Application Of Learning Model Research Articles

Application of Deep Learning Model and Machine Learning Detection Algorithm for Gastric Cancer Patients Undergoing Chemotherapy

This work focused on exploring the application value of machine learning detection (MLD) algorithm and evidence-based nursing (EBN) in the chemotherapy (CHET) for gastric cancer (GC) patients. 100 GC patients who were treated in the Guang’an Traditional Chinese Medicine Hospital and needed postoperative CHET were recruited and randomly assigned to experimental (Exp) and control (Ctrl) groups, each including 50 patients. All participants received adjuvant CHET after gastrectomy. During CHET, participants in the Ctrl group were given routine nursing, while the experimental were given EBN in addition to routine nursing. Differences in self-rating anxiety scale (SAS), self-rating depress scale (SDS), QLQ-C30 life core questionnaire, and adverse reaction (AR) evaluation criteria were compared for participants in different groups after nursing. At the same time, all patients underwent computed tomography (CT) examination and all images were detected by MLD algorithm. After intervention, the SAS and SDS scores of patients in the Exp and Ctrl groups were 26.7±5.3 versus 33.6±6.61 and 30.07±5.58 versus 36.11±8.83, respectively. The total health status (THS) score of patients was 5.59±1.17 in Exp group and 4.53±0.96 in Ctrl group, showing P < 0.05. After intervention, great differences were observed in nausea/vomiting, decreased white blood cells (WBC), decreased haemoglobin (Hb), peripheral nerve paraesthesia (PNP), muscle and joint pain (MJP), hair loss, and other indicators between patients received EBN and routine nursing methods (P < 0.05). The MLD algorithm and EBN were of high application value in the nursing of CHET treatment for GC patients.

Utilizing artificial intelligence for precision exploration of N protein targeting phenanthridine sars-cov-2 inhibitors: A novel approach

The persistent mutation of the novel coronavirus presents a continual threat of infections and associated illnesses. While considerable research efforts have concentrated on the functional proteins of SARS-CoV-2 in the development of anti-COVID-19 therapeutics, the structural proteins, particularly the N protein, have received comparatively less attention. This study focuses on the N protein, a critical structural component of the virus, and employs advanced deep learning models, including EMPIRE and DeepFrag, to optimize the structures of phenanthridine-based compounds. More than 10,000 small molecules, derived through deep learning, underwent high-throughput virtual screening, resulting in the synthesis of 44 compounds. Compound 38 showed a binding potential energy of −8.2 kcal/mol in molecular docking. Surface Plasmon Resonance (SPR) and Microscale Thermophoresis (MST) validation yielded dissociation constants of 353 nM and 726 nM, confirming strong binding to the N protein. Compound 38 demonstrated antiviral activity in vitro and exhibited anti-COVID-19 effects by interfering with the binding of N proteins to RNA. This research underscores the potential of targeting the SARS-CoV-2 N protein for therapeutic intervention and illustrates the efficacy of deep learning model in the design of lead compounds. The application of these deep learning models represents a promising approach for accelerating the discovery and development of antiviral agents.

Advancing geological image segmentation: Deep learning approaches for rock type identification and classification

