Application Of Learning Model Research Articles

Penerapan Model Problem Based Learning dalam Pembelajaran Logika dan Ushul Fiqh

Model learning is very much, including the problem-based learning model. The problem-based learning model itself is a learning model in which there is a problem-solving process carried out by students, both individually and in groups. The PBL model has been carried out by the Institute for the acceleration of Logic and Ushul Fiqh (Logical) in the study of Logic and Ushul Fiqh. This study aims to find out how the application of the PBL model in the LOGIS acceleration institution, the impacts and obstacles faced in implementing the PBL model. This is very important to be studied as an effort to develop the PBL model. The research method used is a qualitative method with a discriminatory case study type. Researchers as full participants. The subjects of this study were the head of LOGIS, Head of Education Bid, some LOGIS supervisors, and some LOGIS members. and some members of LOGIS. Data collection is carried out by interview, observation and documentation techniques. The results showed that: the application of the problem-based learning model in learning Logic and Ushul Fiqh in the Logic and Ushul Fiqh (LOGICAL) acceleration lemabaga can be said to be good. This is still not said to be perfect because there are obstacles faced in implementing the PBL model itself.

The Project Based Model Learning and Discovery Learning Towards Critical Thinking Ability Children Aged 5-6 Years

This study aims to determine (1) the influence of the project-based learning model on the critical thinking skills of 5-6-year-old children , (2) the influence of the discovery learning model on the critical thinking skills of 5-6-year-old children , and (3) the difference in influence between project-based learning and discovery learning models on the critical thinking skills of 5-6-year-old children . This research was a quasi-experimental study with a nonequivalent controls groups design . The population of this study consisted of all students aged 5-6 years in Wangi-Wangi Island, Wakatobi Regency, with a sample of 99 children selected through a purposive sampling technique . The instruments used was an observation questionnaire for children's critical thinking skills . Data analysis techniques included an independent sample t- test with a significance level of 0.05. The results of the study show that (1) there is a significant influence on the application of the project-based learning model on the critical thinking skills of 5-6-year-old children , with a t- value of -24.578 and a Sig . of 0.000 < 0.05, (2) there is a significant influence on the application of the discovery learning models on the critical thinking skills of 5-6-year-old children , with a t- value of -15,304 and Sig . of 0.000 < 0.05, and (3) there is a significant difference in the influence between the project-based learning and discovery learning (DL) models on the critical thinking skills of 5-6-year-old children , with a Sig . Value of 0.000 < 0.05.

Improving critical thinking and metacognitive skills through the discovery learning model assisted by practicum for high school students

Metacognitive abilities and critical thinking support the success of learning biology, so these skills need to be empowered. The purpose of this study is to determine the effectiveness of the practicum-based discovery learning model to improve critical and metacognitive thinking skills in digestive system material. The research method is a quasi-experimental research with a one-grub pretest-posttest design. The research was conducted from November to December 2022 at SMA Negeri 9. The research was carried out from November to December 2022 at one of the high schools in Mataram City. The research population was 98 students of class XI Science. The research sampling technique is a purposive sampling technique. The data collection technique used a test technique, namely 9 critical thinking essay questions and MAI. The instruments used in this study were the ability to think critically about the digestive system with several questions and the MAI instrument. The data analysis technique uses inferential statistics using the Paired Sample T-Test Assisted by SPSS IBM 27 software. The results of the study show that with an alpha of 5%, the practicum-based discovery learning model is effective for increasing critical thinking skills and metacognitive abilities. The average pre-test of critical thinking skills on the digestive system material was 54.0333 and the post-test was 75.8667. While the average pre-test of metacognitive abilities is 73.37 and the post-test of metacognitive abilities is 76.93 The application of the practicum-based discovery learning model effectively improves students' critical thinking and metacognitive skills with an alpha significance level of 5%.

Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0

Abstract. Warm-sector heavy rainfall along the south China coast poses significant forecasting challenges due to its localized nature and prolonged duration. To improve the prediction of such high-impact weather events, high-resolution numerical weather prediction (NWP) models are increasingly used to more accurately represent topographic effects. However, as these models' grid spacing approaches the scale of convective processes, they enter a “gray zone”, where the models struggle to fully resolve the turbulent eddies within the atmospheric boundary layer, necessitating partial parameterization. The appropriateness of applying convection parameterization (CP) schemes within this gray zone remains controversial. To address this, scale-aware CP schemes have been developed to improve the representation of convective transport. Among these, the multi-scale Kain–Fritsch (MSKF) scheme enhances the traditional Kain–Fritsch (KF) scheme, incorporating modifications that facilitate its effective application at spatial resolutions as high as 2 km. In recent years, there has been an increase in the application of machine learning (ML) models across various domains of atmospheric sciences, including efforts to replace conventional physical parameterizations with ML models. This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme. This multi-output Bi-LSTM model is capable of simultaneously predicting the convection trigger while also modeling the associated convective tendencies and precipitation rates with a high performance. Data for training and testing the model are generated using the Weather Research and Forecast (WRF) model over south China at a horizontal resolution of 5 km. Furthermore, this work evaluates the performance of the WRF model coupled with the ML-based CP scheme against simulations with the traditional MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the promising potential of ML models to substitute the MSKF scheme in the gray zone.

The influence of the Think Pair Share model assisted by Quizizz learning media on results studying social studies

Education That must walk For guard sustainability life human, because without education No will There is transformation knowledge as well as social values and norms from generation old to generation young. Based on exposure the can stated that function education is a very important process and not Can free from life man. Formulation problem How application of the Think Pair Share learning model, How use of learning media interactive Quizizz, How results Study students ' eyes social studies lessons, Is There is the influence of the Think Pair Share model assisted by Quizizz learning media on results studying social studies in Class IV Cluster II Sombori Island Regency Morowali. Research purposes Analyze the influence of the Think Pair Share model assisted by Quizizz learning media on results studying social studies in Class IV Cluster II Sombori Island Regency Morowali. Types of research This is quantitative. Study quantitative can interpreted as method Research based on philosophy​ positivism, used For research on populations or sample certain. According to Sugiyono (2022) method Study experiment is method Research conducted​ with experiment, which is used For know influence variable independent ( treatment ) towards variable dependent ( result ) in controlled conditions.​ Research result Something instrument is said to be valid if instrument the can measure what it should be be measured. Validation process instrument study This carried out by expert validators.

Design of Group Investigation (GI) Learning Model assisted by animation media to social skills in social studies learning

Education is an important capital​ For advance A nation Because prosperity and progress A nation can seen from level his education. Education holds role important in create individual quality. Education requires appropriate innovations​ with progress knowledge knowledge and technology without ignore values humanity. Formulation problem How application of the Group Investigation learning model, How use of animation media on the eyes social studies lessons in class V, How Skills social students in social studies subjects in class V, Why Skills social become interesting use design a form of Group Investigation (GI) learning model assisted by animation media in social studies learning in class V at SDN Atananga. Research objectives, Analyze design a form of Group Investigation (GI) learning model assisted by animation media to social skills in social studies learning in class V of SDN Tananga. Research methods used by researchers​ namely research methods qualitative Study qualitative addressed For understand phenomenon social perspective participants which are the people invited interviewed, observed, asked provide data, opinions, thoughts, perceptions. Animation media research results learning is a medium that contains gathering created image​ like that appearance so that produce movement and equipped with interesting audio as well as contain values learning. With use proper animation, material​ difficult learning​ or abstract can be delivered with way more​ easy understood by students. Animation is also possible present information in a way interactive, possible student participate active in the learning process.

Analysis of the Snowball Throwing Learning Model Assisted by Canva Media on Social Studies Learning Outcomes

Education in the era of globalization   This   is   very important thing   in prepare Quality Human Resources​​ To use ensure sustainability development something nation. Formulation problem How application of the Snowball Throwing learning model, How How to use Canva media results Study students ' eyes social studies lesson in class IV, Is it There is the influence of the Snowball Throwing learning model assisted by Canva media on results studying social studies in Class IV of SDN 2 Bahonsuai. Research purposes Analyze the influence of the assisted Snowball Throwing learning model canva to results Study student eye social studies lessons in class IV of SDN 2 Bahonsuai. Types of research This is quantitative. Study quantitative can interpreted as method research based on philosophy​ positivism, used For research on populations or sample certain. Population and sample This aimed at 2 different classes as object study experiment. Research results Reliability test results refers to the extent of a test can produce consistent and reliable results​​​ from time to time.

