Validation Of Results Research Articles

Pengembangan Learning Management System (LMS) Menggunakan Moodle Pada Fase E di SMAN 1 Lubuk Alung

This research aims to increase student engagement in learning through the development and implementation of interactive features on Moodle. Some of the features developed include quizzes, interactive discussion forums, and learning videos. This type of research is Research and Development with a 4D development model. Research data was collected through questionnaires and validation sheets. Data analysis was carried out in a quantitative descriptive manner to determine the level of student involvement and the quality of the LMS. At the development stage, validation tests were carried out by four validators, three media validators and one material. A practicality trial was carried out on Phase E students of SMAN 1 Lubuk Alung totaling 36 students to test the practicality of the LMS developed. The assessment results from the material validators received an average score of 4.6 with the category "very valid". The results of the validation of media experts with validator I got an average score of 4.95 with the category "very valid" while the results of validation by validator II got an average score of 4.95 with the category "very valid" and the results of validator III got an average score of 4.95 with the category "very valid". This LMS can be said to be practical because it has conducted a practicality test with an average score of 4.37 and an effectiveness test with a score of 4.18. Based on the results of the validity, practicality and effectiveness tests, it can be concluded that this LMS is feasible to be implemented in the school

Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling

With the advancement of the large language model (LLM), the demand for data labeling services has increased dramatically. Big models are inseparable from high-quality, specialized scene data, from training to deploying application iterations to landing generation. However, how to achieve intelligent labeling consistency and accuracy and improve labeling efficiency in distributed data middleware scenarios is the main difficulty in enhancing the quality of labeled data at present. In this paper, we proposed an asynchronous federated learning optimization method based on the combination of LLM and digital twin technology. By analysising and comparing and with other existing asynchronous federated learning algorithms, the experimental results show that our proposed method outperforms other algorithms in terms of performance, such as model accuracy and running time. The experimental validation results show that our proposed method has good performance compared with other algorithms in the process of intelligent labeling both in terms of accuracy and running solves the consistency and accuracy problems of intelligent labeling in a distributed data center.

Performance of repair pre-damaged RC columns using steel straps tensioning techniques (SSTT) confinement: experimental data and modelling

PurposeA model that extends study parameters to predict repaired column behaviour is efficient. Three-dimensional nonlinear finite element models were created in ABAQUS to simulate steel strap confinement with inclusion of pre-damaged levels.Design/methodology/approachExperimental and analytical studies demonstrate that restored reinforced concrete (RC) columns usually crush at mid-height under axial compressions. Numerical models verified RC column load-deformation. Although some specimens have considerable column stiffness differences, a numerical model based on statistical analysis matches experimental test results.FindingsIt shows that, finite element model exhibited a tendency to overestimate the stiffness of the columns, with an average absolute error (AAE) of 23.1%. The validation results indicate that the AAE values for strength and ductility were 15.1% and 12.3%. It has been demonstrated that the combination of strength and ductility is capable of yielding predictions with an error rate of approximately 20%. A parametric study focused on finite element model-predicted load bearing capacity reduction.Originality/valueA numerical analysis employing finite element modelling has been formulated to investigate the behaviour of confined columns. The model underwent validation through comparison with the experimental results. The validated model is utilised to perform additional parametric investigations on the confined column.

A deep learning-based method for assessing tricuspid regurgitation using continuous wave Doppler spectra

Transthoracic echocardiography (TTE) is widely recognized as one of the principal modalities for diagnosing tricuspid regurgitation (TR). The diagnostic procedures associated with conventional methods are intricate and labor-intensive, with human errors leading to measurement variability, with outcomes critically dependent on the operators’ diagnostic expertise. In this study, we present an innovative assessment methodology for evaluating TR severity utilizing an end-to-end deep learning system. This deep learning system comprises a segmentation model of single cardiac cycle TR continuous wave (CW) Doppler spectra and a classification model of the spectra, trained on the TR CW Doppler spectra from a cohort of 11,654 patients. The efficacy of this intelligent assessment methodology was validated on 1500 internal cases and 573 external cases. The receiver operating characteristic (ROC) curves of the internal validation results indicate that the deep learning system achieved the areas under curve (AUCs) of 0.88, 0.84, and 0.89 for mild, moderate, and severe TR, respectively. The ROC curves of the external validation results demonstrate that the system attained the AUCs of 0.86, 0.79, and 0.87 for mild, moderate, and severe TR, respectively. Our study results confirm the feasibility and efficacy of this novel intelligent assessment method for TR severity.

