Validation Of Results Research Articles

Pengembangan Media Pembelajaran FINTER (Fikih Interaktif) untuk Pembelajaran Fikih di MTs Bahrul Ulum Tambakberas Jombang

Rapid developments are taking place in the realm of education in the 21st century. Teachers in this era are expected to have the ability to use technology in teaching at school and also be able to overcome various challenges that arise. To increase the effectiveness of learning, it is necessary to use interesting media. This research is entitled the development of FINTER Learning media (Interactive Fikih) for learning Fikih in class VIII MTs Bahrul Ulum Tambakberas Jombang. This research focuses on the development of Powerpoint-based Fikih learning media for Fikih subjects in class VIII MTs Bahrul Ulum Tambakberas Jombang. This research applies the research and development (R&D) method using the ADDIE model. The purpose of this research is to facilitate and make learning Jurisprudence subjects more interesting for teachers and students. This research produces learning media called FINTER. This media received a score of 88% from student responses. The results of the material expert validation received a score of 89% and 91% for media expert validation, thus FINTER media is very feasible to use in learning media at MTs Bahrul Ulum Tambakberas Jombang.

Development of Website-Based Learning Media for Listening Skills for Puppet Story Materials at the Junior High School Level

This study aims to design, develop, and evaluate website-based Javanese language learning media that are designed innovatively, interactively, and easily accessible to students and teachers. Another objective is to ensure that this learning media is in accordance with user needs and supports an effective learning process. The type of research used is research and development (R&D) based on the theory put forward by Sugiono, which includes systematic steps in designing and testing learning products. Data collection was carried out through questionnaires distributed to students and teachers to identify their needs, where the results were analyzed using qualitative and quantitative descriptive approaches. Product validation was carried out by several parties, including students as the primary users, media validators to assess the quality of design and interactivity, and material validators to assess the accuracy of learning content. Based on the validation results, media experts and material experts assessed this product as "very feasible" to use in learning. In addition, the results of the trial conducted on students provided positive feedback by stating that this learning media was also "very feasible". Thus, this website-based learning media is useful for improving students' listening skills, especially in the wayang story material in Javanese language subjects.

Developing Reading Material Based on Local Culture at SMP Negeri 32 Medan

Integrating local culture into the process of learning a foreign language has only recently begun to draw attention. Therefore, local culture-based English reading materials are a solution to attract students' interest in reading English. The objectives of this research are to develop the students’ English reading materials based on local culture at SMPN 32 Medan, to find out the validation of English reading material based on local culture at SMPN 32 Medan, and to explain the effectiveness of English reading material based on local culture at SMPN 32 Medan. This research adapted the R&D model proposed by Sugiyono (2013) with some modifications. The data was taken from students and teachers of SMPN 32 Medan and also expert judgment from Universitas Muslim Nusantara Al Washliyah Medan. The instruments of this research are questionnaire, documentation and test. The result of validation from the material expert judgment is 91.66% and the material expert judgment is 85.71%. It means that the product in very high category. The result of validation from the teacher is also high category, 88.63% . In addition, the product was also tested on students and the results showed a significant increase in scores after the students learned using the local culture-based reading materials. Unit 1 increased up to 10.36%, Unit 2 increased up to 12.15%, Unit 3 increased up to 14.06% and Unit 4 increased up to 15.26%. The average score of students, which was previously the highest at 78, increased to 89. The conclusion based on the research results shows that these English reading materials can be adopted as teaching materials as additional learning for English lessons that are interesting, contextual, and adapted to the Merdeka curriculum.

Construction and Optimization of Integrated Yield Prediction Model Based on Phenotypic Characteristics of Rice Grown in Small–Scale Plantations

