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The rapid development of the Internet of Vehicles (IoV) has introduced new security challenges, particularly in protecting Controller Area Network (CAN Bus) communications from cyberattacks such as Denial of Service (DoS) and spoofing attacks. This study proposes the implementation of the Extreme Gradient Boosting (XGBoost) algorithm combined with Information Gain feature selection to improve intrusion detection performance in IoV environments. The CICIoV2024 dataset, which represents both benign and malicious traffic, is used as the primary data source. The research process includes data integration, preprocessing, feature selection, data splitting, and model training using a 5-fold cross-validation approach. Experimental results demonstrate that the proposed model achieves outstanding performance, with accuracy, precision, recall, and F1-score exceeding 99.99%, and an Area Under Curve (AUC) value approaching 1.00. Furthermore, Information Gain successfully identifies the most influential CAN payload features, enhancing model efficiency without sacrificing accuracy. These findings confirm that the combination of Information Gain and XGBoost is highly effective for developing a fast, accurate, and efficient intrusion detection system in IoV networks.

Public complaint services are an essential part of public service delivery in supporting the government’s rapid response to various social issues and emergency situations. In West Tanjung Jabung Regency, public complaint services are provided through the HALO USTAD 112 Call Center managed by the Department of Communication and Informatics. However, the existing service still faces several limitations, including the lack of optimal integration in complaint data management, inadequate documentation of reports based on regional classifications, and limited capabilities in storing and retrieving complaint data. This study aims to optimize the HALO USTAD 112 Call Center service through the design of a mobile-based public complaint information system, so that the processes of receiving, managing, and monitoring reports can be carried out more effectively and in a structured manner. The system development applies the Waterfall method, which consists of requirement analysis, system design, implementation, and testing stages. The designed information system includes key features such as user and admin login, complaint submission, report management and verification, report monitoring, statistical visualization of complaint data, and regional-based report recapitulation. The application is developed using the Flutter framework with the Dart programming language, while Supabase is utilized as the backend integrated with a PostgreSQL database. The results of this study are in the form of a system design and prototype that are expected to improve the quality of public complaint services and support more accurate, integrated, and efficient data management.

The increasing demand for interactive web applications has encouraged the adoption of server-driven approaches such as Livewire as an alternative to building Single Page Applications (SPAs) without complex client-side JavaScript. However, the performance implications of this approach compared to conventional methods remain insufficiently explored. This study presents an empirical comparison between Laravel Blade with AJAX and Livewire in an academic attendance system scenario. Performance evaluation was conducted using k6 on the same web server, complemented by manual browser-based testing to observe actual communication patterns. The results indicate that Livewire exhibits approximately 2.7× higher average response time and up to 6× greater bandwidth consumption than Laravel Blade, primarily due to its snapshot mechanism and state synchronization process. Conversely, Livewire demonstrates better stability, reflected by lower maximum response times and a 0% error rate. These findings highlight a clear trade-off between resource efficiency and development convenience, where Livewire favors stability and developer productivity, while Laravel Blade provides superior efficiency in terms of latency and bandwidth usage.

Chronic Kidney Disease (CKD) is a heterogeneous disorder that gradually affects the structure and function of the kidneys, is difficult to recover, and causes the body to be unable to maintain metabolism and fail to maintain fluid and electrolyte balance, leading to increased urea levels. Chronic kidney disease data was obtained from Kaggle, in this study a comparison was made between two classification algorithms, namely Naïve Bayes Classifier (NBC) and Random Forest because it is not yet known what algorithm is best in classifying chronic kidney disease (CKD). Both algorithms are evaluated based on performance metrics such as accuracy, precision, recall, and confusion matrix. The results of the evaluation showed that in a dataset of 400 samples, the performance  of the Naïve Bayes Classifier (NBC) algorithm obtained an accuracy of 94%, while Random Forest had an accuracy of 93%. Then in the small dataset (158 data), Random Forest got a better accuracy score with 87% compared to the Naïve Bayes Classifier (NBC) of 78%. Based on the results of the evaluation, Random Forest has a more stable performance on small datasets, while Naïve Bayes Classifier (NBC) provides higher performance on larger datasets in the context of chronic kidney disease classification.