Pengembangan Media Pembelajaran Go Puzzle Pada Mata Pelajaran Pendidikan Pancasila dan Kewarganegaraan untuk Siswa Sekolah Menengah Pertama

This study aims to develop and test the validity, attractiveness, effectiveness, and applicability of Go Puzzle learning media in the subjects of Pancasila and Civic Education. The procedure used is research and development based on the steps proposed by Borg and Gall. The products produced in this development study are Go Puzzle learning media material for the Formulation and Determination of Pancasila as a State Basis that is valid, interesting, effective, and can be applied in learning. The product validity test is based on the results of material expert validation of 92.78 percent, media expert validation of 91.42 percent, expert validation of learning implementation plans of 95.63 percent, and field learning expert validation of 98.21 percent. The product attractiveness test is based on the results of small group trials of 90.36 percent and the results of large group trials of 93.46 percent. The product effectiveness test is based on the results of large group trials which resulted in an average completeness of students’ scores when pretest was 20.89 percent and post test was 86.89. This shows that there is an increase in the percentage of completeness of scores obtained by students, which is 66 percent. The product applicability test is based on the educator response questionnaire, which is 96.88% which shows that the product developed can be applied in learning. Abstrak: Kajian ini bertujuan untuk mengembangk

Design an Electrical Power Practical Module in the Introductory Electrical Energy Course

The introductory electrical energy course is one of the courses that applies learning using practical methods. In the introductory electrical energy course, there is material about electrical power. Based on observations, it is known that students need more learning media in the form of modules in the electrical power practicum, which causes a lack of interest and low understanding of student learning. Based on these problems, a learning medium in the form of a practicum module was developed to complement practicum learning and is expected to increase students' interest in and understanding of learning. The electrical power practicum module was developed using research and development methods. The researcher's research instrument was a media expert and material expert validation sheet to test the feasibility of the practicum module to be applied in the Electrical Power Practicum. The validation results of media and material experts prove that the electrical power practicum module is very feasible regarding media and material. Based on the percentage of scores obtained from media experts, namely 81.66%, material experts obtained a score of 88.57%, which is included in the very suitable category to be applied in the Basic Electrical Energy course.

Trainer Kit Module for DC Motor Speed Control Using Voice Commands Based on Arduino

Based on the results of observations made by researchers through direct observation of lecturers in the field of electricity, Electrical Engineering Education Study Program, it was found that there is a need for additional trainers as learning media in the electrical machine control course. This study aims to find out how to design a trainer kit design module for DC motor speed control using voice commands by utilizing the Arduino microcontroller. We also learned the results of testing the design of a trainer kit design module for DC motor speed control using voice commands by utilizing the Arduino microcontroller. The research method researchers use is the Research and Development (R&D) method, which aims to develop new products or innovations that benefit. Based on the results of expert validation, it can be concluded that the Arduino Microcontroller-Based Trainer Kit Practical Module obtained a high percentage of media experts at 96%, material experts at 95.5%, and language experts at 80%. These results indicate that this module gets the "Very Eligible" category to be applied to the Electrical Machine Control course.

