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This study compared the performance of SARIMA and Prophet models in forecasting daily close prices of three major Indonesian banking stocks: BBCA, BBRI, and BMRI, using data from January 2020 to March 2026. Data were retrieved via the yfinance library, preprocessed, and split into 80% training and 20% testing sets. SARIMA modeling followed the Box-Jenkins procedure, while Prophet was configured with a Lag-1 regressor, weekly and monthly seasonality, Indonesian public holidays, and log transformation. Model performance was evaluated using MAPE, MSE, and Dstat metrics. Results showed that SARIMA outperformed Prophet in MAPE and MSE across all six stock-variable combinations, with MAPE values ranging from 1.3368% to 1.9386% for SARIMA and 1.5992% to 2.2300% for Prophet. However, Prophet demonstrated marginally higher Dstat values in several series. Both models achieved "Very Good" forecasting accuracy. A web-based forecasting system was also developed using Streamlit to make the models accessible to investors.

This study analyzes the minimum age of marriage in Islamic jurisprudence and compares it with the positive law regulations in Indonesia and Malaysia. Using a normative legal method with comparative and conceptual approaches, the study draws on primary sources, including the Qur'an, hadith, Law Number 16 of 2019 on Marriage in Indonesia, and the Islamic Family Law (Federal Territories) Act 1984 in Malaysia. The analysis focuses on how Islamic legal principles concerning marriage eligibility are interpreted and incorporated into contemporary legal frameworks in both countries. The findings reveal that Islamic jurisprudence (fiqh) associates marital readiness with the concept of baligh (puberty) without prescribing a specific numerical age, whereas state law establishes fixed minimum age requirements to safeguard the rights and welfare of women and children. Indonesia sets the minimum marriage age at 19 years for both males and females, while Malaysia prescribes 18 years for males and 16 years for females, with judicial dispensation available in both jurisdictions under certain circumstances. These legal arrangements demonstrate each country's effort to harmonize classical Islamic jurisprudence with contemporary social protection objectives through institutional ijtihad, reflecting a balance between religious principles, legal certainty, and public welfare in regulating marriage.

This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

This research aims to classify students’ lifestyles using the K-Nearest Neighbors (KNN) algorithm. The dataset consists of 392 high school students obtained from Kaggle, with key attributes including study hours, social media usage, Netflix viewing duration, attendance, sleep quality, internet quality, mental health, and extracurricular activities. KNN was chosen for its simplicity in distance-based classification, measured using Euclidean Distance. The data was divided into training and testing sets, then evaluated using accuracy and a confusion matrix. The results show that KNN effectively classifies students’ lifestyles into four categories: healthy, less active, at risk, and highly at risk. This classification is expected to assist educational institutions, parents, and students in understanding lifestyle patterns and their impact on academic performance and mental well-being. Furthermore, this study emphasizes the relevance of applying machine learning in education, aligned with Islamic values concerning health, discipline, and the optimal use of time.

This research aims to classify students’ lifestyles using the K-Nearest Neighbors (KNN) algorithm. The dataset consists of 392 high school students obtained from Kaggle, with key attributes including study hours, social media usage, Netflix viewing duration, attendance, sleep quality, internet quality, mental health, and extracurricular activities. KNN was chosen for its simplicity in distance-based classification, measured using Euclidean Distance. The data was divided into training and testing sets, then evaluated using accuracy and a confusion matrix. The results show that KNN effectively classifies students’ lifestyles into four categories: healthy, less active, at risk, and highly at risk. This classification is expected to assist educational institutions, parents, and students in understanding lifestyle patterns and their impact on academic performance and mental well-being. Furthermore, this study emphasizes the relevance of applying machine learning in education, aligned with Islamic values concerning health, discipline, and the optimal use of time.

The rapid growth of TikTok Shop as a social commerce platform has led to the emergence of 'toxic content' produced by influencers as a marketing tactic that effectively influences Generation Z's shopping behavior. This study investigates the role of TikTok Shop influencers and their 'toxic content' in stimulating impulse buying behavior among Generation Z consumers in the fast fashion sector, focusing on the psychological mechanisms involved, the most frequently purchased product categories, and the broader impacts of these consumption patterns. A qualitative research approach with a phenomenological design was used, and data were gathered through in-depth interviews with eight informants: six Generation Z consumers aged 19–24 years and two active TikTok Shop fashion influencers, selected via purposive sampling. The data were analyzed using the interactive model of Miles, Huberman, and Saldaña (2014). Findings show that 'toxic content' acts as a stimulus within the Stimulus-Organism-Response (S-O-R) theoretical framework, triggering impulse buying within minutes by evoking positive emotions and urgency. The Fear of Missing Out (FOMO) mechanism, driven by emotional triggers like time scarcity, social proof, and exclusivity, was the most dominant psychological factor. The most commonly purchased items were tops, coordinated sets, aesthetic bottoms, locally-made footwear, and accessories. The effects of 'toxic content' are financial, psychological, social, and environmental. This study advances influencer marketing literature and provides insights into Generation Z’s consumer behavior in the social commerce age.

