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This study examines the education of Ahlus Sunnah Wal Jamaah theology, focusing on its concept, classification, and implementation in Muslim life. Using a literature review method, this research explores primary Islamic sources, such as classical theological books, along with modern academic references that explain the 20 Attributes (Sifat 20) in Ahlus Sunnah Wal Jamaah doctrine. The analysis is conducted descriptively and critically to understand how this theology is taught and applied in various aspects of life. The findings reveal that theological education plays a crucial role in shaping Muslim beliefs and character while maintaining relevance in the Islamic education system. This study also recommends more effective teaching strategies for comprehending and internalizing Ahlus Sunnah Wal Jamaah theology.

The development of technology in the digital era has a significant impact on the financial sector, especially in the payment system that is now shifting from cash to non-cash. One form of technological innovation is Financial Technology (Fintech), where E-Wallet is one of the popular classifications in Indonesia, such as Shopeepay, Dana, Gopay, OVO, and LinkAja. This study aims to analyze the influence of digital financial literacy, perception of convenience, and Gen Z lifestyle on the interest in using the Shopeepay E-Wallet among students of the Management Study Program, FEB UNISRI. This study uses a quantitative approach with primary data collected through questionnaires from 94 respondents. The sampling technique uses proportionate stratified random sampling. Data were analyzed using multiple linear regression, t-test, F-test, and coefficient of determination (R²). The results show that digital financial literacy and Gen Z lifestyle have a significant partial effect on the interest in using Shopeepay, while perception of convenience does not have a significant effect. Simultaneously, the three variables have a significant effect. The R² value of 65% shows that the independent variable is able to explain the interest in using Shopeepay, while the remaining 35% is influenced by other factors such as perceptions of security, risk, benefits, trust, and financial behavior.

Travel insurance provides financial protection for individuals during their trips, both domestically and internationally. With the increasing demand for travel insurance, insurance companies face challenges in efficiently managing claims. This study aims to develop a predictive model to classify whether an insurance policy will be claimed based on historical customer and transaction data. This research utilizes a dataset containing various features related to travel and policyholders, such as agent type, distribution channel, insurance product, travel duration, and premium amount. The methods used include data exploration, feature processing, and the application of machine learning algorithms such as Logistic Regression, Random Forest, and XGBoost. Experimental results indicate that the XGBoost model performs the best, achieving the highest accuracy compared to other models. With this predictive model, insurance companies can optimize claim evaluation processes, reduce fraud risks, and improve operational efficiency in handling travel insurance claims.

Hotel booking cancellations pose significant challenges to the hospitality industry, affecting revenue management, demand forecasting, and operational efficiency. This study explores the application of machine learning techniques to predict hotel booking cancellations, leveraging structured data derived from hotel management systems. Various classification algorithms, including Random Forest, XGBoost, and LightGBM were evaluated to identify the most effective predictive model. The findings reveal that XGBoost model outperforms other models, achieving F2-score of 0.7897. Key influencing factors include deposit type, total number of special requests, and marketing segment. The results underscore the potential of predictive modeling in optimizing hotel revenue strategies by enabling proactive measures such as dynamic pricing, targeted customer engagement, and improved overbooking policies. This study contributes to the ongoing advancements in data-driven decision-making within the hospitality industry, offering insights into how machine learning can mitigate financial risks associated with booking cancellations.

Ship pengolongan is an activity that passes through the river channel from upstream to downstream, by crossing under the Mahakam Bridge, Mahulu Bridge and Mahkota 2 Bridge using the services of a guide. The purpose of this study was to determine the factors, effects, and efforts that would arise due to the lack of assist ships in the pengolongan activity at the Mahkota 2 Bridge, Samarinda. This research method uses a qualitative study with a descriptive qualitative model that describes and describes the object being studied. Based on the results of the study, there are several factors related to the lack of assist ships during the pengolongan activity. First, because the Mahakam River Channel is shallow and narrow, especially under the Mahkota 2 Bridge, making assist ships careful in manoeuvring ships, second, weather factors and the depth conditions of the river which are sometimes uncertain, third, the large number of tug boats pulling barges with their loads, making the 2 assist ships very lacking in the process of the activity. The factors above resulted in a long queue of tug boats pulling their loads in the Mahakam River channel when carrying out pengolongan under the Mahkota 2 Bridge.