Optimizing Agility in the Pharmaceutical Supply Chain Using Digital Twins to Cope with the Ripple Effect

The pharmaceutical supply chain is a multilayered and complex structure designed to deliver medicines to customers in a timely manner while ensuring optimal quality and quantity of medicines. The COVID-19 outbreak exposed the vulnerability and uncertainty of the pharmaceutical supply chain, so managing the risks in the pharmaceutical supply chain has become particularly important. This study demonstrates that digital twin (DT) technology can improve pharmaceutical supply chain agility and reduce the ripple effect caused by disruptions. The ripple effect refers to the effect that a sudden disruption at one node in the supply chain has on causing a chain reaction in the rest of the supply chain. The most used risk management method today is the Enterprise Resource Planning (ERP) system, which allows for real-time data sharing but has limitations in predicting and modeling the entire supply chain and lacks the ability to make quick decisions and simulate the entire supply chain. DT technology creates a virtual model of the supply chain, which enables continuous communication and information exchange between real assets and the virtual model, offering a promising alternative to risk management for pharmaceutical supply chains. risk management by providing a promising alternative. This study utilizes the AnyLogistix platform to construct a DT model and demonstrates the effectiveness of DT technology in reducing ripple effects and improving supply chain agility using quantitative analysis. This paper focuses on analyzing the pharmaceutical supply chain for 75mg aspirin tablets in London. A supply chain that operates normally, a supply chain that introduces a disruptive event (closure of the logistics center due to an earthquake), and a supply chain that applies a proactive strategy (activation of an alternate logistics center) to mitigate risk are simulated separately. The positive impact of DT technology on the supply chain is evaluated by analyzing the methods of key performance indicators (KPIs) such as inventory level, order fulfillment rate, and delivery time. The experimental results show that DT technology enhances the responsiveness, flexibility, speed, and proactivity of the supply chain, which significantly improves the agility of the supply chain. Proactive and effective strategies also reduce the financial impact of disruptions and service levels are quickly recovered. In addition, the proactive strategy stabilized inventory levels and reduced delayed orders. The above key metrics confirm the hypothesis that DT technology can improve supply chain agility and effectively reduce the ripple effect. Despite the significant conclusions drawn in this paper, there are some limitations. Firstly, sensitivity analysis and t-test were not used in the study for secondary testing, which resulted in the lack of reliability of the validation results. In addition, the possible interaction between the ripple effect and the bullwhip effect was not discussed, leading to a confounding factor in the experimental results. The above issues can be considered in further research in the future. Finally, this paper explores the possibilities and uses of introducing machine learning techniques and considers the supply chain risk factors of globalization. In summary, this study validates the potential of digital twin technology in improving agility and coping with ripple effects in pharmaceutical supply chains. As research continues, DT technology is expected to improve supply chain efficiency and reduce supply chain risk while ensuring patient safety.

Enhanced ICP-MS detection of 24 elemental impurities in complex, high matrix mineral, medicinal lanthanum carbonates according to ICH Q2 (R2).

Validation of the specificity of ICP-MS methods by ICH Q2 (R2) through spiking experiments cannot characterize the multi-atomic interference of complex high matrix samples, possibly affecting the accuracy of the method and reliability of results. In this study, we highlight this issue by investigating the elemental impurities in lanthanum carbonate raw materials and use isotope ratio analysis to overcome this limitation and establishing an accurate ICP-MS method for 24 elemental impurities (listed in ICH Q3D). An Agilent 7900 ICP-MS was used with an automatic sampler for sample determination in the He mode. The isotope ratio of elemental impurities in the spiked samples was analyzed to characterize polyatomic interference and select the exclusive mass number of elemental impurities. While the recovery rate of most elemental impurities met the requirements, that of selenium (default measurement mass number 78Se) exceeded the standard in the matric sample. The isotope ratio of Se (78Se/82Se) was much higher than the theoretical value, indicating that the response intensity of m/z 78 was formed by the combination of 78Se and other mass-to-charge ratios, such as LaOH2+ formed by the large amount of La in the matrix. Therefore, 82 was chosen as the mass number for Se. The validation results indicate that the method has strong specificity, high accuracy, and is simple and facile. This study provides technical support for the control of elemental impurities in lanthanum carbonates and isotope ratios can be used as a supplementary means of spiked recovery rates.

