Universitas Islam Kalimantan Muhammad Arsyad Al Banjary

Digital Storytelling is an application of technology that is positioned to help educators overcome barriers to the use of technology in the classroom into a productive thing by creating stories or fairy tales digitally. Digital Storytelling can be used as a way to convey stories, both fiction and reality, which can be combined with images, text, audio and video. The purpose of this study is to determine the role of utilizing technological progress through Digital storytelling in education, especially in the field of Arabic. The method used is a qualitative method with an approach through literature studies (literature review). The form of application of Digital Storytelling in Arabic language learning is to tell popular stories that are adjusted to the cognitive level of students so that the material provided is easy to understand. The result of the research is that Digital Storytelling plays a role or has a positive impact on Arabic education and can also be used as an alternative in increasing students' interest in learning, especially in Arabic subjects.

Crawler cranes are critical heavy equipment in the construction industry, but they often experience failures that cause downtime and increased costs. This article comprehensively analyses crawler crane failures using three main methods: Fishbone Diagram, Pareto Principle, and Failure Mode and Effect Analysis (FMEA). Failure data for the past two years (January 2022 – September 2024) is analyzed to identify root causes and determine repair priorities. A Fishbone Diagram is used to identify the main causes of failure, which are grouped into four categories: Mechanical, Electrical, Environmental, and Human Error. From this analysis, it is found that mechanical failure is the most dominant cause. This analysis found that mechanical failure is the most dominant cause, mechanical failures account for most failures (60%), followed by electrical failures (33%), with failures in the gearbox and engine overheating being the most significant causes. Furthermore, FMEA evaluates potential failure modes, determines their impacts, and sets mitigation priorities based on the Risk Priority Number (RPN). The results of this study provide a strategic approach to minimize downtime by focusing maintenance efforts on the root causes of failure. This article also offers a new contribution by combining three comprehensive analysis methods not systematically applied to crawler crane maintenance. This research is expected to help improve operational reliability and reduce repair costs in the construction industry.

This systematic review aims to evaluate the impacts of climate change on the resilience, adaptation, and sustainability of pastures through a comprehensive bibliometric analysis of research conducted from 1994 to 2024. Studies included in this review were those that assessed the impacts of climate change on pastures, focusing on resilience, adaptation, and sustainability, and were published in peer-reviewed journals. Data was sourced from Scopus, a comprehensive database providing a global perspective on research activity. Risk of bias in the selection of studies was minimized by adhering to systematic search and inclusion criteria. A total of 318 studies were included, predominantly published between 1994 and 2024, providing a thorough analysis of global research trends. Bibliometric tools such as Biblioshiny and VOSviewer were used to analyze publication and citation trends, collaboration networks, and research themes. The analysis revealed a significant annual growth rate of 10.11% in publications related to climate change impacts on pastures. The average citation per publication was 39.56, indicating substantial academic impact. Collaborative efforts were notable, with an average of 6.61 authors per document and 44% of these collaborations being international. Key contributors included researchers from China, the US, and Europe. These findings highlight the increasing scientific focus and international collaboration in this field. The integration of environmental science, agriculture, and climatology provides comprehensive insights into the effects of climate change on pasture ecosystems. The study identifies new research themes and knowledge gaps, offering valuable insights for policymakers, researchers, and practitioners. The findings of this study can inform future research priorities and support evidence-based policy-making to enhance the resilience and sustainability of pastures in the face of climate change.

Purpose Phytosome technology, an advanced lipid-based delivery system, offers a promising solution for enhancing the bioavailability and therapeutic efficacy of secondary metabolites, particularly in cancer treatment. These metabolites, such as flavonoids, terpenoids, and alkaloids, possess significant anticancer potential but are often limited by poor solubility and low absorption. This review aims to investigate how phytosome encapsulation improves the pharmacokinetic profiles and anticancer effectiveness of these bioactive compounds. Patients and Methods This comprehensive review is based on an analysis of recent literature retrieved from PubMed, Scopus, and ScienceDirect databases. It focuses on findings from preclinical and in vitro studies that examine the pharmacokinetic enhancements provided by phytosome technology when applied to secondary metabolites. Results Phytosome-encapsulated secondary metabolites exhibit significantly improved solubility, absorption, distribution, and cellular uptake compared to non-encapsulated forms. This enhanced bioavailability facilitates more effective inhibition of cancer pathways, including NF-κB and PI3K/AKT, leading to increased anticancer efficacy in preclinical models. Conclusion Phytosome technology has demonstrated its potential to overcome bioavailability challenges, resulting in safer and more effective therapeutic options for cancer treatment. This review highlights the potential of phytosome-based formulations as a novel approach to anticancer therapy, supporting further development in preclinical, in vitro, and potential clinical applications.

Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate.

In an effort to increase diagnostic efficiency and accuracy, this work investigates the application of machine learning models Random Forest, SVM, and Logistic Regression for the categorization of anemia. Hematocrit and hemoglobin levels were included in the dataset, which was divided into training and testing sets. Using CatBoost, Random Forest outperformed SVM (82.1%) and Logistic Regression (75.1%) with the greatest accuracy (99.2%). SVM and Logistic Regression work well with simpler data, while Random Forest performs best with intricate medical datasets, which makes it perfect for applications involving the detection of anemia.

This study aims to develop a heart disease classification model using an ensemble approach by leveraging a Stacking framework that combines BiGRU, BiLSTM, and XGBoost models. In this research, the BiGRU and BiLSTM models are utilized as base models to extract temporal and spatial features from sequential data, while XGBoost is employed as a metamodel to perform the final classification based on the features generated by the two base models. The test results show that the BiGRU model achieves an accuracy of 0.77, while the BiLSTM model achieves an accuracy of 0.85. By applying the Stacking technique using XGBoost as the meta-model, the classification accuracy significantly increases to 0.92. These findings indicate that the Stacking framework can effectively enhance heart disease classification performance, making it a potentially powerful tool for medical applications in heart disease diagnosis.

Diabetes is a chronic condition that requires accurate and timely diagnosis for effective management and treatment. This study introduces an innovative approach to diabetes classification using a stacking framework that combines Bidirectional Long Short-Term Memory (BiLSTM), Logistic Regression, and XGBoost. The study employed an experimental approach by implementing the stacking framework. The two base models used were BiLSTM and Logistic Regression, with BiLSTM achieving an accuracy of 0.9935 and Logistic Regression reaching 0.9869. The stacking framework with XGBoost as the meta-learner achieved a perfect accuracy of 1.0. These findings demonstrate the potential of the stacking framework to improve diabetes classification performance compared to using individual models alone.

This study compares four machine learning algorithms Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in water quality classification based on contaminant parameters. The purpose of this study is to evaluate and compare the performance of these algorithms in terms of accuracy. The methodology used includes data collection, preprocessing, and algorithm implementation with evaluation using crossvalidation techniques. The results showed that the application of the Stacking method with Gradient Boosting Meta-learner produced the highest accuracy of 96.00%, outperforming all other algorithms. In comparison, Random Forest achieved 95.75% accuracy, followed by SVM with 93.25% accuracy, and Logistic Regression and KNN each achieved 90.19% accuracy. This finding emphasizes that Stacking with Gradient Boosting provides much better performance in water quality classification compared to other models. This research provides new insights into the application of machine learning algorithms for water quality management as well as guidance for optimal algorithm selection.

This research addresses the persistent global challenge of poverty, with a specific focus on Indonesia, a nation with a population exceeding 270 million. The primary objective is to enhance the precision and reliability of poverty classification using advanced machine learning technologies. We employed a combination of Bidirectional Gated Recurrent Unit (BiGRU), Backpropagation Neural Network (BPNN), and stacking techniques with AdaBoost to develop an innovative classification model. The methodology involved training each technique separately and then integrating them into a stacked model to leverage their individual strengths. The results were promising, demonstrating a substantial improvement in model performance with precision, recall, and F1 scores reaching as high as 0.98, and an overall accuracy of 98.06%. The use of visual analytics, including pie charts and bar graphs, provided a comprehensive distribution analysis of poverty levels, confirming the balanced nature of the dataset. These findings underscore the critical role of machine learning in formulating effective policies for poverty alleviation and suggest that integrating multiple machine learning algorithm can significantly enhance decision-making processes. The novelty of this research lies in the successful application of a stacked machine learning model combining BiGRU, BPNN, and AdaBoost, which establishes a new benchmark for poverty classification in large-scale social datasets. This study not only contributes to the academic discourse but also paves the way for practical implementations that can drive inclusive and sustainable development.