This research focuses on assessing the efficacy of a method that integrates Convolutional Neural Networks (CNN) with Decision Trees for the detection of phishing URLs. Phishing represents a major cyber threat, where cybercriminals attempt to deceive individuals into disclosing sensitive information via fraudulent websites. As the frequency of phishing attacks continues to rise, there is a pressing need for effective detection and prevention strategies. In this investigation, a dataset comprising both phishing and legitimate URLs was utilized to train a CNN-Decision Tree model. The training phase includes feature extraction from URLs using CNN, which excels at identifying intricate patterns within the data, followed by classification through Decision Trees, recognized for their capacity to deliver straightforward and comprehensible interpretations of classification outcomes. The model's performance was evaluated across nine distinct scenarios to assess its effectiveness under varying conditions. The results indicated that the hybrid CNN-Decision Tree model achieved a precision rate of 94%, a recall of 90%, and an F1-Score of 92%, with an overall accuracy of 93%. These findings suggest that the model is not only proficient in identifying phishing URLs but also maintains a commendable balance between precision and recall. This research highlights that the synergy of CNN and Decision Trees can serve as a potent solution for phishing URL detection, significantly contributing to the advancement of enhanced cybersecurity systems.

The Semitic language family is one of the oldest linguistic groups that has significantly influenced world civilizations, with Arabic as one of its main branches. Arabic has undergone a long historical journey from prehistoric times to the modern era, shaped by various historical, cultural, and social factors. The study of Fiqhul Lughah, or the philosophy of language, is crucial in understanding the transformation and development of Arabic in different historical contexts. This research employs a library research method, analyzing various literary sources such as books, scholarly journals, and ancient manuscripts that discuss the history and evolution of Semitic languages, particularly Arabic. The analysis is conducted using a descriptive-qualitative approach to comprehend the patterns of Arabic evolution from its ancient Semitic roots to its establishment as a major language in communication, literature, science, and religion. The findings indicate that Arabic has evolved gradually through various historical phases, from its pre-classical stage to the golden age of Islam and its modern adaptations. Understanding the history and classification of Arabic within the Semitic language family provides deeper insights into its dynamics and its significant role in shaping human civilization.

The increasing adoption of artificial intelligence (AI) in decision-making processes has raised significant concerns regarding algorithmic bias and legal accountability. This study examines the regulatory challenges and enforcement gaps in addressing AI bias, with a particular focus on Indonesia’s legal landscape. Through a comparative analysis of AI governance frameworks in the European Union, the United States, China, and Indonesia, this research identifies key deficiencies in Indonesia’s regulatory approach. Unlike the EU’s AI Act, which incorporates risk-based classification and strict compliance measures, Indonesia lacks a dedicated AI legal framework, leading to limited enforcement mechanisms and unclear liability provisions.The findings highlight that transparency mandates alone are insufficient in mitigating algorithmic discrimination, as weak enforcement structures hinder effective regulatory oversight. Furthermore, the study challenges the notion that global AI regulatory harmonization is universally applicable, emphasizing the need for a context-sensitive hybrid model tailored to Indonesia’s socio-legal environment. The research suggests that Indonesia must adopt a comprehensive AI legal framework, strengthen regulatory institutions, and promote interdisciplinary collaboration between legal experts and AI developers. Future research should focus on empirical case studies, the development of context-specific AI accountability models, and the role of public engagement in AI bias mitigation. These efforts will be essential in shaping effective AI governance strategies that ensure fairness, transparency, and accountability in Indonesia’s digital transformation.