This study aims to tackle the obstacles linked with geological image segmentation by employing sophisticated deep learning techniques. Geological formations, characterized by diverse forms, sizes, textures, and colors, present a complex landscape for traditional image processing techniques. Drawing inspiration from recent advancements in image segmentation, particularly in medical imaging and object recognition, this research proposed a comprehensive methodology tailored to the specific requirements of geological image datasets. To establish the dataset, a minimum of 50 images per rock type was deemed essential, with the majority captured at the University of Las Palmas de Gran Canaria and during a field expedition to La Isla de La Palma, Spain. This dual-source approach ensures diversity in geological formations, enriching the dataset with a comprehensive range of visual characteristics. The study involves the identification of 19 distinct rock types, each documented with 50 samples, resulting in a comprehensive database containing 950 images. The methodology involves two crucial phases: initial preprocessing of the dataset, focusing on formatting and optimization, and subsequent application of deep learning models—ResNets, Inception V3, DenseNets, MobileNets V3, and EfficientNet V2 large. Preparing the dataset is crucial for improving both the quality and relevance, thereby to ensure the optimal performance of deep learning models, the dataset was preprocessed. Following this, transfer learning or more specifically fine-tuning is applied in the subsequent phase with ResNets, Inception V3, DenseNets, MobileNets V3, and EfficientNet V2 large, leveraging pre-trained models to enhance classification task performance. After fine-tuning eight deep learning models with optimal hyperparameters, including ResNet101, ResNet152, Inception-v3, DenseNet169, DenseNet201, MobileNet-v3-small, MobileNet-v3-large, and EfficientNet-v2-large, comprehensive evaluation revealed exceptional performance metrics. DenseNet201 and InceptionV3 attained the highest accuracy of 98.49% when tested on the original dataset, leading in precision, sensitivity, specificity, and F-score. Incorporating preprocessing steps further improved results, with all models exceeding 97.5% accuracy on the preprocessed dataset. In K-Fold cross-validation (k = 5), MobileNet V3 large excelled with the highest accuracy of 99.15%, followed by ResNet101 at 99.08%. Despite varying training times, models on the preprocessed dataset showed faster convergence without overfitting. Minimal misclassifications were observed, mainly among specific classes. Overall, the study's methodologies yielded remarkable results, surpassing 99% accuracy on the preprocessed dataset and in K-Fold cross-validation, affirming the efficacy in advancing rock type understanding.

Development and Application of AI Teaching and Learning Model based on Backward Design for using AI Digital Textbook

Development and Application of AI Teaching and Learning Model based on Backward Design for using AI Digital Textbook

Application of Social Simulation Learning Model to Improve Students' Speaking and Writing Skills (Study on English Subjects in Class VII SMP N 3 Bengkulu City in the 2023/2024 academic year)

This study aims to describe the writing and speaking abilities of seventh-grade students at SMPN 3 Bengkulu City as well as the effectiveness of applying the social simulation learning model to improve the writing and speaking abilities of seventh-grade students in English subjects at SMPN 3 Bengkulu City. The study used a quasi-experiment and classroom action research design. Twenty males and forty girls, totaling sixty pupils in the seventh grade at SMPN 3 Bengkulu City, served as the subjects of the class action research. All seventh grade pupils of SMPN 3 Bengkulu City made up the population of the experimental study. Class VII.1 (the experimental class) and class VII.2 (the control class) were the study's samples. Thirty students made up the experimental class, which included thirteen males and seventeen girls, and thirty students from the control class, which included sixteen boys and fourteen girls. The study's findings indicate that: using the social simulation learning model at SMPN 3 Bengkulu City in the 2023–2024 academic year has implications for improving students' speaking skills; using the social simulation learning model at SMPN 3 Bengkulu City in the 2023–2024 academic year has implications for improving students' writing skills; and using the social simulation model is effective for improving students' speaking and writing skills at SMPN 3 Bengkulu City in the 2023–2024 academic year because the results of the students' cognitive assessment have significantly increased.

Sentiment Analysis of Sirekap Tweets Using CNN Algorithm

Background: The research investigates the application of deep learning models for sentiment analysis on Twitter data related to Indonesia's Sirekap system. Sentiment analysis is crucial for understanding public opinion and enhancing the transparency and reliability of election result recapitulation processes. Objective: The objective of this study is to compare the performance of Convolutional Neural Networks (CNN) and CNN-LSTM models in analyzing sentiments from tweets about the Sirekap system. The study aims to identify the most effective model and preprocessing techniques to improve sentiment classification accuracy. Methods: A comprehensive data preprocessing pipeline was implemented, including cleansing, case folding, tokenizing, normalization, stopword removal, and stemming. To address class imbalance, the SMOTE technique was applied. The models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. Pre-trained word embeddings were used to enhance model performance. Results: The CNN model achieved an accuracy of 85.90%, outperforming the CNN-LSTM model, which achieved 79.91% accuracy. Additionally, the CNN model demonstrated superior precision, recall, and F1-score metrics compared to the CNN-LSTM model. The thorough preprocessing and handling of class imbalance significantly contributed to the enhanced performance of the CNN model. Conclusion: The research emphasizes the effectiveness of deep learning approaches, particularly CNNs, in sentiment analysis tasks. The findings highlight the importance of comprehensive preprocessing and class imbalance handling. The use of pre-trained word embeddings and various evaluation metrics ensures robust model performance. These insights contribute to improving the accuracy and efficiency of sentiment classification, thereby enhancing the reliability and transparency of election result recapitulation processes.