Research focuses on developing a water level prediction framework for the Riam Kanan Dam using an innovative stacking approach called ConvLSTM-BPNN-Gradient Boosting and Stacking XGBoost (CLBGXGBoostS), which combines the strengths of Convolutional Long Short-Term Memory (ConvLSTM), Backpropagation Neural Network (BPNN), and Gradient Boosting. The study aims to evaluate the performance of the CLBGXGBoostS stacking framework in predicting the water level of the Riam Kanan Dam using 5 years of historical data. The results demonstrate that the CLBGXGBoostS framework provides more accurate predictions compared to single models, as evidenced by the Root Mean Squared Error (RMSE) values. CLBGXGBoostS achieves an RMSE of 0.0071, significantly lower than the RMSE of the individual models ConvLSTM (0.1006), BPNN (0.2618), and Gradient Boosting (0.6905). This research contributes to the development of a better water level prediction framework for the Riam Kanan Dam, supporting more effective water resource management and serving as a reference for future research in this field.

Air quality prediction, particularly in estimating PM10 particle concentration, is a significant challenge in major cities like Jakarta, which experience high levels of air pollution. This study aims to develop an air quality prediction model using an innovative stacking framework that combines several machine learning algorithms, namely ConvLSTM, CatBoost, SVR, and XGBoost. The methodology employed in this research is an experimental approach, where each model is trained and tested individually before being integrated into the stacking framework. The dataset used was sourced from the Kaggle platform, containing historical air quality data in Jakarta. Performance evaluation was conducted by measuring the Root Mean Squared Error (RMSE) for each model. The results of the study showed that the ConvLSTM model produced an RMSE of 13.5168, CatBoost with an RMSE of 13.4113, and SVR with an RMSE of 14.2725. To improve prediction accuracy, the researchers employed a stacking approach of the four models (ConvLSTM, CatBoost, SVR, and XGBoost), which yielded a much lower RMSE of 0.8093. Thus, this stacking framework has proven to significantly enhance air quality prediction performance, particularly in predicting PM10 concentrations in Jakarta.

Classification can significantly impact treatment decisions and patient outcomes. This study evaluates and compares the performance of three machine learning models Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) in breast cancer classification. ELM, known for its fast-learning speed and strong generalization, is compared with LSTM, which is effective in capturing long-term dependencies in sequential data, and CNN, which is renowned for its ability to automatically extract features from images and structured data. The models were trained and tested on a breast cancer dataset, focusing on accuracy and computational efficiency. The results revealed that while CNNs demonstrated better accuracy in feature-rich data, LSTMs excelled in handling sequential data patterns. On the other hand, ELM offers a good balance between training speed and classification performance. This comparative analysis provides valuable insights into the strengths and limitations of each model, contributing to the development of more effective breast cancer diagnostic tools. In this case, LSTM outperformed ELM by 0.91%, outperformed CNN significantly by 3.72%, and outperformed Improved LSTM by 0.91%. This indicate that the LSTM model shows higher accuracy in breast cancer classification

This study investigates the application of machine learning models to predict plant growth milestones based on environmental and treatment data. The dataset comprises categorical variables such as soil type, water frequency, and fertilizer type, alongside numerical variables including sunlight hours, temperature, and humidity. Preprocessing involved one-hot encoding for categorical variables and standard scaling for numerical features. The models employed were Support Vector Machine (SVM), Naive Bayes, and Extreme Learning Machine (ELM). The baseline SVM model achieved an accuracy of 58.97%, and hyperparameter tuning using GridSearchCV did not improve this performance, maintaining the accuracy at 58.97%. The Naive Bayes model achieved an accuracy of 51.28%, while the ELM model had an accuracy of 43.85%. Among the models, the SVM demonstrated the highest accuracy, though further improvement is required for practical implementation. The findings underscore the importance of selecting appropriate machine learning models and optimizing their parameters to enhance prediction accuracy in agricultural applications. Despite the SVM's superior performance in this context, continued refinement is essential to address the challenges posed by predicting plant growth milestones accurately.