This study aims to classify the level of satisfaction of Dasawisma cadres with the Carik application in West Semper Village by utilizing the Naive Bayes method. Data was obtained through questionnaires, which were compiled based on three main aspects: ease of use, speed of access, and the usefulness of applications in supporting cadre tasks. After the data is collected, a pre-processing and labeling process is carried out, where the level of satisfaction of respondents is categorized into two classes, namely "satisfied" and "dissatisfied". The Naive Bayes algorithm is applied to predict satisfaction classes based on questionnaire answers. The results of the analysis show that the Naive Bayes method is able to perform classification with sufficient accuracy, so that it can be used as an evaluation tool and decision support in the development of the carik application. This method can also help the management understand user perceptions and improve the system based on objective and routine data in line with the needs of field cadres.

I Gusti Ngurah Rai International Airport – Bali is one of the airports with the busiest flight traffic in Indonesia, for both domestic and international routes. The high intensity of operational activities on the airside demands optimal management of all supporting elements, one of which is Ground Support Equipment (GSE). GSE plays a vital role in supporting aircraft movements and handling baggage, cargo, and passengers. Therefore, the provision of well-organized Equipment Parking Area (EPA) facilities in the Make Up and Break Down areas is crucial to ensure smooth and safe operations on the apron. This paper aims to examine the effectiveness of EPA provision and management in supporting GSE work efficiency, while identifying challenges and providing solutions to various emerging problems. The method used is a descriptive qualitative approach through direct observation, field documentation, and analysis of the actual layout compared to the provisions of the Directorate General of Civil Aviation Regulation No. PR 21 of 2023 concerning Technical and Operational Standards for Civil Aviation Safety Regulations. This research also complements EPA management practices at other airports in Indonesia, to provide a constructive comparative perspective. The study results indicate that the current management of the EPA at I Gusti Ngurah Rai Airport is not optimal. The main problems identified include irregular parking of GSE vehicles, the absence of zoning based on vehicle type, minimal markings, and a weak monitoring system due to limited personnel. As a result, congestion often occurs on GSE operational routes, which can disrupt the smooth operation of ground handling services. Based on these findings, proposed improvements include redesigning the EPA layout based on vehicle classification, installing signs and vehicle flow maps in strategic areas, and implementing an integrated technology-based monitoring system such as CCTV and digital access control.

This study aims to classify employees at the North Sumatra Provincial Human Resources Development Agency (BPSDM) based on age, education level, and rank using the K-Means Clustering method. Age, education level (converted to numeric), and rank (converted to numeric) data were processed through preprocessing, cluster number determination, K-Means implementation, and evaluation stages. The results showed that employees were grouped into three clusters: 1) dominated by ages 54-64 years, S2 education, rank IV/d; 2) dominated by ages 43-53 years, S2 education, rank III/d; 3) dominated by ages 32-42 years, SLTA education, rank III/b. This classification provides employee characteristic information to support human resource management.

Monkeypox is a zoonotic disease caused by Orthopoxvirus, presenting clinical challenges due to its visual similarity to other dermatological conditions. Early and accurate detection is crucial to prevent further transmission, yet conventional diagnostic methods are often resource-intensive and time-consuming. This study proposes a deep learning-based classification model by integrating Xception and InceptionV3 using feature fusion to enhance performance in classifying Monkeypox skin lesions. Given the limited availability of annotated medical images, data augmentation was applied using Albumentation to improve model generalization. The proposed model was trained and evaluated on the Monkeypox Skin Lesion Dataset (MSLD), achieving 85.96% accuracy, 86.47% precision, 85.25% recall, 78.43% specificity, and an AUC score of 0.8931, outperforming existing methods. Notably, data augmentation significantly improved recall from 81.23% to 85.25%, demonstrating its effectiveness in enhancing sensitivity to positive cases. Ablation studies further validated that augmentation increased overall accuracy from 82.02% to 85.96%, emphasizing its role in improving model robustness. Comparative analysis with other models confirmed the superiority of our approach. This research enhances automated Monkeypox detection, offering a robust and efficient tool for low-resource clinical settings. The findings reinforce the potential of feature fusion and augmentation in improving deep learn-ing-based medical image classification, facilitating more reliable and accessible disease identification.