Advancing credit risk assessment and financial decision-making: Integrating modern techniques and insights

Credit risk assessment and fraud detection are critical functions in the financial industry, necessary for ensuring the stability and integrity of financial institutions. Traditional approaches often struggle to accurately assess risk and detect fraudulent activities in a timely manner. However, the rise of machine learning has introduced powerful tools that leverage large datasets and advanced algorithms to improve these processes. This research paper investigates the application of machine learning techniques in credit risk assessment and fraud detection within financial transactions. The paper begins by emphasizing the importance of accurate risk assessment and fraud detection in the financial sector and introduces machine learning as a solution to the limitations of traditional methods. A thorough literature review examines existing methodologies, algorithms, and trends, highlighting the advancements in this field. The discussion covers data acquisition and preprocessing techniques, underscoring the necessity of clean and relevant data for effective model training. Additionally, feature engineering strategies are explored to extract valuable insights from financial transaction data, enhancing the predictive power of machine learning models. The research analyzes various machine learning algorithms, such as logistic regression, decision trees, random forests, support vector machines, and neural networks, as well as ensemble methods. Model evaluation metrics, including accuracy, precision, recall, and ROC-AUC, are employed to assess the performance of these algorithms. The paper presents case studies and experimental results to demonstrate the practical application of machine learning models in real-world scenarios, showcasing their effectiveness in improving credit risk assessment and fraud detection. Furthermore, the research explores challenges such as imbalanced datasets, model interpretability, and regulatory compliance, offering insights into potential solutions and future research directions. The integration of behavioral finance, Bayesian networks, and optimization methods is also discussed, highlighting how these modern approaches, combined with big data analytics, can enhance predictive accuracy and decision reliability. In conclusion, this research underscores the transformative potential of machine learning in credit risk assessment and fraud detection within financial transactions. By adopting advanced algorithms and data-driven approaches, financial institutions can significantly improve their risk management strategies, mitigate potential risks, and protect against fraudulent activities. This ultimately contributes to a more secure and resilient financial ecosystem, enabling institutions to maintain a competitive edge in an ever-evolving market.

Residual energy evaluation in vortex structures: On the application of machine learning models

Vortex structures are widely employed for energy dissipation in urban surface water conveyance systems. When transporting wastewater through these networks, a substantial amount of water energy is dissipated. The effectiveness of these structures is usually evaluated by their efficiency in dissipating energy. Recent literature reviews on vortex structures have emphasized that, despite numerous experimental studies aimed at assessing their hydraulic performance, a reliable mathematical model to predict the residual energy head ratio remains elusive. In this study, resilient numerical models employing Artificial Intelligence (AI) methodologies (such as non-parametric regression, decision trees, and ensemble learning) have been structured by reliable experimental tests. By analyzing the experiments, three primary factors, referred to as flow Froude number (Fr), the ratio of sump height (Hs) to shaft diameter (D), and the ratio of drop total height (L) to shaft diameter (D) were determined to estimate the residual energy head ratio. Through experimental study, the residual energy head ratio is computed as a ratio of downstream flow energy (E2) to upstream flow energy (E1) at vortex structure. During the training and testing phases of AI models, the results of statistical tests, serving as quantitative evaluations, have shown that ensemble learning models namely Adaptive Boosting (AdaBoost) and Categorical Boosting (CatBoost) models had higher level of efficiency in the E2/E1 predictions and followed by Model Tree (MT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Multivariate Adaptive Regression Spline (MARS). Additionally, the second-order regression-based equation was obtained from Fully Factorial Method (FFM) which had lower level of precision (R = 0.8275, RMSE = 0.1156, and MAE = 0.0846) in the residual energy head ratio predictions when compared with all predictive AI models. Variations of three effective factors (i.e., Fr, L/D, Hs/D) versus the predicted E2/E1 ratios were in well agreement with observational tests. Moreover, the results of Sobol's index indicated that Fr number was determined as the most effective parameter in the evaluation of residual energy head ratio in the vortex structure.

