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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.

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.

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.

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 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.

Islamic character education faces dual challenges: declining moral values among students and conventional teaching methods inadequate for developing higher-order thinking skills. This study examines the implementation of a smart learning environment (SLE) model to enhance metacognitive understanding in Aqidah Akhlak (Islamic Creed and Ethics) instruction among eighth-grade students at MTs Salafiyah Merakurak. Employing a mixed-methods action research design, involved 16 eighth-grade students divided into two groups. Data collection utilized classroom observations, semi-structured interviews, and learning documentation. The SLE model integrated Google Classroom, interactive video content, WhatsApp Group discussions, and Google Forms assessments to create a technology-enhanced learning ecosystem. Findings revealed substantial improvements in students' metacognitive capacities: planning skills increased from 25% to 75%, monitoring abilities rose from 31% to 81%, and evaluation competencies grew from 19% to 69%. Students demonstrated enhanced learning autonomy, active participation in collaborative discussions, and improved self-reflection on content comprehension. The SLE approach successfully fostered engaging learning experiences while facilitating deeper internalization of Islamic ethical values. However, implementation encountered constraints including limited technological infrastructure and varied digital literacy levels among students. This research underscores the critical need for developing teachers' digital competencies and strengthening madrasah technological infrastructure to optimize technology-integrated Islamic education.

Contemporary developments in science, technology, and socio-cultural dynamics have given rise to various new fiqh issues (masailul al-fiqhiyyah al-mu‘ashirah) that were not explicitly discussed in classical Islamic jurisprudence. These contemporary fiqh issues demand contextual ijtihad that is responsive to current realities while remaining grounded in Islamic legal principles. This study aims to analyze contemporary fiqh issues and examine their implications for the moral formation of students in Islamic education. This research employs a qualitative library research approach by analyzing classical fiqh literature, contemporary fiqh studies, and relevant educational theories. The findings indicate that contemporary fiqh issues such as digital ethics, biomedical technology, and socio-economic practices carry significant moral dimensions that directly influence students’ attitudes and behavior. Integrating contemporary fiqh discourse into Islamic education encourages critical thinking, moral reasoning, and ethical awareness among learners. Therefore, contemporary fiqh learning not only functions as a legal reference but also as a strategic instrument for strengthening students’ moral character in accordance with Islamic values. The study implies that Islamic education institutions should contextualize fiqh instruction to address contemporary moral challenges faced by students.

The discourse on the attributes of God has developed to include the issue of His physical attributes, as described in the texts, which state that God has hands, a face, a chair, a throne, and so on. This article employs a literature study as its method. Literature data are secondary sources, meaning the researcher obtains material indirectly and not from original, first-hand sources. Such sources may contain the biases or perspectives of their authors, and the researcher does not always have full control over how the data were collected or organized according to their original purpose. The results of this study indicate that the existence of God’s attributes is clearly explained by Abduh: the attributes that must be believed by the faithful are derived from the guidance of reason and the information provided by Islamic law. Regarding the classification of God’s attributes, there are 20 attributes that are obligatory for God, 20 that are impossible for God, and attributes that are jaiz (possible) for God. Summarizing the attributes of God mentioned in Surah Al-Qashash verses 68–70: God is the Creator, God is free to choose, God is Most Holy, God is All-Knowing, God is One, God is worthy of praise, God is Most Wise, and to God all things will return. One of the characteristics of a believer is to affirm and have certainty in the existence of God while distancing themselves from ideologies that negate or oppose God.

Among the afterlife in Islamic belief is the grave, where at the beginning of this period it is believed that the soul will be questioned by the angels Munkar and Nakir about his faith and deeds in the world. The purpose of this writing is focused on elaborating on Islamic eschatology in the story of Umar bin Khattab about post-death procedures. Literature research to find out a more in-depth discussion on the topic of post-death procedures by collecting, analyzing, and synthesizing information from books, journals, articles, and other relevant documents, was analyzed content. Findings: The story of Umar bin Khattab with the Angels Munkar and Nakir shows his concern for the welfare of the people. This reflects responsible leadership and is oriented towards the long-term good for all Muslims. Although this particular narrative is not widely documented in classical Islamic texts, it reflects a broader theme in Islamic teachings about the mercy and compassion of Allah and the angels. This story also highlights the exemplary character of Umar and his concern for the welfare of others, even in the afterlife. Conclusion: This story is about a leader who always thinks about the welfare of the people, including those who will face questions in the grave. Novelty: This finding highlights visionary leadership based on the essence of Islam that fosters true happiness in this world and the hereafter, considering the challenges of modern leadership oriented toward the afterlife.

This study aims to analyze the influence of macroeconomic variables in the form of the dollar exchange rate, inflation, and Gross Domestic Product (GDP) on the Composite Stock Price Index (JCI) in Indonesia for the period 2010–2024. The research method used is a quantitative approach with multiple linear regression analysis using time series data obtained from Bank Indonesia, the Central Statistics Agency (BPS), and the Indonesia Stock Exchange (IDX). The data analysis technique was carried out through classical assumption tests and hypothesis testing to determine the relationship between variables. The results of the study show that partially GDP has a significant effect on the JCI, while inflation and the dollar exchange rate tend not to have a significant effect. However, simultaneously these three variables have a significant influence on the JCI. These findings show that macroeconomic stability is very important in maintaining the performance of the capital market in Indonesia and can be a reference for investors in making investment decisions. In addition, the results of the study confirm that national economic growth is the main indicator that market participants pay attention to in assessing investment prospects. Therefore, the government needs to maintain economic stability through effective and sustainable fiscal and monetary policies.