Marriage in Islam is not only a civil bond, but also a very strong bond (mitsaqan ghalidzan) to obey Allah's commands and carry it out as worship. The purpose of marriage in Islam is to fulfill religious guidance in order to establish a harmonious, prosperous and happy family, where the relationship between husband and wife is in a strong bond in accordance with the commands of Allah SWT. The purpose of this study is to analyze the legal provisions regarding marriage dispensation, analyze the judge's legal considerations in Decree Number 46 / Pdt.P / 2024 / PA.Lpk., and analyze the implementation of the principle of the best interests of children in determining marriage dispensation at the Lubuk Pakam Religious Court. The research method used is normative juridical legal research with a document study approach to laws and court decisions. Data sources consist of primary data obtained through interviews with judges, clerks, and religious figures, as well as secondary data in the form of primary, secondary, and tertiary legal materials. The data analysis technique uses qualitative analysis. The results of the study show that: Legal provisions regarding marriage dispensation are regulated in Article 7 of Law Number 16 of 2019 which sets the minimum age limit for marriage at 19 years for men and women, with exceptions where dispensation can be requested from the Court for urgent reasons. Supreme Court Regulation Number 5 of 2019 regulates the procedure for examining marriage dispensation cases which requires the judge to listen to the child's statement, verify the absence of coercion, and consider the best interests of the child. The judge's legal considerations in Decision Number 46/Pdt.P/2024/PA.Lpk. have fulfilled formal requirements by considering the authority to adjudicate, the applicant's legal standing, the reasons for the request in the form of a very close relationship, the absence of coercion, the absence of obstacles to marriage, and the economic readiness of the prospective husband. The implementation of the principle of the best interests of the child has been carried out through providing advice on the risks of child marriage, listening to the child's opinions, and suggesting marriage postponement, although there are still limitations such as the lack of referrals to psychologists and minimal consideration of continuing education. The conclusion of this study is that the Lubuk Pakam Religious Court has implemented the provisions of marriage dispensation in accordance with applicable laws and regulations. The judge's legal considerations in Decree Number 46/Pdt.P/2024/PA.Lpk have fulfilled the formal and material aspects, however, the implementation of the principle of the best interests of the child still faces challenges from the normative, institutional, and socio-cultural aspects.

The phenomenon of authority trap occurs when local governments are burdened with achieving SDG 7 (“Affordable and Clean Energy”) targets without being granted adequate authority and resources. This study examines the context of West Java Province and Bandung City, which face complex clean energy governance due to fragmented authority across levels of government. At the national level, Government Regulation No. 40/2025 reinforces emission reduction and the transition toward Net-Zero Emissions by 2060. West Java has established RUED No. 2/2019, which sets a minimum renewable energy share of 17% by 2025. However, the technical responsibilities for implementation (such as electrification and energy conservation) lie at the regency/municipal level. The analysis highlights fiscal disparities, PLN’s dominance, and national regulations that override local authority. Local innovations such as rooftop solar PV, solar-powered street lighting, the Green Building Mayor Regulation, and PPP schemes are evaluated for their effectiveness. This qualitative study combines policy document analysis and literature review. The findings show that the absence of strong local regulation (regulatory void) and the central dominance of PLN reinforce the authority trap, hindering synergy among stakeholders. The recommendations emphasize strengthening institutional collaboration, aligning local and national regulatory frameworks, and promoting innovative financing mechanisms to enable Bandung City to break free from the authority trap in achieving SDG 7 targets.