The growth of e-commerce in Indonesia has led to an increase in product reviews, including for beauty products on Tokopedia and Shopee. These reviews serve as important sources of information to assess consumer satisfaction; however, manually analyzing thousands of reviews daily is impractical. This study applies Natural Language Processing (NLP) with Naive Bayes, C4.5, XGBoost algorithms to classify sentiment in Indonesian-language reviews. The dataset used consists of 76,256 reviews labeled as positive, negative, and neutral. The research stages include text preprocessing, feature representation using BoW and TF-IDF, data balancing through SMOTE, and model performance evaluation based on accuracy, precision, and recall. Differences in results among the algorithms were analyzed using ANOVA. The results show that Naive Bayes achieved the highest accuracy at 67.71%, followed by XGBoost at 65.91%, and C4.5 at 58.39%, with Naive Bayes performing best in identifying positive and negative sentiments, while XGBoost and C4.5 handled more complex data patterns effectively. These findings provide guidance for sentiment analysis in Indonesian and support businesses in obtaining automated insights from customer reviews to improve product quality and services.

Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.

WPS Office stands for Writer, Presentation, and Spreadsheets—a software suite offering diverse office functions, including document processing, spreadsheet creation, and presentation tools. This study analyzes user satisfaction levels and the influence of the variables Content, Accuracy, Format, Ease of Use, and Timeliness on WPS Office application users in Jambi City, using the End User Computing Satisfaction method. Data were gathered through an online questionnaire distributed to students in Jambi City who had used the application; created via Google Forms, it garnered 385 responses. Post-collection, analysis was conducted using Structural Equation Modeling in SmartPLS software version 4. Of the five hypotheses tested, four were accepted. The results reveal that accuracy, format, ease of use, and timeliness positively and significantly influence user satisfaction, while content shows no significant effect.

The Indonesia Pintar Program (PIP) is an educational assistance program for students from underprivileged families, but determining the eligibility of recipients still faces obstacles in the form of subjectivity and data imbalance. This study aims to classify the eligibility of high school students receiving PIP in Jambi City using data mining methods. The SMOTE technique was applied to overcome class imbalance, and Gain Ratio feature selection was used to determine important attributes. The dataset used consisted of 19,596 student data with a training data distribution of 70% and testing data of 30%. The classification process used the Naïve Bayes, Decision Tree (J48), and Random Forest algorithms with the Use Training Set, 5-Fold, and 10-Fold Cross Validation testing schemes. The results show that SMOTE improves model performance, but feature selection in some cases reduces accuracy. Overall, Random Forest without feature selection provides the best results with an accuracy of 93.33% and is recommended as the most effective model for objectively determining PIP recipient eligibility.

Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify user opinion tendencies based on textual reviews. This study analyzer user reviews of the Maxim application on the Google Play Store and compares three Machine Learning algoritmhs-Naïve Bayes, Support Vector Machine (SVM), and CatBoost-in classifying sentiment. The research stages include data collection, text preprocessing, feature extraction using TF-IDF and Chi-Square, class balancing using SMOTE, and performance evaluation through Accuracy, Precision, Recall, and F1-Score. ANOVA is used to examine the influence of feature selection on model performance. The results show that each model exhibits different performance level across the tested feature combinations. The CatBoost achieved the highest accuracy of 99,26% and demonstrating the most stable performance. Meanwhile, the Naïve Bayes and SVM models experienced performance decreases experiments, especially after applying SMOTE. These findings indicate that the choise of algorithm, feature extraction method, and class balancing technique significantly affects classification outcomes. Overall, CatBoost is identified as the best-performing model, providing more consistenst classification result in accordance with the characteristics of the user reviews.

The development of digital technology has led to an increase in the use of short video-based entertainment applications, including the Melolo application. However, the free version still has various complaints, such as inconsistent subtitles, unintuitive navigation, force close glitches, and unstable advertisements, so user satisfaction analysis is needed. This study aims to measure the level of satisfaction of users of the free version of the Melolo application using the End User Computing Satisfaction (EUCS) method, which covers five variables, namely content, accuracy, format, ease of use, and timeliness. Data was collected through an online questionnaire of 385 Melolo app users in Jambi City and analyzed using Structural Equation Modeling (SEM) with the help of SmartPLS 4. The results showed an R-Square value of 0.546, indicating that the model was able to explain 54.6% of the changes in user satisfaction levels. The variables of content and timeliness were found to have a significant effect on user satisfaction, while accuracy, format, and ease of use had no significant effect. These results indicate that content quality and system timeliness are the main factors in increasing user satisfaction. Therefore, Melolo app developers are advised to maintain content quality and improve system performance and stability to optimize the user experience.

Data mining is a technique of several fields of science to find previously unknown relationships in the data warehouse so that it becomes an information that can be used later. The unwise use of electricity will of course have an impact on the high use of electricity, therefore it is expected that every community understands the effort to use electricity wisely. Therefore, authors perform analysis of data mining on these electrical usage data in order to know which is a small, medium and large category. The authors use data on electrical use questionnaire as much as 200 data which is then presented into the ARFF format. In performing author analysis using WEKA Tools. The method used is Naive Bayes classification method with the greatest percentage of accuracy obtained using the Use Training Set Correctly of 80.5%, using a 5-Fold Cross Validation Correctly of 75%, and using 10-Fold Cross Validation amounted to 74%. While the result of the selection of the attributes using the algorithm classifier attribute evaluation (ClassifierAttributeEval) is stated that the most influential attribute against the electrical power usage classification is Electonic Goods.