Data Mining Strategies for Gold Price Prediction Using Multi-Factorial Influences

The prediction of gold prices is a critical endeavor in the realm of financial markets, given the precious metal's significance as a store of value and its susceptibility to various economic, geopolitical, and market influences. This research focuses on the application of machine learning (ML) models to predict gold rates by incorporating factors such as the dollar exchange rate, crude oil prices, and element prices. The objective is to utilize advanced ML algorithms, including regression, decision trees, and ensemble methods, to analyze and interpret the complex relationships between these key factors and gold prices. Through the use of robust ML models and historical data, the study aims to improve the accuracy of gold rate predictions. Additionally, the research seeks to explore the importance of features and interactions among variables to develop comprehensive predictive models. The utilization of ML techniques in this context is expected to provide a deeper understanding of the dynamic influences of the dollar exchange rate, crude oil prices, and element prices on gold rates, offering valuable insights for investment and risk management strategies in financial markets.

Talking Stick Learning Model Assisted by Flashcard Media: Improving Mufradat Learning Outcomes

This research aimed to determine the application of the talk stick learning model assisted by flash card media and to determine the level of effectiveness of the talk stick learning model assisted by flash card media in improving mufradat learning outcomes. The study employed a quantitative approach using a one-group pretest-posttest design model. Data were gathered through three methods: (1) watching teacher and student activities during the learning process; (2) using the talking stick model to test what to do before and after; and (3) documenting by gathering research-related documents. The normalcy distribution was used to calculate the One-Sample Shapiro-Wilk Test method, paired sample tests were used to test hypotheses, and N-gain was employed for data analysis.  To simplify the process, data analysis was carried out with the help of SPSS 26. This research was conducted at MI Al-Faidizn Buko Poso, Way Serdang class V, totaling 20 students. The research results showed a significance value of 0.000 < 0.05 that Ho was rejected and Ha was accepted, and the N-gain percent value was 85.3492. So the use of the talking stick learning model assisted by flashcard media could improve mufradat learning outcomes.  

Pharmaceutical Supply Chain Management

Supply chain management (SCM) refers to the coordination and oversight of all activities involved in the production, procurement, transformation, and delivery of goods or services from the point of origin to the point of consumption. It encompasses the planning, execution, and control of various processes involved in the flow of materials, information, and finances across the entire supply chain. In this paper, the authors have done a detailed analysis of the pharmaceutical dataset and predicted useful metrics that can be used in the shipping industry. This paper aims to demonstrate the application of machine learning models on the prediction of shipping mode. The model is trained on various features of the pharmaceutical supply chain dataset such as unit price, first-line designation, and delivery dates. Data cleaning and feature engineering were done and multiple models were trained. Out of the models tested, XGboost was found to give the best results for the prediction of weight, shipment modes and delivery date. Key Words: Supply chain management, Machine Learning models, Shipping Industry, XG Boost

Penerapan Strategi Pembelajaran Mind Map Untuk Meningkatkan Hasil Dan Aktivitas Belajar Siswa Pada Materi Pokok Struktur Dan Fungsi Jaringan Tumbuhan Di Kelas VIII-A SMP Negeri 4 Jakarta

This research aims to find out whether the application of the Mind map learning model to the main topic of structure and function of tissues in plants can improve student learning outcomes. This research is a PTK carried out in two cycles, each consisting of two meetings. This is intended to obtain maximum research results.From the implementation of this PTK, findings were obtained that there was an increase in learning outcomes and student activities. This can be seen from the results of observations in cycle I and cycle II. In the first cycle, students' test scores averaged 65.79 with student activity increasing from 27% to 43%, while student learning completion was only 65.79%. In cycle II, students' test results increased to an average of 72.10, their activities also increased from 49% to 69%, while students' learning completion reached 74.38%.From the overall data and findings, it can be concluded that the application of the Jigsaw learning model to Mind map learning material on the main topic of structure and function of tissues in plants can improve the learning outcomes of class VIII.A students at SMP Negeri 4 Jakarta.

Identifying autism spectrum disorder from multi-modal data with privacy-preserving

The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models. However, data privacy protection mechanisms make it difficult to perform medical data collection from different medical institutions. In autism spectrum disorder (ASD) diagnosis, automatic diagnosis using multimodal information from heterogeneous data has not yet achieved satisfactory performance. To address the privacy preservation issue as well as to improve ASD diagnosis, we propose a deep learning framework using multimodal feature fusion and hypergraph neural networks for disease prediction in federated learning (FedHNN). By introducing the federated learning strategy, each local model is trained and computed independently in a distributed manner without data sharing, allowing rapid scaling of medical datasets to achieve robust and scalable deep learning predictive models. To further improve the performance with privacy preservation, we improve the hypergraph model for multimodal fusion to make it suitable for autism spectrum disorder (ASD) diagnosis tasks by capturing the complementarity and correlation between modalities through a hypergraph fusion strategy. The results demonstrate that our proposed federated learning-based prediction model is superior to all local models and outperforms other deep learning models. Overall, our proposed FedHNN has good results in the work of using multi-site data to improve the performance of ASD identification.

Peningkatan Berpikir Kritis Siswa Dengan Model Pembelajaran Problem Based Learning

The purpose of this study was (1) to find out the application of the problem-based Learning Learning model to improve critical thinking skills and (2) to find out the improvement of student critical thinking skills through the application of the problem-based learning model to improve students thinking skills critical of class X IPS SMA PGRI Swasthika Lewolebe. This type of research was classroom action research (PTK), intended to improve classroom learning. The subjects of this study were students of grade X IPS SMA PGRI Swasthika Lewoleba, with 24 students. The method used in this study was a test. The research data were analyzed using quantitative analysis data. The study results can conclude that problem-based learning models can increase students' critical thinking skills at SMA PGRI Swasthika Lewoleba. Think can be proven by improving student learning outcomes in each cycle; it was very good, with a percentage of 29.16 % in the category good; in the first cycle, the rate increased to 37.5% in the good category, and in the second cycle it was very good with a percentage of 95.8% in the very good category. These results have achieved the success criteria set by the researcher, namely 80% above KKM 75.

Application of the RADEC Learning Model through the Experimental Method on Students' Critical Thinking Ability in Natural Science Learning Animal Movement Organ Material Class V SDN 13 Singkawang

This research aims to: 1) To describe the differences between the RADEC learning model through the experimental method on the critical thinking abilities of students who study with the RADEC learning model and classes that use direct learning in class V science learning at SDN 13 Singkawang; 2) To describe the implementation of the RADEC learning model through the experimental method on the critical thinking abilities of class V students at SDN 13 Singkawang in science learning; 3) To describe students' responses to the RADEC learning model through experimental methods on students' critical thinking abilities. This research is a quantitative experimental research. This research method is a quasi experimental design, with a post-test only control group design. The population of this study was all fifth grade students, totaling 50 students. Samples were taken using probability sampling techniques with saturated sampling. Data collection techniques use test and non-test techniques. The test instrument is a critical thinking ability question sheet in the form of an essay. Meanwhile, non-tests are in the form of model implementation observation sheets and student response questionnaire sheets. The data analysis technique used was a two-sample t test. The results of the research, 1) show that tcount = 3.844 ttable = 2.010, which means that there is a significant difference in the results of students' critical thinking ability tests between students who applied the RADEC model through the experimental method and students who used the direct learning model. 2) The results of the model implementation analysis show a percentage of 90.5%, very good criteria and 3) the results of the analysis of student response questionnaires show a percentage of 75% good criteria, which means that learning using the RADEC learning model through the experimental method in science learning has an effect on critical thinking skills. students' science, so that students respond well to learning activities. So it can be concluded that the RADEC learning model during the learning process makes an impression, attracts students' attention, and provides a new learning atmosphere

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design.

Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.

Development and Application of Machine Learning Models for Prediction of Soil Available Cadmium Based on Soil Properties and Climate Features

Identifying the key influencing factors in soil available cadmium (Cd) is crucial for preventing the Cd accumulation in the food chain. However, current experimental methods and traditional prediction models for assessing available Cd are time-consuming and ineffective. In this study, machine learning (ML) models were developed to investigate the intricate interactions among soil properties, climate features, and available Cd, aiming to identify the key influencing factors. The optimal model was obtained through a combination of stratified sampling, Bayesian optimization, and 10-fold cross-validation. It was further explained through the utilization of permutation feature importance, 2D partial dependence plot, and 3D interaction plot. The findings revealed that pH, surface pressure, sensible heat net flux and organic matter content significantly influenced the Cd accumulation in the soil. By utilizing historical soil surveys and climate change data from China, this study predicted the spatial distribution trend of available Cd in the Chinese region, highlighting the primary areas with heightened Cd activity. These areas were primarily located in the eastern, southern, central, and northeastern China. This study introduces a novel methodology for comprehending the process of available Cd accumulation in soil. Furthermore, it provides recommendations and directions for the remediation and control of soil Cd pollution.

Advanced hybrid numerical-machine learning computational study on fluid flow modeling in magnetic nanocarriers for targeted drug delivery

Development of comprehensive models for simulation of blood flow containing magnetic nanoparticles is of fundamental importance due to its application in medical science and cancer treatment. Owing to the complexity of the process, advanced hybrid models are preferred over conventional models. The focus of this study is on the application of machine learning models for predicting velocity in a given dataset to study the fluid flow in vessel containing magnetic nanocarrier for targeted cancer therapy. The numerical model was performed to simulate the blood flow through vessel, while the output data was used in developing machine learning models. The dataset is utilized to train three distinct models: Random Forest, Extra Tree, and AdaBoost Decision Tree. The hyper-parameter optimization is carried out via the Whale Optimization Algorithm (WOA) to improve the models' predictive capabilities. The dataset encompasses more than 17 thousand rows of data. The findings demonstrate the effectiveness of the suggested methodology, as evidenced by the impressive performance metrics exhibited by each model. The Random Forest model achieved a high R-squared value of 0.98768, indicating a robust fit to the data. The Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) for Random Forest were recorded at 6.1223E-04 and 6.09246, respectively. Similarly, the Extra Tree model exhibited superior predictive accuracy with a score of 0.99151 with R-squared metric, showcasing its skill in uncovering the fundamental patterns present in the dataset. The associated RMSE and MAPE for Extra Tree were notably low at 5.1682E-04 and 1.20955E-01, respectively. The AdaBoost Decision Tree model exhibited notable efficacy, as evidenced by its R-squared score of 0.96944, albeit marginally lower than the other models. The RMSE and MAPE for AdaBoost DT were reported at 9.1863E-04 and 8.06333, respectively.

Problem-Based Learning and Flipped Classroom: Can it Improve Student Achievement?

The low learning achievement of students is still a problem in the world of education. The application of learning models by less innovative teachers is the main problem. This study aims to examine: (1) the difference in the effect of the combined learning model of Problem-Based Learning with Flipped Classroom and the Problem-Based Learning learning model on student achievement; (2) differences in the effect of academic ability on student achievement; and (3) the effect of the interaction between learning models and academic abilities on student achievement. The sampling technique used a cluster random sampling system. The instrument used was a test which includes a pretest and posttest which functions to determine the level of students' critical thinking skills and academic abilities. This study used an experimental method with a 2x2 factorial design so that four learning groups were formed. The data analysis technique used the Two-Way ANOVA test with unequal cell frequencies. The results of the study show that: (1) there were differences in the effect of the combined learning model of Problem-Based Learning with Flipped Classroom and Problem-Based Learning learning models on student achievement; (2) there were differences in the effect of academic ability on student achievement; and (3) there was no interaction effect between learning models and academic abilities on student achievement. This research is expected to be able to overcome the problem of low student achievement through the application of innovative learning models that can develop students' academic abilities.

Real-Time Violence Detection in Surveillance Videos Using Deep Learning Approach

Abstract: In order to tackle the ongoing problem of keeping fruits fresh, this research presents an automated freshness detection. The ever-growing presence of surveillance cameras has undoubtedly enhanced public safety. However, manually reviewing vast quantities of video footage for violence detection remains a tedious and error-prone task. This paper proposes a deep learningbased approach for anomaly detection in surveillance videos, with a specific focus on identifying violent activities. Traditional surveillance methods often lack the sophistication to distinguish between normal behaviour and potentially threatening actions. Deep learning offers a significant advantage by automating the identification of deviations from established behavioural patterns in video data. This automation enables real-time analysis of footage, potentially signifying the occurrence of violent incidents and allowing for a swifter response from security personnel. This research delves into the application of deep learning models for violence detection in surveillance footage. We begin by discussing the limitations inherent in conventional methods, such as motion detection and manual review, which struggle to capture the nuances of human behaviour. Subsequently, we explore the advantages that deep learning-based approaches bring to the table, including their ability to learn complex patterns from large datasets and identify subtle anomalies that might escape human observation.

Revolutionizing the cultural industry: The transformative power of data processing algorithms

This paper provides a comprehensive analysis of the impact of data processing algorithms on the cultural industry, focusing on user behavior analysis, dynamic content adaptation, content discovery, efficient distribution networks, and driving creative innovation. Through the application of sophisticated machine learning models and quantitative analysis, this study demonstrates the transformative potential of algorithms in enhancing user engagement, optimizing distribution channels, and fostering creative innovation. By analyzing vast datasets from leading cultural content platforms, social media, and collaborative creation platforms, the paper reveals significant improvements in user consumption rates, engagement metrics, global market penetration, and the development of novel content formats. The findings underscore the importance of algorithmic insights in shaping the future of the cultural industry, offering personalized experiences, streamlining distribution, and expanding creative horizons.