An intelligent prediction model for rice yield in small-scale cultivation areas can provide precise forecasting results for farmers, rice planting enterprises, and researchers, holding significant importance for agricultural industries and crop science research within small regions. Although machine learning can handle complex nonlinear problems to enhance prediction accuracy, further improvements in models are still needed to accurately predict rice yields in small areas facing complex planting environments, thereby enhancing model performance. This study employs four rice phenotypic traits, namely, panicle angle, panicle length, total branch length, and grain number, along with seven machine learning methods—multiple linear regression, support vector machine, MLP, random forest, GBR, XGBoost, and LightGBM—to construct a yield prediction model group. Subsequently, the top three models with the best performance in individual model predictions are integrated using voting and stacking ensemble methods to obtain the optimal integrated model. Finally, the impact of different rice phenotypic traits on the performance of the stacked ensemble model is explored. Experimental results indicate that the random forest model performs best after individual machine learning modeling, with RMSE, R2, and MAPE values of 0.2777, 0.9062, and 17.04%, respectively. After model integration, Stacking–3m demonstrates the best performance, with RMSE, R2, and MAPE values of 0.2483, 0.9250, and 6.90%, respectively. Compared to the performance after random forest modeling, the RMSE decreased by 10.58%, R2 increased by 1.88%, and MAPE decreased by 0.76%, indicating improved model performance after stacking ensemble. The Stacking–3m model, which demonstrated the best comprehensive evaluation metrics, was selected for model validation, and the validation results were satisfactory, with MAE, R2, and MAPE values of 8.3384, 0.9285, and 0.2689, respectively. The above research findings demonstrate that this integrated model possesses high practical value and fills a gap in precise yield prediction for small-scale rice cultivation in the Yunnan Plateau region.

Pengembangan Bahan Ajar Menulis Teks Eksplanasi Berbasis Kontekstual dengan Menggunakan Animasi

Writing skills are one of the most difficult skills to do. A change is needed to stimulate students to be interested in writing. Animation is expected to be a solution in making it easier for students to think.This study aims to 1) determine the needs of students for teaching material products to write contextual-based explanations using animation by utilizing web animaker 2) design teaching material models for writing contextual-based explanations using web animaker animations 3) implementing contextual-based explanatory writing teaching materials using web animaker animations 4) analyzing the results of expert validation and feasibility tests for teaching materials writing contextual-based explanations using web animaker animations. This method use R&D. This research and development refers to the ADDIE development model. The result can improved students. The feasibility test results of teaching materials received an average of 3.8 with a percentage of 95% in the “Very Decent” category and an average score from media experts of 3.75 or a percentage of 94% in the “Very Decent” category. The results of Indonesian teacher feedback at MA Annajah Petukangan South Jakarta obtained an average of 3.6 with a percentage of 90% thus it can be categorized as “Very Feasible”. Small-scale trial 38 students obtained an average of 3.89 and a percentage of 97.4%. Thus, based on a likert scale of 3.21 to 4, it can be categorized as “Very Decent”.

Deep guided wave convolution neural network committee-based multi-path fusion diagnosis method for fatigue corner crack

Accurate diagnosis of crack size is a critical task for guided wave (GW)-based structural health monitoring (SHM). However, fatigue cracks would have complex morphology due to complex structural geometries and loading conditions, in which multiple dimension characteristics, like crack length, depth, and angle are involved. It is challenging to quantitatively evaluate these characteristics with GW signals from a single excitation-sensing path. This paper proposes a novel deep guided wave convolution neural network (CNN) committee-based multi-path GW fusion diagnosis method, aiming at quantitative evaluation of dimension characteristics of the complex fatigue damage. GW signals from multiple excitation-sensing paths are synthesized as a high-dimension input image to enhance the effects of the fatigue crack. Besides, the deep GW-CNN committee is developed for damage quantification, in which each GW-CNN is trained with a portion of the training dataset to reduce the impact of small sample size. The proposed method is validated on fatigue tests of landing gear beam specimens under variable amplitude loading, which is designed referring to the critical region of a real aircraft and its fatigue crack presents as a corner crack. The leave-one-out validation results show the effectiveness of the proposed method, especially improvements in the diagnosis of small cracks.