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.

This study examines Japanese advertising and product-information texts on Shiseido Japan’s official website (www.brand.shiseido.co.jp) that grammatically prevent readers from construing statements as universal claims (“always” or “true for everyone”). It addresses two problems: how universal readings are blocked through grammatical construction in this register, and how the main blocking mechanisms differ in limiting generalisation and managing scope. The data consist of sentence-level usage, precautionary, and quality-related statements that plausibly invite broad general interpretations. Seven analytically representative tokens are used as illustrative evidence, covering wake-negation, baai-based case framing, and event/occasion packaging with V-ru koto ga aru, including rare-event calibration with mare ni and layered conditional framing. The study employs qualitative, theory-driven grammatical analysis focusing on clause structure, embedding, nominalisation, connective relations, and the compositional contribution of key markers. The results identify recurring templates with distinct structural signatures. Wake-negation blocks over-strong uptake by denying a candidate inference (…to iu wake de wa arimasen). Case framing with baai shifts categorical commitments into situation-restricted possibility (…baai ga arimasu), including complex variants that add causal linkage, avoidance marking, and directive closure. Event/occasion packaging with koto plus existential predication (…koto ga arimasu) presents anomalies as contingent occurrences, and it can be triggered by causal conditions (e.g., temperature change) or conditional frames (…to). Rare-event marking with mare ni further calibrates frequency and often co-occurs with contrastive reassurance about quality. Overall, universal-blocking emerges as a set of non-redundant grammatical routes that constrain inference, situational domain, and event profiling in a compact public informational genre.

Sarcasm is a literary device and one of the most expressive forms of figurative language, often used to convey humor, criticism, or emotional tension in both daily conversation and literature. This study explores the use of sarcasm in William Shakespeare‟s Much Ado About Nothing by applying Elizabeth Camp‟s (2011) typology, which classifies sarcasm into four types: propositional, lexical, illocutionary, and like-prefixed sarcasm. Using a qualitative descriptive method, the researchers collected all sarcastic utterances from the play, classified them according to Camp‟s framework, and analyzed their pragmatic functions in the dramatic context. The findings reveal a total of 50 sarcastic utterances, with propositional sarcasm being the most frequent (42%), followed by illocutionary sarcasm (28%), lexical sarcasm (24%), and like-prefixed sarcasm (6%). These results indicate that sarcasm serves as both a comedic and dramatic device, shaping character interactions, driving conflicts, and reinforcing Elizabethan cultural norms. Beatrice and Benedick‟s witty verbal duels exemplify how sarcasm fosters humor and intimacy, while Claudio‟s sarcasm highlights themes of honor and social tension. Overall, the study demonstrates that sarcasm in Shakespeare‟s play is not merely humorous banter but a sophisticated rhetorical strategy that enhances characterization, thematic depth, and audience engagement.

This study aims to explore the contribution of Christian Religious Education in developing the character of people with disabilities, grounded in Jean Vanier’s perspective on inclusive community and human dignity. People with disabilities are often marginalized due to persistent social stigma, which limits their access to education, meaningful participation, and employment opportunities, particularly within faith-based educational contexts. Employing a qualitative research approach through an in-depth literature review, this study examines key concepts in Christian Religious Education, the characteristics and lived experiences of individuals with disabilities, and the challenges and strategies associated with inclusive educational practices. The findings indicate that Christian Religious Education can function as an effective empowerment framework by integrating spiritual formation with the development of social skills, self-confidence, and communal belonging. Based on Jean Vanier’s inclusive vision, the study highlights practical implications for local churches, Christian schools, and faith-based communities, such as fostering inclusive learning environments, implementing participatory pedagogical models, and strengthening community-based support systems for people with disabilities.Furthermore, reducing social stigma through value-based education and community engagement emerges as a critical strategy to enhance educational participation and social integration. These findings contribute to the discourse on inclusive Christian education and offer contextual strategies applicable to local academic and ecclesial settings in promoting the dignity and empowerment of people with disabilities.

The increasing social and cultural diversity of contemporary societies has positioned schools as strategic spaces for nurturing inclusive attitudes and managing the challenges of pluralism. Many schools still face obstacles such as limited teacher readiness, inadequate curriculum representation of diversity, and school cultures that are not fully supportive of inclusive practices. This study aims to analyze school strategies in fostering students’ inclusive dispositions in response to pluralistic challenges. The research employed a descriptive qualitative approach through library research, using purposive sampling to select relevant scientific literature. Data were analyzed using an interactive model that involved data reduction, data display, and conclusion drawing. The findings indicate that participatory learning strategies, the application of Universal Design for Learning, and project-based learning are effective in strengthening students’ inclusive attitudes, empathy, and tolerance. The role of teachers as role models, the development of inclusive school culture, and active community involvement were identified as key supporting factors for successful inclusive education. The implications of this study highlight the importance of synergy between teacher professional development, curriculum adaptation, and school policy reinforcement to establish equitable, inclusive, and sustainable educational practices in pluralistic contexts.

This article discusses the concept of fitnah in Islam, focusing on its definition, forms, risks, and social impacts. Fitnah, referring to false accusations, deceit, and the spreading of misleading information, can pose a serious threat to individual integrity and social balance. The study employs a qualitative descriptive approach with a literature review from classical Islamic texts, Quranic verses, the teachings of the Prophet, and the latest scholarly articles from trusted sources. The findings indicate that fitnah is a significant moral and social violation that can lead to various issues, such as conflicts, defamation, social division, and the loss of trust within society. The study also emphasizes the importance of Islamic ethical principles in preventing and addressing fitnah, especially in the digital age, where information can spread rapidly and widely. The study concludes that preventing fitnah requires an increase in public literacy, more ethical communication, and adherence to Islamic values that emphasize honesty and caution in disseminating information. These efforts are expected to reduce the negative impact of fitnah in social life and foster a more harmonious society.

This study aims to analyze the effect of the use of kahoot game media on the cognitive learning outcomes of moral beliefs in grade VII C MTS negeri 1 Tuban students. The background of this research is the low enthusiasm and learning outcomes of students in learning moral beliefs which are still dominated by conventional methods while the characteristics of agency students demand a more interactive and adaptive learning approach to technology. This study uses a quantitative approach with a quasi-experimental design of one group pretest design involving 34 students as a sample. This research instrument is in the form of a multiple-choice cognitive learning outcome test of 20 questions covering 4 levels of bloom taxonomy, namely remembering, understanding, applying, and analyzing. The data was analyzed using descriptive statistics and normalistic calculations, the results of the study showed a significant increase in students' cognitive learning outcomes where the average class score increased from 66.03 in the pre-test to 80.29 in the post-test with an engine value of 0.42 which was in the medium category. These findings identify that kahood media has a positive impact on improving students' cognitive learning outcomes.

This study was motivated by the low level of internalisation of religious values in Islamic Religious Education, especially at the madrasah level, where the process of instilling valuesoften stops at the cognitive level. This study aims todescribe the process of internalising religious values through the film Laut Tengahand assess its effectiveness in improving students' understanding andappreciation of religious values at MTs Manbail Futuh 2 Bancar. The approach used was qualitative with a case study design, because the phenomenon under reviewwas contextual and required in-depth understanding. Datawere collected through direct observation during learning, interviews with teachers andstudents, as well as documentation of learning tools and film clips. Data analysiswas carried out through data condensation, data presentation, and drawingconclusions using the Miles, Huberman, and Saldaña model. The results showedthat the film Laut Tengahwas effective in facilitating the internalisation of religious values through the visual,emotional, and cognitive engagement of students. Students were able to identify values such ashonesty, patience, discipline, gratitude, and social awareness, anddemonstrated appreciation reflected in their self-reflections. With teacher guidance, films became a medium capable of connecting values with the real experiences ofstudents.

This study aims to analyze the implementation of the Merdeka Curriculum in Fiqh subjects and how its application affects the improvement of students' understanding. Using the library research method, this study reviews various recent literature in the form of scientific articles, accredited journals, academic books, and relevant policy documents. The results of the study show that the Merdeka Curriculum is conceptually aligned with the principles of Islamic education, especially in terms of character development, freedom of learning, and student-centered learning. However, the implementation of this curriculum in Fiqh learning still faces a number of challenges, such as teachers' limited understanding of the new curriculum approach, a lack of project-based contextual teaching materials, and difficulties in conducting authentic assessments on aspects of worship and morals. Technical challenges such as the availability of facilities and access to technology also hamper the effectiveness of learning. This study emphasizes the need for continuous teacher training, the development of open-access learning resources, and improved infrastructure support in order to strengthen the implementation of the Merdeka Curriculum. Overall, this curriculum has significant potential to improve students' understanding of Fiqh if it is supported by appropriate and collaborative implementation strategies.