Comparative analysis of machine learning techniques for accurate prediction of unfrozen water content in frozen soils

Unfrozen water content (UWC) plays a critical role in determining the thermal, hydraulic, and mechanical properties of frozen soils. Existing empirical, semi-empirical, and theoretical models for UWC estimation have limitations in terms of accuracy as well as generalizability. To address these challenges, the present study explored the application of six machine learning techniques to predict UWC in frozen soils: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). Considering the UWC hysteresis phenomenon between the freezing and thawing processes, experimental UWC data collected from the literature were separated into two sub-datasets: freezing branch dataset (FBD) and thawing branch dataset (TBD). Based on that, a comprehensive framework integrating Bayesian optimization and 10-fold cross-validation was established to optimize the six models' hyperparameters and to evaluate their performance. The results highlighted significant variations in the predictive capability among the six machine learning models, with ensemble methods (i.e., RF, XGBoost, LightGBM) generally demonstrating superior accuracy. Feature importance analysis, robustness checks, and uncertainty quantification further elucidated the strengths and limitations of each model. The present study provides profound insights into the selection and application of machine learning models for accurately modeling the properties of frozen soils for cold regions science and engineering.

Application of Machine Learning Models for Short-term Drought Analysis Based on Streamflow Drought Index

Application of Machine Learning Models for Short-term Drought Analysis Based on Streamflow Drought Index

Meningkatkan Softskill Siswa Melalui Model Pembelajaran Project Based Learning dalam Pembelajaran Tematik di Madrasah Ibtidaiyah

The students’ low levels of creativity and critical thinking skills are significant issues that need to be addressed in the face of the rapidly developing modern era. Therefore, an appropriate learning model is required to enhance students’ creativity and critical thinking skills over time. This study focuses on the problem related to the Project-Based Learning model and students’ soft skills. The study aims to determine how the Project-Based Learning model is applied in thematic learning and its effect on the soft skills of fifth-grade students at MI Manbaul Huda. This study employs a pre-experimental research design, specifically the pretest-posttest one-group design, with a population of 27 fifth-grade students. The sample used was saturated, and it included all 27 students. To assess the students’ creativity and critical thinking skills, the research involved administering a test in the form of essays consisting of 10 questions that had been tested for validity and reliability. Hypothesis testing in this study was conducted using a t-test. The results of the hypothesis testing indicated a significance value of 0.000. Since the significance value is less than 0.05, it can be concluded that "there is a significant influence of the application of the Project-Based Learning model in thematic learning on the soft skills of fifth-grade students at MI Manbaul Huda."

Pneumonia image classification using convolutional neural network

Throughout history, humanity has continually faced numerous unforeseen health crises, thereby emphasizing the utmost significance of harnessing cutting-edge technology and scientific approaches to proactively anticipate and tackle potential outbreaks. In the context of the COVID-19 pandemic, the utilization of CT scans has proven to be an indispensable asset in accurately detecting and diagnosing individuals afflicted with COVID-19. By virtue of their advanced imaging capabilities and ability to capture detailed internal images, CT scans have emerged as a pivotal diagnostic tool in the fight against this infectious disease. The objective of this study is to improve the precision and effectiveness of COVID-19 diagnosis by employing a convolutional neural network (CNN) model architecture specifically tailored for the analysis of CT images. We conducted a comprehensive analysis of the current body of literature to investigate the clinical features, imaging manifestations, and image-based diagnostic methods of COVID-19. This study examines the application of deep learning models in the diagnosis of COVID-19, specifically emphasizing the utilization of Convolutional Neural Networks (CNNs) as a robust method for analyzing medical images. In addition, we demonstrate experimental enhancements to the CNN model, achieving a diagnosis accuracy of up to 92.80% when evaluated on test data.