Development of Mathematics Learning Tools Using Hopscotch Games to Enhance Students' Creative Thinking in the Topic of Plane Figures' Area

The creative thinking ability of students in mathematics remains very low in practice. Creative thinking skills can be enhanced through contextual mathematics learning by connecting mathematics to everyday life, and one of its examples is the use of hopscotch games. However, in reality, there are no existing learning tools that incorporate games to improve students’ creative thinking skills. This study is a developmental research aimed at designing mathematics learning tools with hopscotch games on the topic of the area of plane figures, aligned with the Merdeka Curriculum. The development model used is the 4D model. Data collection techniques include observation, tests, and questionnaires. Validity is determined through the validation results of teaching modules, student worksheets and test packages. Practicality is assessed from observations on the implementation of the learning tools, while effectiveness is measured by learning mastery, N-Gain category, student response questionnaires, and statistical tests. The research results indicate that the developed learning tools meet the criteria of being valid, practical, and effective. The validity coefficients for the teaching module, student worksheets and test packages are 3.38, 3.31, and 3.43. The observation results for the implementation of the learning tools achieved a score of 3.83, categorized as high. The classical mastery percentage reached 76.5%, with 100% of students in at least the medium N-Gain category. Students’ responses were positive, and there was a significant influence of the implementation of mathematics learning tools with hopscotch games on improving students’ creative thinking skills. Therefore, these learning tools are deemed feasible and effective for enhancing students’ creative thinking abilities through hopscotch games.

Development Of Explosion Box Media Recognizing Culinary Heritage

The objectives of this research: 1) produce a product in the form of explosion box media; 2) analyze the suitability of the explosion box media; 3) analyze the effectiveness of the explosion box media. This Development Research or RD uses Sugiyono's development model, the stages it goes through include: 1. potential and problems; 2. data collection; 3. product design; 4.design validation; 5. design revision; 6. product testing, and 7. product revision. The research subjects were media experts and material experts who acted as validators, as well as language experts, with one expert each and 30 respondents aged between 7 and 13 years who lived in Kampoeng Heritage Kajoetangan Malang. Data collection uses questionnaires and assessment sheets from material experts, language experts and media experts to evaluate how effective the explosion box media is. Produce: 1) explosion box media getting to know the Kampoeng Heritage Culinary of Kajoetangan Malang; 2) the explosion box media recognizes that the Kampoeng Heritage Kajoetangan Malang Culinary is suitable for use, this is in accordance with the results of validation by material experts who gave a score of 87.5%, media experts 88.6%, and language experts 84.3%; 3.) shows the effectiveness of the explosion box media to get to know the culinary delights of Kampoeng Heritage Kajoetangan Malang which was tested using a validity test and a reliability test which showed a result of 0.755 which means reliable.

Development of Learning Media Based on Kinemaster Pro Application in History Subjects

This study aims to develop learning media based on kinemaster pro applications that are effective and efficient. This research is a type of research that uses the Alessi Trollip development model. The purpose of this research is to develop learning media based on kinemaster pro applications that are valid and have an effective impact on history learning. The validity of the developed product is based on the results of validation conducted by several experts, namely media experts, material experts and learning design experts. The results of the validation of media experts are 3.93 with a valid category, material experts are 4.30 with a very valid category, and learning design with a value of 4.10 with a valid category. The effectiveness of the use of kinemaster pro application-based learning media can be seen in the learning outcomes of students who experienced an increase in learning outcomes by 43.61%. Furthermore, the N-gain is 0.78 which is included in the high category.

A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation

The correlation analysis between current surface cracks of structures and external loads can provide important insights into determining the structural residual bearing capacity. The classical regression assessment method based on experimental data not only relies on costly structure experiments; it also lacks interpretability. Therefore, a novel load estimation method for RC beams, based on correlation analysis between detected crack images and strain contour plots calculated by FEM, is proposed. The distinct discrepancies between crack images and strain contour figures, coupled with the stochastic nature of actual crack distributions, pose considerable challenges for load estimation tasks. Therefore, a new correlation index model is initially introduced to quantify the correlation between the two types of images in the proposed method. Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. Ultimately, the range of the optimal load level and its confidence interval are determined via statistical analysis of the load estimations under different random fields. The validation results of RC beams under four-point bending loads show that the proposed algorithm can quickly estimate load levels based on numerical simulation results, and the mean absolute percentage error (MAPE) for load estimation based solely on a single measured structural crack image is 20.68%.

Enhanced detection of atrial fibrillation in single-lead electrocardiograms using a cloud-based artificial intelligence platform.