This study aims to analyze the effect of the Current Ratio (CR), Debt-to-Equity Ratio (DER), and Return on Equity (ROE) on the stock prices of healthcare companies classified as sharia-compliant on the Indonesia Stock Exchange (IDX) for the 2020–2024 period. The background of the study is motivated by notable stock price fluctuations among sharia healthcare issuers, such as the sharp decline in PT Kimia Farma Tbk and price dynamics of other issuers including KLBF, MIKA, PEHA, and SIDO. The analysis uses a quantitative approach applying Partial Least Squares – Structural Equation Modeling (PLS-SEM) implemented in WarpPLS 8.0. The results indicate that CR does not have a significant effect on stock price (p = 0.174), while DER has a negative but not statistically significant effect (p = 0.484). In contrast, ROE has a positive and significant effect on stock price (p < 0.001), making ROE the dominant factor influencing investor interest. Simultaneously, the three independent variables explain only 20.2% of stock price variation, while the remaining 79.8% is influenced by factors outside the research model. The Tenenhaus goodness of fit (GOF) value of 0.450 suggests the research model has good overall quality despite the limited explanatory power of the tested financial variables.

Perfume is often used to enhance self-confidence and build a positive self-image, particularly among young people in social and academic settings. Thus, purchasing decisions are shaped not only by functional needs but also by emotional and social factors. This study examines how live streaming, electronic word-of-mouth (E-WOM), and celebrity endorsements influence consumers’ decisions to buy Evangeline perfume. A quantitative survey method was employed using purposive sampling, with respondents limited to FEB Udinus students who have experience with and knowledge of Evangeline products. This group was selected as they represent an active consumer segment for lifestyle and cosmetic goods. Data from 140 respondents were analyzed using multiple linear regression along with the classical assumption test. The results show that all three marketing variables significantly and positively affect purchase decisions, with live streaming being the most influential factor. These findings highlight the importance of interactive engagement, peer validation, and positive selection cues in shaping consumer trust and perception. Academically, this study contributes to consumer behavior and digital marketing research. Practically, the results suggest that perfume companies should prioritize live streaming campaigns, strengthen E-WOM strategies, and leverage celebrity endorsements to improve marketing effectiveness and increase sales.

The objective of this study is to directly analyze and illustrate the compositioneof the auditecommittee, which consists of financial knowledge, independence and the quantity of members on the committee, concerning the financial statement quality of energy sector industries listed on the IDX in 2023-2024.High-quality financial statements are a crucial component reflecting the outcome of the accounting process and are vital for stakeholders in decision-making. Despite regulatory requirements for audit committees, corporate financial statements in Indonesia often contain earnings management or accounting irregularities, indicating that the audit committee's very existence is insufficient to guarantee financial statements' quality. A numerical approach with a causal-comparative approach is utilized in this investigation. The secondary quantitative data are obtained from companies’ yearly financial statements, annual reports, and corporate governance disclosures published on the official IDX website. The data are examined using EViews software for panel data regression, going through many steps, including descriptive statistics, classical assumption testing, panel data model selection, and regression analysis for hypothesis testing. The audit committee's size, objectivity, and financial acumen make up the study's independent variables. Meanwhile, financial statement quality as the dependent variable is measured through earnings quality proxy using the discretionary accruals calculation approach (Jones model or Modified Jones model). Specifically, this research seeks to deliver theoretical and practical benefits for regulators in formulating corporate governance policies, give companies a comprehension of the importance of an effective audit committee, and help investors make informed investment choices.

This study aims to analyze the influence of advertising, price, and brand image on purchasing decisions for Madu Sumber Podang in Kediri Regency. The research employed a quantitative descriptive approach with a sample of 136 respondents selected through accidental sampling. Data were collected using a questionnaire and analyzed using classical assumption tests, multiple linear regression, t-test, F-test, and coefficient of determination (R²). The results indicate that advertising has a positive but insignificant effect on purchasing decisions. Price has a negative and significant effect, while brand image has a positive and significant effect on purchasing decisions. Simultaneously, the three independent variables have a significant influence on purchasing decisions. The determination coefficient (R²) value of 0,388 shows that 38.8% of the variation in purchasing decisions is explained by advertising, price, and brand image, while the remaining portion is influenced by other variables outside the study. These findings highlight the importance for business actors to improve promotional effectiveness, pricing strategies, and brand image strengthening to enhance consumer purchasing decisions for Madu Sumber Podang.

In an increasingly competitive workplace, employee performance is one of the important factors determining a company’s success. PT Gadjahmada Nusantarajaya, a company engaged in the service and trade sector, faces the challenge of maintaining and improving its employees’ performance. Internal factors such as organizational culture, work communication, and work discipline are thought to have a significant influence on employee performance. Based on this, this study was conducted to determine the influence of organizational culture, work communication, and work discipline on employee performance at PT Gadjahmada Nusantarajaya. The research questions in this study are: (1) Does organizational culture influence employee performance? (2) Does work communication influence employee performance? (3) Does work discipline influence employee performance? and (4) Do organizational culture, work communication, and work discipline simultaneously influence employee performance at PT Gadjahmada Nusantarajaya? This research is quantitative. Data were obtained through primary data collected using a questionnaire, as well as secondary data from company documents. The study population was all 49 employees of PT Gadjahmada Nusantarajaya. The sampling technique used saturated sampling; thus, the entire population was used as the research sample. Data analysis used validity tests, reliability tests, classical assumption tests, multiple linear regression analysis, and hypothesis tests (t-tests and F-tests). The results showed that partially (t-tests) the variables of organizational culture, work communication, and work discipline had a significant effect on employee performance. Simultaneously (F-tests), these three variables also had a significant effect on employee performance at PT Gadjahmada Nusantarajaya.