Investigation on improving immunologic reconstitution insufficiency using DiwuYanggan capsules in AIDS patients

BackgroundThis study aimed to explore the mechanism of action of DiWuYangGan (DWYG) capsule in improving Immunological non-responder (INR) by analyzing the active ingredients of DWYG.MethodsThe study employed a randomized, controlled, double-blind, single-simulation method. Patients were randomly divided into control and trial groups and treated with the primal highly effective antiretroviral therapy. To demonstrate the effect of DWYG on INR, patients in the control group were administered simulated DWYG, whereas patients in the trial group were administered DWYG capsules (ChiCTR1900024673). The chemical composition of DWYG was analyzed using ultra-performance liquid chromatography-high-resolution mass spectrometry. Potential targets of DWYG in the treatment of INR were identified and predicted using network pharmacology and molecular docking. The molecular mechanisms underlying the effects of DWYG were validated using a peripheral blood monocyte model.ResultsThe CD4:CD8 ratio in the trial group was significantly higher than that in the control group (p < 0.01). A total of 210 DWYG compounds were identified and network pharmacology revealed 182 potential therapeutic targets for DWYG and INR. The results of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that the toll-like receptor signaling pathway is one of the key pathways. This study demonstrated that DWYG reduced the expression level of TLR4 and the levels of IL-2, IL-10, and TNF-α, which are important cytokines involved in the immune response.ConclusionThe efficacy of DWYG in the treatment of INR confirmed the potential practical components of DWYG. Moreover, the results of network pharmacology and experimental validation showed that DWYG could restore the immune function of acquired immune deficiency syndrome patients by inhibiting the expression of TLR4 and related signaling pathways and the overactivation of immune function.Clinical Trial Registration:https://www.chictr.org.cn/index.html, identifier ChiCTR1900024673.

Construction of a nomogram for personalized prediction of lower limb lymphedema risk after cervical cancer surgery

ObjectiveThis study aims to construct and evaluate an individualized nomogram for predicting the risk of lower limb lymphedema after cervical cancer surgery. Healthcare professionals can utilize line chart models to predict the probability of postoperative lower limb lymphedema in different patients, allowing for the early identification of high-risk patients and facilitating early prevention and treatment.MethodsA retrospective study was conducted among 411 cervical cancer patients treated at our hospital from May 2021 to December 2023. The patients were randomly divided into a modeling group (313 cases) and a validation group (98 cases) according to an approximate 3:1 ratio. The modeling group was further divided into a lower limb lymphedema group (61 cases) and a non-lower limb lymphedema group (252 cases) based on the presence of postoperative lower limb lymphedema. Multiple factors Logistic regression was used to identify risk factors, and a nomogram was constructed using R software version 4.0.2, with internal and external validation performed.ResultsRisk factors for lower limb lymphedema following cervical cancer surgery include age 60 years or older, a Body Mass Index (BMI) of 24 kg/m² or higher, hypertension, the removal of 30 or more lymph nodes, adjuvant radiotherapy and chemotherapy, and prolonged standing for six hours or more (P < 0.05). Internal and external validation results demonstrated that the calibration curve closely aligned with the ideal curve. The Area Under Curve(AUC) of the Receiver Operating Characteristic(ROC) curve was 0.890 (95% CI: 0.844 ∼ 0.936) and 0.876 (95% CI: 0.821 ∼ 0.930), indicating high model calibration and discrimination. Decision Curve Analysis(DCA) curve revealed that the Logistic model had good net returns and high clinical practicality when the probability range of the high-risk threshold was 0.11 ∼ 0.98.ConclusionThe nomogram, developed using factors such as age, BMI, hypertension, number of lymph nodes dissected, adjuvant radiotherapy and chemotherapy, and duration of standing, has strong predictive value and offers significant clinical benefits, making it a valuable tool for clinical decision-making.

