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This study investigates how Lembaga Musik Pervagatus, a group that does not display Islamic identity, creates an Islamic musical image when performing at religious events in Islamic campuses.  This study investigates how visual elements, song selection, and stage interaction influence audience perception. It does so using qualitative methods through interviews and performance observations.  How the band's performative identity is influenced by attributes such as appearance, personality, cultural values, and audience relations is studied using Kapferer's Brand Identity Prism model.  Conversely, Stuart Hall's theory of representation helps explain how Islamic meaning emerges in the context of events. The results show that the image of Islamic music attached to Pervagatus does not originate from the band's original identity; rather, it is a construction of meaning influenced by the context of religious events and the audience's interpretation of the song ‘Maulana Ya Maulana’ that they performed.  This perception was further reinforced by their neat, polite, and enthusiastic appearance.  The results show that the relationship between performance, context, and audience interpretation shapes the image of Islamic music on campus.

The Problem-Based Learning (PBL) model is a teaching strategy that focuses on students being the main participants in their education by engaging in the resolution of actual problems. This article looks into how PBL is applied in the classroom, its effects on enhancing students' critical thinking abilities, and the primary attributes of this model that aid in developing problem-solving skills. PBL consists of five key phases: identifying a challenge, organizing research efforts, performing both individual and group inquiries, gathering and sharing findings, and reflecting on the process. These phases create a learning experience that promotes teamwork, self-directed research, and thorough analysis of information. Findings from this research reveal that PBL significantly enhances students' critical thinking skills, particularly in areas like analysis, assessment of evidence, solution formulation, and articulating arguments logically. Furthermore, features of PBL, which include prioritizing students, addressing real-world issues, fostering self-directed learning, encouraging group collaboration, and positioning the teacher as a guide, play a crucial role in enhancing problem-solving capabilities. By tackling issues that relate to their daily lives, PBL motivates students to independently build knowledge, foster innovation, and reinforce their autonomy in the learning journey.

This study analyzes creditworthiness assessment and predicts non-performing loan (NPL) risk using the Naïve Bayes algorithm at BPR Ukabima Lestari, Jambi Branch. A quantitative data mining approach with probabilistic classification is applied. The dataset includes borrower attributes such as age, occupation, income, loan amount, tenor, collateral, and repayment history. Research stages comprise data preprocessing, model development, and performance evaluation using accuracy, precision, recall, and F1-score implemented in RapidMiner. The results indicate that the Naïve Bayes model achieves 99.58% accuracy, demonstrating strong capability to predict potential problem loans accurately and efficiently, supporting data-driven credit decisions and strengthening credit risk management in microbanking institutions.

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.

In Indonesia, micro, small, and medium enterprises (MSMEs) have long been recognized as a very important business sector due to their various real roles in the economy. However, MSMEs often face many obstacles in running their businesses. More incentive support from various parties, especially the government, is needed for the development of MSMEs. In order to overcome the problems faced by MSMEs and support their sustainability, a KKU (Business Field Study) activity was carried out by Group 15 at the KKU-11 of the Institute of Technology and Business (ITB) Adias Pemalang at the MSME “Ev_Kids Product.”After conducting an observation phase, several problems faced by the SME were identified, including the lack of social media for marketing, failure to calculate the Cost of Goods Sold (COGS) and maintain simple accounting records, absence of a business logo, banners, promotional image designs, organizational structure, business profile, product catalog, stamps, receipt books, and product attributes. Additionally, the SME had never produced a production video or promotional video.After conducting the observation phase, KKU actors formulated several business development assistance programs, namely creating social media accounts, calculating the Cost of Goods Sold (COGS), creating simple bookkeeping, creating a business logo, banners, promotional image designs, organizational structure, business profile, product catalog, stamps, note books, and product attributes in the form of hangtags. They also created production videos and promotional videos.

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.

This study aims to design and build a population distribution application in Demak Regency in 2025 using a Geographic Information System (GIS) approach. The study focuses on three main variables: population, population density, and population growth rate per sub-district. The author used the research method of collecting data and references that can later strengthen the results of this study and the application design using the waterfall model. Non-spatial data, namely data in the form of population information, was obtained from the Central Statistics Agency of Demak Regency, while spatial data is data related to regional administrative boundaries. Data processing was carried out using QGIS 2.18 through the stages of joining attributes, classification using the Natural Breaks (Jenks) method, and thematic map creation. The results show that population distribution is uneven. Demak Kota, Karangtengah, and Sayung sub-districts have the highest number and density, while coastal sub-districts such as Wedung and Bonang have low densities. The highest population growth rate is in Karangtengah sub-district at 0.8%. The application of GIS has proven effective in visualizing population distribution and supporting spatial-based regional development planning.  

Job placement is a brief and concise summary of the process of placing employees in positions that match their expertise, skills, and knowledge within an organization. Human capital refers to the knowledge, skills, competencies, and attributes of individuals that contribute to economic and social performance. Teamwork is one of the important factors in increasing effectiveness and productivity in an organizational environment. Employee performance is one of the main indicators in determining the success and competitiveness of an organization. This study aims to analyze the effect of job placement, human capital, and teamwork on employee performance at PT. Faithfull The Brand. This study was conducted at PT. Faithfull The Brand. The research population was employees of PT. Faithfull The Brand. The sample in this study was 87 respondents who were determined based on the Slovin formula. The data analysis technique used was multiple linear regression analysis using the SPSS program. The results of testing the hypothesis stated that job placement had a positive and significant effect on employee performance at PT. Faithfull The Brand, human capital had a positive and significant effect on employee performance at PT. