such as gas leaks, crying, or movement in risky areas. The conclusion confirms the effectiveness of the prototype in providing timely alerts to safeguard infants in potentially dangerous situations.
Volume: 38
Issue: 1
Page: 88-98
Publish at: 2025-04-01

Exploring diverse prediction models in intelligent traffic control

10.11591/ijeecs.v38.i1.pp393-402
Sahira Vilakkumadathil , Velumani Thiyagarajan
Traffic congestion is a major challenge that affects excellence of life for numerous people across world. The fast growth in many vehicles contributes to congestion during peak and non-peak hours. The vehicle traffic resulted in many issues like accidents and inefficiency in traffic flow. Many traffic light control systems operate on fixed time intervals leads to inefficiency. The fixed-time signals cause unnecessary delays on roads with minimum number of quantity vehicles. Intelligent transport systems (ITS) introduce new comprehensive framework that combine the advanced technologies to improve the transportation network efficiency and to optimize the traffic management. The high-traffic routes are forced to wait excessively. Machine learning (ML) methods have designed to examine the traffic control. However, the accurate detection and vehicle tracking are essential one for effective ITS. In order to mention these problems, ML and deep learning (DL) methods are introduced to improve prediction performance.
Volume: 38
Issue: 1
Page: 393-402
Publish at: 2025-04-01

Energy and cost-aware workload scheduler for heterogeneous cloud platform

10.11591/ijeecs.v38.i1.pp546-554
Manjunatha Shivanandappa , Naveen Kumar Chowdaiah , Swetha Mysore Devaraje Gowda , Rashmi Shivaswamy , Vadivel Ramasamy , Subramani Suryakumar Prabhu Vijay
Parallel scientific workloads, often represented as directed acyclic graphs (DAGs), consist of interdependent tasks that require significant data exchange and are executed on distributed clusters. The communication overhead between tasks running on different nodes can lead to substantial increases in makespan, energy usage, and monetary costs. Therefore, there is potential to balance communication and computation to reduce these costs. In this paper, we introduce an energy and cost-aware workload scheduler (ECAWS) tailored for executing parallel scientific workloads, generated by the internet of things (IoT), in a heterogeneous cloud environment. The performance of the proposed ECAWS model is evaluated against existing models using the Inspiral scientific workload. Results indicate that ECAWS outperforms other models in reducing makespan, costs, and energy consumption.
Volume: 38
Issue: 1
Page: 546-554
Publish at: 2025-04-01

Simulation of ray behavior in biconvex converging lenses using machine learning algorithms

10.11591/ijeecs.v38.i1.pp357-366
Juan Deyby Carlos-Chullo , Marielena Vilca-Quispe , Whinders Joel Fernandez-Granda , Eveling Castro-Gutierrez
This study used machine learning (ML) algorithms to investigate the simulation of light ray behavior in biconvex converging lenses. While earlier studies have focused on lens image formation and ray tracing, they have not applied reinforcement learning (RL) algorithms like proximal policy optimization (PPO) and soft actor-critic (SAC), to model light refraction through 3D lens models. This study addresses that gap by assessing and contrasting the performance of these two algorithms in an optical simulation context. The findings of this study suggest that the PPO algorithm achieves superior ray convergence, surpassing SAC in terms of stability and accuracy in optical simulation. Consequently, PPO offers a promising avenue for optimizing optical ray simulators. It allows for a representation that closely aligns with the behavior in biconvex converging lenses, which holds significant potential for application in more complex optical scenarios.
Volume: 38
Issue: 1
Page: 357-366
Publish at: 2025-04-01

Study on postal life insurance attributes and its growth prediction using machine learning algorithms

10.11591/ijeecs.v38.i1.pp622-631
Thangavelu Ananadaraj Rajasekaran , Pichamuthu Vijayalakshmi , Velayutham Rajendran
The oldest insurer in the country, since 1884, is Postal Insurance. For today's livelihood, the citizens of India's life-saving coverage and insurance have become necessary. For customers to overcome difficult situations, life insurance is crucial in creating confidence. This is one of the highlights of the Postal organization. Under postal life insurance (PLI), the volume of new policies is enrolled throughout India, and a supervised machine learning (ML) process for finding the business cluster is carried out based on this data, which is discussed. A ML algorithm that predicts the growth for the future, using a suitable algorithm for accessing the features and process to identify the prediction model, has been developed, which is the main goal of this study. Simulation results show that expected is one of the most important variables used to predict and that both random forest (RF) and logistic regression outperformed the other two models. The RF model is the most effective and fastest in predicting the system's future state, and it shows the highest value for the PLI product.
Volume: 38
Issue: 1
Page: 622-631
Publish at: 2025-04-01

Photoplethysmograph-based time-frequency and machine learning applications on biomedical signal analysis for medical diagnosis

10.11591/ijeecs.v38.i1.pp145-160
Soumyadip Jana , Partha Sarathi Pal
Machine learning (ML) integration in biomedical signal processing and medical diagnosis has the potential to revolutionize healthcare by improving diagnostic accuracy. This paper focuses on the applications of different ML algorithms for analyzing real-time physiological data collected from Photoplethysmography (PPG) sensors. Heart rate variability (HRV) analysis using electrocardiography (ECG) signals makes the process longer and bulky. Therefore, this paper demonstrates the real-time generation of HRV signals using a simple, low-cost, and non-invasive PPG sensor which is further processed using the Arduino ATMEGA328P microcontroller and then interfaced to a PC for display to investigate the usefulness of HRV feature analysis. HRV features have been computed using time domain analysis (TA), and frequency domain analysis (FA). At last, these TA and FA indices have been given to different ML models that could predict the gender, age group, and physiological conditions of a human being. Prediction of the physiological conditions using TA, FA, and ML models simultaneously makes the proposed approach more novel than the other existing methods. Comparative analysis of different ML approaches using ROC curves and confusion matrices has been shown to find the effectiveness and precision of different proposed models. It shows random forest ML approach has achieved 91% accuracy in identifying the physiological conditions. This simple yet accurate real-time PPG-based time-frequency ML system might be useful in medical assessment with faster response.
Volume: 38
Issue: 1
Page: 145-160
Publish at: 2025-04-01

Self-assessment of secondary school Islamic education teacher: validity and reliability of qualitative study

10.11591/ijere.v14i2.29438
Azwani Masuwai , Hafizhah Zulkifli , Mohd Isa Hamzah
Issues concerning the validity of qualitative studies are frequently debated among scholars. Interview techniques often lack standardized procedures, which can affect reliability, and eliminating biases in interviews is challenging. Ensuring reliability and validity is crucial for establishing research credibility and high measurement reliability. This study was conducted to explore the reliability and validity of qualitative study of self-assessment concept among Islamic education teachers towards continuous professionalism development. The qualitative study was involved six participants selected based on the purposive sampling technique using a semi-structured interview. This study aimed to validate interview data through participant verification, ensure the validity of qualitative themes through expert validation, and enhance reliability via data triangulation. Multiple methods were employed to achieve these goals, including interview protocol verification, pre-field study, data triangulation, field notes, participant verification, expert validation, and agreement assessed using the Cohen Kappa Index, along with an extended study period. The findings indicated that the study met it is objectives for validity and reliability according to the recommended strategies and techniques. The use of various validation methods and attention to language barriers contributed to the study’s robustness and offers valuable guidance for future research.
Volume: 14
Issue: 2
Page: 961-974
Publish at: 2025-04-01

Influence of social networks on the mental health of university students in Huancayo, Peru

10.11591/ijere.v14i2.31094
Nilton David Vilchez Galarza , Luis Angel Huaynate Espejo , Carmen Rocío Ricra Echevarría
Since the appearance of signed social networks (SSNs), their use has increased steadily among young people, not only in terms of the number of users but also in terms of the time they devote to managing the platforms, a situation that may be influencing their behavior. This study aimed to analyze the influence of the use of social networks (SNs) on the mental health of young university students. For this purpose, a quantitative, basic, and correlational study was carried out. We worked with a sample of 361 undergraduate students in health careers at a university in Huancayo. The PERMA-Profiler scale for mental health and the brief social network addiction questionnaire were used as data collection instruments to evaluate the use of SNs. The results indicate that there is a statistically significant influence of the use of SNs on the mental health of students, which explains a variability of 53.5% to 79.9%, according to the values of the Nagelkerke Pseudo X2 calculation for SNs. This suggests that the use of SNs hurts students’ mental health.
Volume: 14
Issue: 2
Page: 1134-1140
Publish at: 2025-04-01

Integrating energy literacy into science education: a comprehensive systematic review

10.11591/ijere.v14i2.31873
Nik Aida Mastura Nik Abdul Majid , Kamisah Osman , Tan Siok Yee
Energy literacy is vital for preparing future generations to tackle global energy issues and advance sustainable development. However, integrating energy literacy into science education faces challenges due to diverse pedagogical approaches and educational contexts. This systematic literature review synthesizes current research to identify effective strategies for embedding energy literacy in science education. By employing advanced search techniques and preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, a thorough search across Scopus, Web of Science, and ERIC databases yielded 30 relevant studies meeting inclusion criteria. Findings indicate that interdisciplinary approaches, hands-on experiments, and innovative teaching tools like virtual and augmented reality (VR-AR) effectively enhance students’ understanding and attitudes toward energy, particularly renewable energy. Notably, project-based learning and science, technology, engineering, and mathematics (STEM) integration significantly improve problem-solving skills and creativity. Despite these positive outcomes, challenges such as high cognitive load in interdisciplinary courses and the need for ongoing teacher training persist. The review concludes that standardized curricula and professional development programs are necessary to further support teachers. Future research should focus on longitudinal studies to assess the long-term impact of these educational interventions and explore scalable models for diverse educational settings. This review provides valuable insights for educators, policymakers, and researchers aiming to enhance energy literacy through science education.
Volume: 14
Issue: 2
Page: 1253-1263
Publish at: 2025-04-01

Kafka-machine learning based storage benchmark kit for estimation of large file storage performance

10.11591/ijece.v15i2.pp1990-1999
Sanjay Kumar Naazre Vittal Rao , Anitha Chikkanayakanahalli Lokesh Kumar , Subhash Kamble
Efficient storage and maintenance of big data is important with respect to assuring accessibility and cost-friendliness to improve risk management and achieve an effective comprehension of the user requirements. Managing the extensive data volumes and optimizing storage performance poses a significant challenge. To address this challenge, this research proposes the Kafka-machine learning (ML) based storage benchmark kit (SBK) designed to evaluate the performance of the file storage system. The proposed method employs Kafka-ML and a drill-down feature to optimize storage performance and enhance throughput. Kafka-ML-based SBK has the capability to optimize storage efficiency and system performance through space requirements and enhance data handling. The drill-down search feature precisely contributes through reducing disk space usage, enabling faster data retrieval and more efficient real-time processing within the Kafka-ML framework. The SBK aims to provide transparency and ease of utilization for benchmarking purposes. The proposed method attains maximum throughput and minimum latency of 20 MBs and 70 ms, respectively on the number of data bytes is 10, as opposed to the existing method SBK Kafka.
Volume: 15
Issue: 2
Page: 1990-1999
Publish at: 2025-04-01

Impact of external demands problems on students’ psychological well-being: systematic literature review