Vegetation Restoration Enhanced Canopy Interception and Soil Evaporation but Constrained Transpiration in Hekou–Longmen Section During 2000–2018

The quantitative assessment of the impact of vegetation restoration on evapotranspiration and its components is of great significance in developing sustainable ecological restoration strategies for water resources in a given region. In this study, we used the Priestley-Taylor Jet Pro-pulsion Laboratory (PT-JPL) to simulate the ET components in the Helong section (HLS) of the Yellow River basin. The effects of vegetation restoration on ET and its components, vegetation transpiration (Et), soil evaporation (Es), and canopy interception evaporation (Ei) were separated by manipulating model variables. Our findings are as follows: (1) The simulation results are compared with the ET calculated by water balance and the annual average ET of MODIS products. The R2 of the validation results are 0.61 and 0.78, respectively. The results show that the PT-JPL model tracks the change in ET in the HLS well. During 2000–2018, the ET, Ei, and Es increased at a rate of 1.33, 0.87, and 2.99 mm/a, respectively, while the Et decreased at a rate of 2.52 mm/a. (2) Vegetation restoration increased the annual ET in the region from 331.26 mm (vegetation-unchanged scenario) to 338.85 mm (vegetation change scenario) during the study period, an increase of 2.3%. (3) TMP (temperature) and VPD (vapor pressure deficit) were the dominant factors affecting ET changes in most areas of the HLS. In more than 37.2% of the HLS, TMP dominated the change affecting ET, and vapor pressure difference (VPD) dominated the area affecting ET in 30.5% of the HLS. Overall, the precipitation (PRE) and VPD were the main factors affecting ET changes. Compared with previous studies that directly explore the relationship between many influencing factors and ET results through correlation research methods, our study uses control variables to obtain results under two different scenarios and then performs difference analysis. This method can reduce the excessive interference of influencing factors other than vegetation changes on the research results. Our findings can provide strategic support for future water resource management and sustainable vegetation restoration in the HLS region.

Pengembangan Media Pembelajaran Berbasis Whatsapp dengan Fitur Chatbot untuk Meningkatkan Pemahaman Siswa pada Materi Kebijakan Moneter dan Fiskal Kelas XI SMAS Islam Nurul Amanah

The purpose of this development research is to produce a whatsapp-based learning media product with a chatbot feature to improve students' understanding of monetary and fiscal policy materials. SMAS Islam Nurul Amanah Class XI is the subject of this study. In this study, a type of development research (Development Research) is used. The development model in this study uses the ADDIE development model. This model consists of five main phases or stages, namely Analysis, Design, Development, Implementation, and Evaluation. In this study, quantitative and qualitative methods are used. The results of the study showed that the whatsapp-based learning media with the chatbot feature was considered quite valid based on the results of a questionnaire filled out by material validators with a score of 70%, media validators with a score of 78%, and obtained a very valid assessment from economic learning practitioners with a score of 87%. Based on the results of validation and field trials, whatsapp-based learning media with a chatbot feature that has been developed by researchers is feasible to be used in the learning process in economics lessons, especially in monetary and fiscal policy material for grade XI of SMAS Islam Nurul Amanah.

Cultural adaptation and validation of the Sinhala version of the spiritual needs assessment for patients (S-SNAP) questionnaire

BackgroundSpiritual support for patients and caregivers of critically ill patients is associated with improved quality of life. This aspect, however, is not incorporated into the current care pathways in Sri Lanka. The Spiritual Needs Assessment for Patients (SNAP) questionnaire, comprised of 3 domains: psychosocial, spiritual and religious, gives a platform for clinicians to assess the spiritual needs of those patients. This study presents the results of validation of the Sinhala version of the SNAP (S-SNAP) questionnaire.MethodsThe SNAP was translated from English to Sinhala using the standard forward and backward translation process. After verifying the content validity, unambiguity and clarity of items in a focused group discussion, and a pilot study, the pre-final version was tested among 267 volunteers with cancer selected from three state-run cancer care institutions. Data were analysed for internal consistency and item-total correlations. Factor analysis was done using Varimax rotation with Kaiser normalization. A Scree plot was also made to determine the number of factors.ResultsThe mean (SD) age of subjects was 63.2 (11.4) years. The total S-SNAP score ranged from 22 to 88 (maximum 88). The overall Cronbach’s alpha was 0.94 while item-total correlations varied from 0.26 to 0.87. Total SNAP score showed inverse correlations with age, Charleson Comorbidity index and Barthel index while a positive correlation was seen with the Karnofsky performance status scale (p < 0.05). Kaiser-Meyer-Olkein value of 0.92 (P = < 0.001) for Bartlett’s test indicated adequate sampling and non-linearity of factors. The scree plot showed a four-factor structure explaining 76% variation. Meaning of life and relationship with a supernatural being and religious rituals are loaded as 2 different factors. Worries, fears and forgiveness are grouped as the third factor while relaxation, coping and sharing feelings are loaded separately.ConclusionsThe S-SNAP is a reliable and valid tool to assess spiritual suffering among patients with cancers conversant in the Sinhala language.