Human action recognition involves recognizing and classifying actions performed by humans. It has many applications, including sports, healthcare, and surveillance. Challenges such as a limited number of classes of activities and variations within inter and intra-class groups lead to high misclassification rates in some of the intelligent systems developed. Existing studies focused mainly on using public datasets with little focus on real-life action datasets, with limited research on HAR for military obstacle-crossing activities.  This paper focuses on recognizing human actions in an obstacle-crossing competition video sequence where multiple participants are performing different obstacle-crossing activities. This study proposes a feature descriptor approach that combines a Histogram of Oriented Gradient and Region Descriptors (HOGReG) for human action recognition in a military obstacle crossing competition. The dataset was captured during military trainees’ obstacle-crossing exercises at a military training institution to achieve this objective. Images were segmented into background and foreground using a Grabcut-based segmentation algorithm, and thereafter, features were extracted and used for classification. The features were extracted using a Histogram of Oriented Gradient (HOG) and region descriptors from segmented images. The extracted features are presented to a neural network classifier for classification and evaluation. The experimental results recorded 63.8%, 82.6%, and 86.4% recognition accuracies using the region descriptors HOG and HOGReG, respectively. The region descriptor gave a training time of 5.6048 seconds, while HOG and HOGReG reported 32.233 and 31.975 seconds, respectively. The outcome shows how effectively the suggested model performed.

This study analyses the Froude number and type of hydraulic jump in flow through a sluice gate in a secondary channel, both with and without a wide sill. Culverts, as important hydraulic elements in irrigation systems, often trigger hydraulic jumps that have the potential to cause scour. This research is motivated by the need to understand the characteristics of hydraulic jumps in more detail, as a basis for designing effective energy dissipators. Unlike previous studies that focus on contraction coefficient and discharge, this study specifically examines the Froude number and classification of springboard types. This study used secondary data from Sunik's (2001) research which included discharge, water level and flow velocity data for two channel configurations: with and without wide sill. The analysed discharge data varied from 10 to 30 litres/second with variations in door opening. The analytical method used was the calculation of Froude number based on secondary data to classify the type of hydraulic jump. The objectives of this study were to determine the Froude number value and identify the type of hydraulic jump that occurred. The findings showed that discharge and door opening influenced flow characteristics and springboard type. In channels without a sill, discharge affects the transition of flow from subcritical to supercritical. The presence of a wide sill creates a more linear relationship between water level, velocity and Froude number. Varying the door opening affects the type of surge, from choppy, to weak, to vibrating, as the flow energy increases. Larger discharges resulted in higher Froude numbers and stronger surge types. This research makes an important contribution to the understanding of hydraulic springing phenomena and the design of energy dissipator.

Problem solving is a process that involves mental operations such as deduction, induction, classification, evaluation, and reasoning. This study aims to capture the landscape of problem solving research, particularly in secondary schools. The method used is bibliometric descriptive analysis. The source of the data obtained came from the Scopus database. 100 relevant documents were found, indicating the increasing but still limited interest of researchers in problem-solving skills in inclusive education. This research aims to direct future research and support the development of responsive and inclusive education policies.

Study This contains about the detection of circular object images. The circular object tested is the moon image object, the moon image was chosen because the moon image has various moon shapes, namely the full moon, half moon and crescent moon. To detect the shape of the circular object image, several stages are carried out by starting the image segmentation process. (1) The segmentation process using the bi-level thresholding method makes the image black and white, (2) after that the image is repaired with the morphological process of the opening and closing methods. (3) For training data, the shape extraction process is carried out, namely the circular nature of the object (circularity) to determine the roundness of an object. For the testing process, the same process is also carried out as the process of obtaining circular image detection.