The Influence of the Introduction, Connect, Apply, Reflect, and Extend (ICARE) Learning Model on Cognitive Learning Outcomes in Science and Social Studies (IPAS) in Elementary Schools

This study addresses the issue of low student learning outcomes attributed to the frequent use of conventional teaching methods by educators in elementary schools. The Introduction, Connect, Apply, Reflect, and Extend (ICARE) learning model is proposed as a potential solution to enhance these outcomes. The purpose of this research is to examine the impact of implementing the ICARE learning model on students' cognitive learning outcomes in science and social studies (IPAS) in elementary schools. Additionally, the study compares the improvement of cognitive outcomes between students taught using the ICARE model and those taught using traditional methods. This quasi-experimental research employs a non-equivalent pretest-posttest control group design. The findings reveal that the application of the ICARE learning model significantly influences cognitive learning outcomes in IPAS subjects, with an effect size of 46.2%. Furthermore, students instructed through the ICARE model demonstrate greater improvement in cognitive learning outcomes compared to those who receive conventional instruction.

Implementation of Levels of Inquiry Model in Improving Students' HOTS on Fluid Material

This research is motivated by the low level of students' higher order thinking skills (HOTS) in physics. The purpose of this study is to determine how the application of Levels of Inquiry (LoI) learning model in improving students' HOTS. The research design used was a quasi-experiment involving grade XI students in one of the high schools in Bandung City with a research sample of 35 students in the experimental class and 25 students in the control class. The instruments used in this study were higher order thinking skills test in the form of two-level multiple choices for fluid material and observation sheet of learning model implementation. Data analysis techniques include N-Gain, hypothesis testing and effect size. The results of the analysis showed that the application of the LoI learning model in the experimental class was in the excellent category and could improve students' HOTS in the moderate category with an N-gain value of 0.53. In the control class, the application of the conventional learning model can improve students' HOTS in the low category with an N-gain score of 0.18. In addition, the analysis of the Mann-Whitney test results showed that there was a significant difference in students' HOTS between the experimental group and the control group with an effect size in the very large category. This research proves that the application of the LoI learning model is effective in improving students' HOTS in fluid materials.

Implementasi Pembelajaran Berbasis Proyek Berbantuan Aplikasi Simurelay untuk Meningkatkan Kreativitas Siswa

This study was focused on identifying the increase in student creativity in project-based learning in classes that use the Simurelay application with classes that use powerpoint as a learning medium, and there are significant differences between the two classes. The research method used in this study is a quasi-experiment with a non-equivalent control group design of a design. This study involved the population of students in class XI TITL SMK YPM 1 Taman Sidoarjo. The research sample of 76 students was divided into 38 students in the control class and 38 in the control class who were selected with the purposive sampling technique. The data collection instrument was in the form of 30 multiple-choice questions. The data analysis techniques used are descriptive and inferential analysis. The results of data analysis showed that there was an increase in creativity before and after being treated in the experimental class with an average score of 19.79 with the category of moderately creative while in the control class obtained an average score of 15.18 with the category of non-creative. There was a significant difference in creativity between the two classes with (t-count = 6.8778 > t-table = 1.993) at a significance level of 5%. The application of the PjBL learning model assisted by Simurelay in the category is quite effective in increasing the creativity of students in class XI TITL SMK YPM 1 Taman Sidoarjo. In this study, students showed an excellent response after the learning model was applied.

Research on low-power driving fatigue monitoring method based on spiking neural network.

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21-42.59%.

INCREASING THE ACTIVITY AND OUTCOMES OF SCIENCE LEARNING THROUGH THE IMPLEMENTATION OF THE DISCOVERY LEARNING MODEL IN STUDENT X AT SMK AL KHAIRIYAH CIOMAS

This action research aims to increase student activity and learning outcomes through the application of discovery learning models in class X students at SMK Al Khairiyah Ciomas. The subjects of this research were 30 students in class X. What is used is classroom action research with the Kemmis and Mc model. Taggart. Each cycle is carried out with stages: planning, action implementation, observation and reflection. The results of the analysis of descriptive data show that in cycle I student learning outcomes have not yet reached the minimum completeness criteria (KKM) applied because they have only achieved an average score of 67.60 from a KKM score of 75, and an absorption capacity of 50% which means that 15 students have fulfilled the KKM and 15 students have not reached KKM. This is due to the activeness of students in learning 61.60 while the ability of teachers in learning has only reached 75%. From these results it is necessary to proceed to cycle II. And as for the results of the actions in cycle II that the average student learning outcomes were 76.20, which meant that they had reached the KKM and the absorption was 76.67%, which meant that 23 students had exceeded the KKM. While the ability of teachers by 85%. Because this research had reached KKM, this research was not continued in the next cycle. The results of the study concluded that the application of the discovery learning model can increase the activity and learning outcomes of science.