Umami-gcForest: Construction of a predictive model for umami peptides based on deep forest

Umami peptides have recently gained attention for their ability to enhance umami flavor, reduce salt content, and provide nutritional benefits. However, traditional wet laboratory methods to identify them are time-consuming, laborious, and costly. Therefore, we developed the Umami-gcForest model using the deep forest algorithm. It constructs amino acid feature matrices using ProtBERT, amino acid composition, composition-transition-distribution, and pseudo amino acid composition, applying mutual information for feature selection to optimize dimensions. Compared to other machine learning baseline, umami peptide prediction, and composite models, the validation results of Umami-gcForest on different test sets demonstrated outstanding predictive accuracy. Using SHapley Additive exPlanations to calculate feature contributions, we found that the key features of Umami-gcForest were hydrophobicity, charge, and polarity. Based on this, an online platform was developed to facilitate its user application. In conclusion, Umami-gcForest serves as a powerful tool, providing a solid foundation for the efficient and accurate screening of umami peptides.

Comparison of K-Means and FCM Algorithms to Optimize Spatiotemporal Pore Pressure Prediction of Earth Dams

To identify the behavior and health monitoring of dams, it is necessary to correctly interpret the results of instrumentation in different phases of construction, impounding, and operation. Therefore, the approach based on spatiotemporal prediction is presented to improve the interpretation of pore pressure behavior of Eyvashan Earth Dam. In this research, using the results of other existing healthy piezometers, a spatiotemporal distribution model is proposed using panel data, which can be effective for predicting and reconstructing missing data. The optimal spatiotemporal clustering of pore pressure changes monitoring with K-Means and Fuzzy C-means (FCM) algorithms will enable the monitoring of points of the dam where instrumentations are not designed and installed or defective instrumentations. In predicting the pore pressure of dams, the input data is classified based on the pore pressure monitoring data, but with the use of clustering algorithms, the classification after the cluster analysis steps will lead to the proper resolution of the pore pressure clustering. According to the validation results of each of the clustering algorithms, the FCM clustering algorithm has more suitable results than the K-Means algorithm in determining the pore pressure clusters. In general, FCM clustering and K-Means algorithms are suitable and efficient tools in the field of more accurate monitoring of earth dams, and by using the proposed method, the detection of unusual areas of pore pressure and the related safety diagnosis is facilitated.

An Adaptive Combined Method for Lithium‐Ion Battery State of Charge Estimation Using Long Short‐Term Memory Network and Unscented Kalman Filter Considering Battery Aging

AbstractThe accurate estimation of battery state of charge (SOC) enables the reliable and safe operation of lithium‐ion batteries. Data‐driven SOC estimation is considered an emerging and effective solution. However, existing data‐driven SOC estimation methods typically involve direct estimation and lack effective feedback correction. Moreover, battery degradation poses additional challenges to accurate SOC estimation. Therefore, this study proposes an adaptive combined method for battery SOC estimation based on a long short‐term memory (LSTM) network and unscented Kalman filter (UKF) algorithm considering battery aging status. First, an LSTM model is constructed to characterize the battery's dynamic performance instead of traditional battery models. Then, the UKF algorithm is employed to perform SOC estimation through the feedback of terminal voltage prediction. To enhance estimation accuracy under different aging statuses, a proportional‐integral‐derivative controller is employed to correct the capacity fading during the SOC estimation process. Validation results indicate that the terminal voltage prediction model demonstrates exceptional robustness against interference from current and voltage noise. Compared to the traditional estimation method combining the deep learning model and Kalman filter algorithm, the proposed method demonstrates superior estimation accuracy under various complex operating conditions. Furthermore, the proposed method outperforms the traditional method in estimation performance during battery aging.