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, necessitating robust attack detection mechanisms. This study presents a comprehensive comparative analysis of ten supervised learning algorithms for IoT attack detection and classification, addressing the critical challenge of balancing detection accuracy with practical deployment constraints. Using the CICIoT2023 dataset, encompassing data from 105 IoT devices and 33 attack types, we evaluate Naive Bayes, Artificial Neural Networks (ANN), Logistic Regression (LR), k-NN, XGBoost, Random Forest (RF), LightGBM, GRU, LSTM, and CNN algorithms based on some performance metrics. The comparative test results show superior performance to the traditional ensemble approach, with RF achieving 99.29% accuracy and leading precision (82.30%), followed closely by XGBoost with 99.26% accuracy and 79.60% precision. Deep learning approaches also demonstrate strong capabilities, with CNN achieving 98.33% accuracy and 71.18% precision, though these metrics indicate ongoing challenges with class imbalance. The analysis of confusion matrices reveals varying success across different attack types, with some algorithms showing perfect detection rates for certain attacks while struggling with others. The study highlights a crucial distinction in IoT security: while high precision remains important, the potentially catastrophic impact of missed attacks necessitates equal attention to recall metrics, as evidenced by the varying recall rates across algorithms (RF: 72.19%, XGBoost: 71.69%, CNN: 64.72%). These findings provide vital insights for developing balanced, context-aware intrusion detection systems for IoT environments, emphasizing the need to consider performance metrics and practical deployment constraints.

Pronunciation is one of the determiner aspects in English as it can change the meaning of the words and sentences. This research discusses the analysis of errors in the pronunciation of triphthongs in words and sentences in English. The research was aimed to know the types of triphthongs often mispronounced in the pronunciation of words and sentences in English among second semester students in class A of Speaking for workplace communication lecture at English study program of Madiun State Polytechnic. The quantitative were used after being collected from the assignments and pronunciation tests for several words and sentences containing triphthongs during the research. The research results are described in percentages. The diphthong errors that students often make are //aʊə /= 34.6%, /aɪə /= 31.8%, /ɔɪə /= 20%, /əʊə/= 9%, and /eɪə/= 4.6%. Errors that occurred in the classification of substitutions = 43.64%, insertions = 32.73%, and omissions = 23.63%. The errors occur due to the influence of inter-language and intra-language factors of the students.

Predicting credit worthiness is an important step for banks to reduce the risk of bad credit. This research compares the performance of four classification algorithms, namely SVM, Naïve Bayes, Random Forest and Decision Tree using simulated datasets. The results obtained on the metrics of accuracy, precision, recall, F1 score, and AUC-ROC, show that Decision Tree has the best performance with 42.5% accuracy, 48.3% precision, 47.5% recall, 47.5% F1 score, and AUC 0.60, indicating its ability to is in differentiating credit worthiness. Random Forest achieved an accuracy of 37.5% and an AUC of 0.493, while Naïve Bayes had the lowest accuracy with an accuracy of 27.5% and an AUC of 0.425. SVM gives better results than Naïve Bayes but is still inferior to Decision Tree. This research recommends implementing a Decision Tree as the main model with optimization through hyperparameter tuning, adding relevant features, and handling data accounting. These results are expected to support banking decision making more effectively and efficiently.

This study aims to optimize the implementation of the K-Nearest Neighbors (K-NN) algorithm for medical image classification by focusing on selecting the optimal KKK parameter and applying dimensionality reduction techniques to improve accuracy and efficiency. The data used was sourced from public medical image repositories such as The Cancer Imaging Archive (TCIA) and Medical Image Analysis datasets, covering various diseases, including brain tumors, lung cancer, and kidney lesions. The research process involves data collection, data preprocessing, dimensionality reduction using Principal Component Analysis (PCA), applying the K-NN algorithm with Euclidean, Minkowski, and Cosine distance metrics, and performance evaluation using accuracy, precision, recall, and F1-score. Experimental results demonstrate that K=5with the Euclidean distance metric provides the best performance, achieving an accuracy of 90%. Additionally, PCA effectively reduces computational time by 30% without significantly compromising accuracy. This study proves that K-NN is an effective method for medical image classification. However, further research is needed to integrate K-NN with deep learning models to enhance performance and feature extraction capabilities.