10.11591/ijere.v14i2.30128
Muhammad Andi Setiawan , Endang Sri Estimurti , Yuni Pantiwati , Latipun Latipun , Bulkani Bulkani , Akhsanul In'am , Atok Miftachul Hudha
Students’ well-being is often disturbed by external demands, such as academic pressure, family expectations, and social expectations. These demands can impact students’ mental and emotional well-being. This research aims to explore the problems of external demands for students’ psychological well-being. This research used the systematic literature review (SLR) method to investigate the impact of external demands on students’ psychological well-being. Data were collected from articles published between 2018 and 2023 from the Scopus database. Of the 93 articles, 26 articles were obtained after screening. Data mining and analysis were conducted with the help of Publish or Perish, Biblioshiny, and ATLAS.ti. The results show the complexity of external demands, with factors such as internal and external support, job control, social media use, and individual differences in emotion regulation playing essential roles. The long-term impacts of these demands can include increased levels of stress, anxiety, and depression in students. Therefore, it is essential to manage external demands strategically to create a learning environment that supports students’ psychological well-being. This research highlights the need for joint efforts between schools, families, and communities to address external demands on students. Effective interventions are needed to reduce the negative impact of external demands.
Volume: 14
Issue: 2
Page: 1447-1458
Publish at: 2025-04-01

The readiness of mathematics teachers as agents of change: a recent comprehensive review

10.11591/ijere.v14i2.30291
Sharida Abu Talib , Nurfaradilla Mohamad Nasri , Muhammad Sofwan Mahmud
This study scrutinizes the role of mathematics teachers as pivotal agents of change in the evolving educational landscape, focusing on their readiness to embrace pedagogical reforms. The review aims to reveal the current patterns and trends in mathematics teachers’ readiness literature discussed in recent studies. Utilizing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, this study analyzed 31 empirical articles from the Scopus and Web of Science databases in 2023. The review process for chosen articles is examined, encompassing aspects such as publication criteria, eligibility and exclusion standards, databases, and the progression of review stages. The most striking result from the analysis is that mathematics teachers’ readiness is closely related to teaching strategies and pedagogy. Moreover, inconsistencies in practice and constraints such as inadequate resources, insufficient institutional support, and teacher training program gaps hinder their ability to implement change effectively. The implications of this study extend to various stakeholders in the education ecosystem, including policymakers, educational institutions, teacher training programs, and practitioners. This review suggests strategies to enhance teachers’ professional development and serve as preliminary work toward developing a pedagogy model. In conclusion, this systematic review consolidates the existing knowledge on the readiness of mathematics teachers as agents of change.
Volume: 14
Issue: 2
Page: 1468-1476
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

Investigation of the factors affecting students’ self-directed learning readiness in the blended learning model

10.11591/ijere.v14i2.31398
Nguyen Thi Bich , Kieu Phuong Thuy , Vu Thi Mai Huong , Pham Thi Binh
Many factors influence the level of readiness for self-directed learning. This study seeks to examine the relationship between learners’ personal characteristics (gender, major, academic year), external factors (facilities, self-study time, peer influence, teacher support), internal factors (cognitive skills, metacognitive skills, attitudes, motivation), and self-directed learning readiness in a blended learning model. The aim is to identify the decisive influencing factors to promote learners’ readiness for self-directed learning and improve blended teaching effectiveness. A survey was conducted with 1,276 students participating in the blended learning model at Hanoi National University of Education in Vietnam. The data were quantitatively analyzed using structural equation modeling with the partial least squares approach in SmartPLS 3, as well as regression analysis in SPSS 20. The findings showed that external factors accounted for 68.7% of the variation in internal factors and 41.6% of the variation in self-directed learning readiness. The study also found that factors such as major and academic year had significant impacts on self-directed learning readiness, as evidenced by statistically significant differences with p-values less than 0.05. These results suggest strategies for educators to effectively address these factors to enhance students’ self-directed learning readiness in blended learning environments.
Volume: 14
Issue: 2
Page: 1340-1350
Publish at: 2025-04-01
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