This study aims to analyze consumer purchasing behavior on e-commerce platforms using the Decision Tree algorithm as an easily interpretable classification method. The dataset used consists of 12,330 transaction records with 18 attributes representing visitor characteristics and user activities during interactions with the e-commerce platform. The research stages include data exploration to identify initial patterns, data preprocessing to handle missing values and class imbalance, splitting the data into training and testing sets, training the Decision Tree model, evaluating model performance, and visualizing the tree structure to analyze decision rules.The test results show that the Decision Tree model with a maximum depth of 3 achieves fairly good performance, with an average accuracy of 89.78%, precision of 69.82%, recall of 59.95%, and an F1-score of 64.51% for the buyer class. The visualization of the decision tree provides clear interpretation of the main attributes influencing purchasing decisions, thereby facilitating understanding for non-technical decision makers. Overall, this study demonstrates that the Decision Tree method is effective in modeling consumer purchasing behavior in e-commerce and can be utilized as a basis for data-driven business decision making, particularly in marketing strategies and improving sales conversion rates.

Hospitals often face difficulties in determining patient treatment priorities due to limited medical resources and the uncertainty of patient conditions. Conventional prioritization methods tend to rely on subjective judgment, which can lead to inconsistent decisions and delays in treatment. This study aims to apply fuzzy logic in a decision support system to determine patient priority levels more objectively and systematically. The proposed method utilizes a fuzzy inference system that processes several criteria, including the severity of symptoms, vital signs, patient age, and waiting time. These criteria are represented as fuzzy sets and evaluated using a set of inference rules to generate priority classifications. The results indicate that the fuzzy logic–based system is able to classify patient priorities more consistently and transparently compared to manual assessment. The system provides clear priority categories that can support medical staff in making faster and more accurate decisions. The findings imply that the implementation of fuzzy logic in hospital decision support systems can improve the quality of healthcare services, enhance fairness in patient handling, and optimize the allocation of medical resources, particularly in emergency and high-demand situations.

Thіs studу aіmеd to examіnе the eligibilitу оf tеасhеr honorаrium bу іmрlemеntіng a classifiсatіon method usіng thе Decіsіon Тree algorithm. Тhе primary іssuе addressеd in thіs rеsеаrсh is the absеnce of а fair and dаtа-driven salarу sуstеm аt SМA YPKPР. А сlаssificatіon aрproach was emрloуеd to сatеgorіze teасhers intо "Elіgіble" and "Not Elіgiblе" grоuрs basеd оn attributеs such аs tеachіng hоurs, hourly wаge, eduсatiоn lеvel, jоb роsіtіon, сеrtіfіcаtіоn allowаnсe, аnd schoоl status.Thе сlаssifiсatіon model was dеveloрed usіng RаріdМiner sоftwаrе. Тhе datasеt was dіvidеd into trainіng and tеsting sets usіng a sрlіt datа technique. Тhe modеl wаs еvaluatеd usіng metrісs such as acсuraсy, рrecisіоn, rеcall, and сonfusiоn matrіх. The rеsults indіcаtеd that thе Dесіsiоn Тree model аchіеved аn асcurасy оf 93.75% in сlаssіfуing tеаchеr honorarium еligіbіlity. Тeасhing hours and hourlу wаge werе idеntifіеd as thе twо most іnfluеntial variables іn the сlаssіfісаtіоn рroсеss.Аs a form of vаlіdatіоn, addіtionаl statistіcal аnalysis was соnducted usіng SРSS. The Рeаrsоn cоrrelаtіon tеst showеd а sіgnіficаnt relаtionshір bеtween teaching hours and hourlу wаge wіth thе tоtаl honоrаrіum rеcеivеd. Мultіplе .Lineаr rеgressiоn аnalysis resulted іn аn R Squаre valuе of 0.860, indicаting that 86% оf thе varіation in hоnorarium сan be eхрlаіnеd bу thе twо vаrіаblеs.Тhis study іs expесtеd tо serve аs а foundаtіоn for mоre objеctive аnd dаtа-drіven dеcisіоn-mаking іn thе tеaсhеr comреnsation sуstem. Тhe findіngs dеmоnstrаte thаt а combinаtiоn of datа minіng аnd stаtіstісаl аnаlуsis aрprоаches сan bе usеd to devеlop a trаnsparent, fair, аnd efficient sаlаrу system.

Hospitalization often triggers anxiety in children because of unfamiliar environments, invasive procedures, and disruption of daily routines. Pediatric nurses are expected to implement atraumatic care through developmentally appropriate, non‑pharmacological interventions. This community service program aimed to strengthen nurses’ and families’ skills in delivering puzzle‑based play therapy and to evaluate short‑term changes in children’s anxiety responses in the pediatric ward of Awal Bros Hospital, Batam. The program was conducted in December 2025 using an action-based approach: coordination with the ward team, preparation of graded puzzle sets and education media, bedside education for parents, and supervised play sessions. Four hospitalized children with different diagnoses (chemotherapy, childhood‑onset lupus erythematosus, febrile illness, and stoma care) participated. Anxiety was assessed before and after the session using an observation sheet and a faces anxiety scale adapted for clinical use. After a 20–30 minute session adjusted to the child’s developmental stage, all participants demonstrated lower anxiety scores and better cooperative behaviors. Caregivers also reported increased understanding of play therapy as part of holistic nursing care. Puzzle play therapy is feasible, low‑cost, and can be integrated into routine pediatric nursing to support coping during hospitalization.

Set theory is one of the fundamental concepts underlying the structure of discrete mathematics and computer science and plays a crucial role in understanding various advanced topics, such as relations, functions, mathematical logic, and other discrete structures. This concept serves as the foundation for developing a systematic and structured mathematical mindset. This paper aims to explain the fundamental concepts of set theory, review its significance in discrete mathematics, and outline how to present sets and basic operations on sets, such as union, intersection, complement, and difference. The method used in this paper is a literature study by reviewing, collecting, reading, and analyzing data from various relevant written sources, both textbooks and scientific articles. Through this study, students are expected to be able to understand set theory more comprehensively. Thus, it can be concluded that set theory is not merely an introductory topic, but rather a formal framework that defines the validity of logic and structure in modern discrete systems.  

The rapid growth of e-commerce platforms in Indonesia, particularly Tokopedia, has resulted in a large volume of consumer reviews containing valuable information regarding customer perceptions and satisfaction. However, manual analysis of such reviews is inefficient and prone to subjectivity, necessitating an automated approach based on machine learning. This study aims to classify the sentiment of sports product reviews on Tokopedia into positive, negative, and neutral categories by applying Logistic Regression, Support Vector Machine (SVM), and Random Forest using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. The data were collected through web scraping of Indonesian-language sports product reviews and processed through several preprocessing stages, including data cleaning, case folding, tokenization, stopword removal, and stemming. Feature representation was performed using TF-IDF to transform textual data into numerical vectors, after which the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of TF-IDF significantly improves the performance of all models, with SVM consistently achieving the most optimal performance compared to Logistic Regression and Random Forest. These findings demonstrate that classical machine learning algorithms combined with TF-IDF remain highly effective for sentiment analysis of Indonesian-language text. The implications of this study are expected to assist sellers in understanding customer opinions, support consumers in making informed purchasing decisions, and serve as a foundation for the development of sentiment analysis and recommendation systems on e-commerce platforms.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

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 aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

The development of digital technology has driven the  increasing use of e-modules as interactive learning media that support  independent and collaborative learning. The integration of pedagogical approaches and multimedia technology makes e-modules increasingly adaptive and effective in developing higher-order thinking skills. This study aims to map trends in the development and utilization of e-modules through a Systematic Literature Review (SLR)  of Scopus articles published in 2020–2024. From the selection process, 20 articles were analyzed based on their pedagogical approach, as well as the media and digital platforms used. The study results show that pedagogical approaches such as Problem-Based Learning, STEM, SETS, Discovery Learning, and Guided Inquiry have been consistently proven to improve critical thinking skills, learning outcomes, and 21st-century competencies. From a technological perspective, Android and flipbook platforms are the primary choices due to their flexible access and multimedia support such as video, animation, simulations, and interactive quizzes. These findings emphasize the importance of developing systematic, pedagogically-based e-modules supported by interactive multimedia.  Further research is needed to explore the integration of AR/VR, gamification, and artificial intelligence.   

This study aims to conduct a comparative analysis of curriculum development and teaching methods in Islamic education in Indonesia and Egypt. Both countries have a long tradition in the development of Islamic sciences, but they demonstrate different approaches in accordance with their respective social, cultural, and educational policy contexts. This study uses a literature review method with a descriptive qualitative approach through a review of journals, books, and Islamic education policy documents. The results of the analysis show that the Islamic education system in Indonesia emphasizes the integration of religious and general knowledge in its curriculum, in line with the spirit of religious moderation and the demands of modern society. Meanwhile, Islamic education in Egypt, especially through Al-Azhar University, emphasizes a traditional approach based on tafaqquh fi al-din with a focus on mastery of classical Islamic sciences. In terms of teaching methods, Indonesia is more adaptive to active and technology-based learning models, while Egypt maintains the talaqqi and memorization methods, but has begun to adopt modern pedagogical innovations. Overall, both education systems have their own strengths; Indonesia excels in contextualizing the curriculum, while Egypt sets an example in maintaining the authority and continuity of Islamic scholarship. These findings are expected to contribute to the development of a balanced Islamic education that integrates values and modernity.