Although smartphone-based devices have been developed to record 1-lead ECG, existing solutions for automatic atrial fibrillation (AF) detection often has poor positive predictive value. This study aimed to validate a cloud-based deep learning platform for automatic AF detection in a large cohort of patients using 1-lead ECG records. We analyzed 8,528 patients with 30-second ECG records from a single-lead handheld ECG device. Ground truth for AF presence was established through a benchmark algorithm and expert manual labeling. The Willem Artificial Intelligence (AI) platform, not trained on these ECGs, was used for automatic arrhythmia detection, including AF. A rules-based algorithm was also used for comparison. An expert cardiology committee reviewed false positives and negatives and performance metrics were computed. The AI platform achieved an accuracy of 96.1% (initial labels) and 96.4% (expert review), with sensitivities of 83.3% and 84.2%, and specificities of 97.3% and 97.6%, respectively. The positive predictive value was 75.2% and 78.0%, and the negative predictive value was 98.4%. Performance of the AI platform largely exceeded the performance of the rules-based algorithm for all metrics. The AI also detected other arrhythmias, such as premature ventricular complexes, premature atrial complexes along with 1-degree atrioventricular blocks. The result of this external validation indicates that the AI platform can match cardiologist-level accuracy in AF detection from 1-lead ECGs. Such tools are promising for AF screening and has the potential to improve accuracy in non-cardiology expert healthcare professional interpretation and trigger further tests for effective patient management.

Designing a Pocket Book to Support Healthy Living in Islamic Boarding School Communities

Background: One of the efforts to prevent health problems in students in Islamic boarding schools is by building awareness to carry out clean and healthy living behavior (PHBS). Efforts to introduce and improve students' knowledge can be done by using health education media in the form of pocket books. Objective: To produce a PHBS pocket book for students in Islamic boarding schools. Methods: This study uses the RnD approach based on the ADDIE Model (Analyze, Design, Development, Implementation, and Evaluation). The media was validated by expert media validators, language experts, public health practitioners, agency validators, and students in grades X and XI of the Ar-Rahman Islamic Boarding School. Data collection used literature studies and data were analyzed manually. Results: The development of the PHBS pocket book in Islamic Boarding Schools began with identifying potential problems and, collecting data, and information. Product design creation using the Ibis Paint X application. The product obtained validation results, namely media experts of 96% (very feasible), language experts of 92% (very feasible), public health practitioners of 99% (very feasible), validation of institutions consisting of teachers of 100% (very feasible). After receiving suggestions and comments from the validator, the product was then revised. Then the results of the product trial on the participant group were 88% (very feasible) Conclusion: The PHBS pocket book in Islamic Boarding Schools can be used as a health education media with a very feasible feasibility test. Suggestion: Pocket books can be digitized based on Android.

Experimental Validation of Virtual Torque Sensing for Wind Turbine Gearboxes Based on Strain Measurements

ABSTRACTIn efforts to reduce the operation and maintenance cost of wind turbines, there is an increasing interest to monitor key turbine quantities such as the torque load on the gearbox. Monitoring the torque paves the way for the calculation of remaining useful lifetime, leading to cost reductions through improved reliability and maintenance planning. In order to avoid expensive direct torque sensors, this paper investigates the potential of virtual torque sensing, a technique based on 3 basic components: first, a set of non‐intrusive sensors installed on the gearbox. Three groups of strain gauges on the gearbox as well an angular encoder are considered in this paper. Second is a physics‐based model, capable of predicting the response of aforementioned sensors. These models are constructed with a purposeful balance between accuracy and computational cost. Model validation and updating are performed to ensure efficient and accurate prediction of the sensor output. Finally, an augmented extended Kalman filter (AEKF) is used to combine the measured response with predictions from the model to infer the gearbox input torque. Since a key factor determining the performance of the AEKF is the tuning of the AEKF covariance matrices, multiple methods are introduced to systematically tune the covariance matrices. Experimental validation results show that the virtual torque sensor can detect the load torque with a normalized mean absolute error (NMAE) between 3.41% and 7.47%, depending on the sensor set. The influence of the amount of sensors used and the tuning method are also investigated.