Mental health significantly impacts overall well-being, yet the increasing prevalence of mental health issues presents challenges in their effective classification and treatment. Traditional methods often fail to accurately handle complex, non-linear data, compromising the timeliness and appropriateness of interventions. This study introduces an innovative mental health classification framework, ELM-MLP-CatBoost Stacking, to address these deficiencies. The primary objective is to enhance classification accuracy by integrating three advanced computational techniques: the speed of the Extreme Learning Machine (ELM), the flexibility of the Multi-Layer Perceptron (MLP) for modeling non-linear data, and the predictive refinement of CatBoost as a meta-model. Our methodology involves a stacking approach where ELM and MLP models serve as base learners with CatBoost integrating their outputs to optimize final predictions. Experimental results demonstrate that the ELM-MLP-CatBoost Stacking framework substantially outperforms traditional models, achieving a notable accuracy of 92.76%, an improvement over the MLP’s 92.64% and the ELM’s 69.59%. This framework enhances the reliability and efficiency of mental health condition classifications and paves the way for further research into advanced diagnostic tools. The novelty of this research lies in the synergistic combination of these models, setting a new standard for accuracy and reliability in mental health diagnostics and establishing a robust foundation for future advancements in the field.

The high prevalence of catheter-associated urinary tract infections (CAUTIs) is causing significant concern in healthcare systems. Antibacterial urinary catheters have been developed to prevent CAUTIs in clinical application. In this work, a copper sulfide nanorod (CuS NR)-embedded urinary catheter (CuS/UC) was designed as an antibacterial urinary catheter with photothermal sterilization. The CuS NRs with low cytotoxicity were synthesized via the hydrothermal method. The CuS NRs were embedded into urinary catheters at different weight percentages. The CuS/UC exhibited homogenous surface roughness, low wettability, hydrophobicity, and low adhesiveness, promoting minimal interaction with bacteria and healthy cells. Under near-infrared (NIR) laser irradiation, the 0.8 weight percentage of CuS NRs in the urinary catheter (0.8CuS/UC) reached a temperature of 67.4 °C, demonstrating its photothermal antibacterial activity and suitability for catheter sterilization. Agar plate test verified that CuS/UCs exhibited a superior photothermal antibacterial activity against both Gram-negative Escherichia coli (E. coli) and Gram-positive Streptococcus aureus (S. aureus). Moreover, the 0.8CuS/UC exhibited excellent biocompatibility and anti-cell adhesion properties. The 0.8CuS/UC with photothermal performance, excellent biocompatibility, and anti-cell adhesion properties demonstrated its potential as a photothermal antibacterial catheter for clinical applications.

This study investigates the influence of circular cylinders positioned beside the advancing blade and in front of the returning blade on the performance of a Savonius wind turbine. The experimental method focuses on assessing how these cylinders affect the turbine’s efficiency and self-starting capability. Various distances between the cylinders and the turbine blades (S/D = 1.4) and different cylinder positions (Y/D = 1.27, 1.32, 1.37, 1.42, 1.51, 1.61, 1.71, 1.82, 2.00) were explored. Tests were conducted under constant speeds corresponding to Reynolds numbers of Re = 120,000 and 150,000. Preliminary findings of all various distances indicate a significant improvement in turbine performance, particularly in enhancing self-starting ability, when using these disturbance cylinders compared to conventional designs. Specifically, the configuration with cylinders positioned at S/D = 1.4 and Y/D = 1.51 demonstrated the most promising results, showing the static torque coefficient a 48% increase in performance at a wind speed of 6 m/s and a remarkable 78% increase at 7 m/s. These results highlight the potential of utilizing circular cylinders strategically around Savonius wind turbines to optimize their performance and operational efficiency.