Development of electronic students’ worksheets based on problem-based learning on air pollution material

This research is development research with the aim to produce teaching material products in the form of PBL-based electronic student worksheets on air pollution sub-materials that will be used during the learning process, so that students can be more active and independent. This type of research is development research using the ADDIE model (analysis, design, development, implementation, evaluation), but this research is limited to the development stage. This research was conducted at State Senior High Schools in Palembang in the 2023/2024 school year. The sample in this study was class X students totaling 12 people. Sampling in this study using purposive sampling technique. electronic student worksheets are designed using the Canva and liveworksheets applications; after completing the design of electronic student worksheets, they are assessed by material and media validators using an instrument in the form of a validation questionnaire. After the electronic student worksheet is declared valid, the electronic student worksheet is applied to students in a small group trial. Based on the validation results with the Kappa calculation, the electronic student worksheets were declared valid with a Kappa value of 1. Electronic student worksheets also fall into the very practical category based on the teacher's practicality questionnaire which obtained a percentage of 100%, while the students' practicality questionnaire obtained an average percentage of 90.68%. Based on the results of the validation and practicality questionnaires of teachers and students, it can be concluded that PBL-based electronic student worksheets are valid and feasible to use during the learning process on air pollution sub-materials.

Development of Canva-Based Science Infographic Learning Media to Improve Students’ Learning Outcomes

The aim of this research is to develop, test the feasibility, and effectiveness of Canva-based infographic learning media. This type of research and development (R&amp;D) development research with the ADDIE development model, namely analysis, design, development, implementation, and evaluation). The research subjects were fourth grade students totaling 25 students, data collection techniques with observation, interviews, questionnaires, and documentation data. The results of media expert validation show a percentage value of 88.3% very feasible category, material experts with a percentage value of 86.66% very feasible category. Based on the pretest-posttest results, it is known that Canva-based infographic media is effective in improving student learning outcomes, which is indicated by an increase in the average pretest score from 52 to 80 on the posttest, with an N-Gain test result of 0.58 in the moderate category. Based on the response questionnaire distributed, a very positive response was obtained from teachers and students. From these results, it can be concluded that Canva-based infographic learning media is effective in improving student learning outcomes and is feasible and practical to use in IPAS learning on respiratory system material in grade V SD Negeri Tambakaji 05.

The development of elema 2.0 for the invertebrate zoology course on the phylum arthropoda topic

Currently, technology has become an inseparable part of education and learning. Various digital learning tools have been developed and widely used in the classroom. eLEMA (electronic learning materials) was developed as a mobile application that supports learning in the Invertebrate Zoology courses at the Biology Education Program, University of Palangka Raya. At that time, eLEMA was developed to meet the need for electronic learning materials for online learning during the COVID-19 pandemic. Along with the rapid development of technology in recent years, eLEMA needs to be developed further for updates. This research aims to develop eLEMA 2.0 for the Invertebrate Zoology course. This study is a research and development (R &amp; D) using Hannafin &amp; Peck development model to develop eLEMA 2.0 for the Invertebrate Zoology course on the Phylum Arthropoda topic. The development product is the eLEMA 2.0 application for lectures on the Phylum Arthropoda topic in the Invertebrate Zoology course with improvements to the user interface and additional features that support participatory learning and student-centered learning. The results of trials and validation show that all pages and content in this application were successfully opened without any disturbing errors. These results indicate that the development product is feasible to be implemented in the classroom.