A grinding force prediction model and experimental validation for ultrasonic-assisted end grinding of 2.5D C/SiC composites

In this work, a grinding force predictive model is proposed for processing 2.5D carbon fibre-reinforced silicon carbide matrix composites (C/SiCs) using ultrasonic-assisted end grinding (UAEG). The coupling effects of process parameters on prediction results are explored, and the influence of processing parameters on forces is analyzed from the theoretical perspective. In the modelling process, the anisotropy of the 2.5D C/SiCs, the motion path of the end-face grits, and the grit-workpiece interaction stages were fully considered. According to the material’s removal properties, the interaction of the grit-workpiece was classified into different stages. The proposed model can be derived by considering the forces at different contact stages. To evaluate the model’s reliability, single-factor experiments were conducted to assess its prediction accuracy. The results of validation with experiments demonstrated that the maximum error of the model was less than 12 %, and the average error was only 6.23 %.

Shade watch: Mapping citywide shade dynamics through ray tracing and LiDAR data in Hong Kong's complex 3-D built environment

The importance of urban shade in city planning has been underscored by its various associations with thermal comfort, living environment quality, human health and well-being. However, existing methods for urban shade mapping are often limited to street-level scopes or singular sampling observational times, leaving citywide shade mapping, its spatiotemporal patterns, and controlling factors largely unexplored. To fill these knowledge gaps, this study proposed a novel protocol that enables seamless mapping of spatiotemporal urban shade patterns over the entire city by fusing look-up-table (LUT)-based ray-tracing approach with Google Earth Engine cloud computing technology, as well as high-resolution digital surface model (DSM) derived from LiDAR data. This protocol was applied to Hong Kong, a city characterized by a complex 3-D built environment, and then delved into the spatiotemporal patterns of urban shade and the associated controlling factors. Validation results indicate that the proposed method can accurately quantify urban shade compared to ground-based photo references and high-resolution satellite imagery. The analysis uncovers that: (1) Shade exhibits spatial heterogeneity across different 3-D built environments while maintaining a consistent "U-shape" diurnal shade fraction during the day; (2) Solar geometry, regardless of solar zenith and azimuth angle, is positively associated with aggregated metrics of shade landscape and negatively associated with fragmented metrics. Moreover, solar zenith angles exert stronger controls over the citywide shade landscape patterns; and (3) Persistent shade significantly reduces the sunlight hours (i.e., the accumulated sunshine hours per day) across administrative districts, local climate zones (LCZs), and seasons. Due to shade effects originating from 3-D urban structures, Hong Kong typically experiences an average of 4-8 sunlight hours per day within a one-year cycle, which is substantially lower than the 11-14 hours of natural daylength. This study offers a practical protocol for large-scale urban shade mapping, enhancing our understanding of urban shade, its spatiotemporal dynamics, and benefits in a broader spatiotemporal context. The associated datasets and findings from this study can inform urban planners and policymakers in developing effective strategies to create healthier and sustainable urban environments in Hong Kong.

Prediction of load-bearing capacity of FRP-steel composite tubed concrete columns: using explainable machine learning model with limited data

The complex interaction mechanism between the FRP jacket, steel tube, and confined concrete in the FRP-steel composite tubed concrete (F-STC) column makes the prediction of its mechanical behaviour a challenging task. This paper specifically investigates the application of machine learning models to predict the load-bearing capacity of F-STC columns. Since relatively few experimental works are performed on F-STC columns, the database established based on the test results from previous research contains only 69 data samples. In order to circumvent the training difficulties caused by the relatively small database, the application of the Gaussian process regression (GPR) model is trialled in this study. To make the predictions of the GPR model explainable, the Shapley additive explanations (SHAP) approach is incorporated with the GPR model in this study. Besides, four contrasting prediction models based on artificial neural network (ANN), support vector regression (SVR), decision tree (DT), and random forest (RF), are also proposed. The k-fold validation and test results indicate that the GPR model provides strong potential in predicting the load-bearing capacity of F-STC columns with high prediction accuracy and generalisation capability. Besides, 95% and 99% confidence intervals obtained by the GPR model are provided to show the uncertainty of the prediction results. Furthermore, the effect of the database size on the prediction performance of GPR, ANN, and SVR models is further examined by gradually reducing the number of data samples, and the comparisons illustrate the superiority of the GPR model.