Implementing Inquiry Learning Strategy to Improve Students’ Thinking Ability of SMPN 6 Gunungsitoli in 2023/2024

This research was motivated by initial observations which showed low student learning outcomes, which can be seen based on the average score on exam results, namely 60. The average score is still below the minimum completeness criteria (MCC) set by the school, namely 67, in English subjects. This shows that student learning outcomes in English learning are still low. This research aims to implement inquiry learning strategies in improving students' thinking skills and improving listening skills in English at SMP Negeri 6 Gunungsitoli. This research focuses on finding out the teaching methods used by teachers to improve students' thinking skills, implementation, assessment in learning with research results obtained through student observations in learning activities, questions to improve one of the students' competencies in listening skills, and the researcher's field notes. while carrying out research. The informants for this research were class 8, totaling 7 people. The method used in this research is Classroom Action Research (CAR). The results obtained from this research include: 1). Implementation of the learning process using the inquiry method. 2). Observation results showing improvement from the first cycle to the second cycle at the second meeting. 3). Implementation of listening tests for students makes students like learning English and through MCC. The average value presentation obtained is 90%. This shows an increase from cycles I and II student learning motivation through implementing the inquiry learning model in the When I was a child material with an average score of 90% in the very high category. The application of the inquiry learning model can increase students' activeness in learning, improve students' thinking abilities, and increase the completeness of learning outcomes.

Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method

The advent of deep learning techniques has shown promising improvement in flood forecasting accuracy which are crucial for operating reservoir and mitigating flood damages. However, the practical application of typical deep learning models, such as the Long Short-Term Memory network (LSTM), is hindered by their high complexity, computational cost, and lack of interpretability. This study introduces an Explainable Convolutional Neural Network (ECNN) model that can effectively capture inter-variable relationships and temporal dynamics to address these challenges. The proposed ECNN model was applied to the Lushui basin in China and compared with benchmark models. The interpretability of the ECNN model was analyzed using the Gradient-weighted Class Activation Mapping (Grad-CAM) method. Our findings demonstrated that the ECNN model exhibits exceptional performance in flood forecast tasks, particularly for long-term forecast horizons. Furthermore, precipitation and inflow play a decisive role in flood forecasting with the watershed-specific flood characteristics. The ECNN model is highly promising in real-time flood forecasting with ingenious parallel structure, remarkable accuracy, and interpretability.

Penerapan Model Pembelajaran Active Learning dengan Menggunakan Games Roda Pintar untuk Meningkatkan Minat Belajar Materi Perkalian Pelajaran Matematika Kelas III SDIT Baitul Ilmi

The Active Learning method is active learning. Most practitioners and observers refer to it as a learning by doing strategy. The approach views learning as a process of building understanding through experience and information. With this approach, learners' unique perceptions, knowledge and feelings influence the learning process. The problems in this study are (1) How is student interest in learning in the application of the Active Learning learning model using smart wheel games multiplication material of class III mathematics lessons SDIT Baitul Ilmi (2) Whether the application of the Active Learning learning model using smart wheel games can increase student interest in learning multiplication material of class III mathematics lessons SDIT Baitul Ilmi. This research includes Classroom Action Research (PTK) conducted in two cycles using quantitative analysis and qualitative analysis methods. Each cycle consists of four stages, namely design, implementation, observation, and reflection. The subjects of this study were third grade students of SDIT Baitul Ilmi, totalling 33 students. The research data was obtained using teacher and student observation sheets and student learning interest test results, then the data was analysed using the percentage formula. The results obtained are (1) teacher activity in cycle I using the Active Learning model from 87.50% to 93.75% in cycle II, (2) student activity in cycle I using the Active Learning model 54.64% to 85.17% and, (3) the results of student learning interest in Mathematics subjects cycle I using Active Learning 32.25% to 83.87% in cycle II. This shows that the results of students' confidence in Mathematics subjects using the Active Learning model show very positive results. Based on the results of this study, it can increase students' interest in learning to be more active and the teacher's ability to improve for the better.