Climatic impacts on spring disappearance in the Indian Himalayas

Due to climate change the drop in spring-water discharge poses a serious issue in the Himalayan region, especially in the higher section of Himachal Pradesh. This study used different climatic factors along with long-term rainfall data to understand the decreasing trend in spring-water discharge. It was determined which climate parameter was most closely correlated with spring discharge volumes using a general as well as partial correlation plot. Based on 40 years (1981–2021) of daily average rainfall data, a rainfall-runoff model was utilised to predict and assess trends in spring-water discharge using the MIKE 11 NAM hydrological model. The model’s effectiveness was effectively proved by the validation results (NSE = 0.79, R2 = 0.944, RMSE = 0.23, PBIAS = 32%). Model calibration and simulation revealed that both observed and simulated spring-water runoff decreased by almost 29%, within the past 40 years. Consequently, reduced spring-water discharge is made sensitive to the hydrological (groundwater stress, base flow, and stream water flow) and environmental entities (drinking water, evaporation, soil moisture, and evapotranspiration). This study will help researchers and policymakers to think and work on the spring disappearance and water security issues in the Himalayan region.

Development of board game respiratory smart card (restard) in human respiratory system material

The learning outcomes of Biology subjects in the Kurikulum Merdeka lead to mastery of competencies, materials, and process skills. Biological material is more abstract, teachers must be creative when using teaching media to visualize concretely. Learning media developed by researchers is board games with the name RESTARD (Respiratory Smart Card). The objectives of this study is to find out the form of development of the Board Game Respiratory Smart Card (RESTARD) in Human Respiratory System Material that is valid and feasible for biology learning. The method of the research is a Research and Development (R&D) using 4-D instructional design. The population for the practicality test was all class XI of SMAN 6 Madiun, totaling 360 students. The sample for this research was class XB with a total of 72 students. Data collection techniques were carried out using interviews, filling out validation sheets from material and teaching media experts, and filling out practicality questionnaires by students. The result of this research is that the development of the RESTARD board game on respiratory disorders has been successfully carried out. RESTARD consists of 6 main types of cards, namely organ cards, O2 cards, CO2 cards, sneezing cards, breathing cards, and sick/health cards. The validation results from media experts stated that the RESTARD card media received an average score of 93 with the criteria very valid for use. The results of the material expert validation stated that the RESTARD card media received an average score of 91 with criteria very valid for use. The practicality in small class showed that 91 score with very practicality. Result in large class show that the average of score is 94, with very practicality. The conclusion is RESTARD media is declared valid, practical and can be used to support learning of respiratory system material.

Pengembangan LKPD Biologi Materi Keanekaragaman Hayati Terintegrasi Higher Order Thinking Skills

LKPD is a teaching material product that is often used by teachers. The development of LKPD that integrates HOTS questions is needed to increase students' motivation and science process activities. This study aims to determine the validity, and feasibility of using LKPDs integrated with HOTS in class X biodiversity biology material at Al-Hamzar High School. The research method with the type of research is the development of the 4D model (Defaine the definition stage of development needs, Designe the LKPD design stage, Develop the LKPD manufacturing stage with expert validation, and Disseminate the dissemination stage through limited trials). The research instrument used hots-based LKPD and expert validation sheets. The results of the research on the validation results of teaching materials with a score range of 159.99 <X with a very feasible category and on the material validation sheet are valid with several revisions by three expert validators, namely teaching material validators, material validators and language validators. HOTS LKPD is used in the learning process to determine the extent to which students understand the biodiversity of living things.

TEXTBOOK DEVELOPMENT ON ADDITIVE AND ADDICTIVE MATERIALS USING INQUIRY BASED LEARNING STEM APPROACH IN INCREASING STUDENT SCIENCE LITERACY

The purpose of this research was to develop textbook as teaching materials on additive and addictive materials using inquiry based learning model and STEM approach in increasing students’ scientific literacy. The method of this research was development research with ADDIE model. The qualitative data came from suggestions, while the quantitave data came from likert and guttman questionnaire and also the result of SPSS data processing. The result of this research, inquiry based learning with STEM approach made students to identify scientific issues, explain scientific phenomena, and use scientific evidence in everyday life. From validation result showed a very valid category, and pretest-posttest result showed an n-gain percentage of 80.17% (very effective). It can be conclude that the implementation of textbook with the inquiry based learning model and STEM approach was effectively used in learning to increase students’ scientific literacy.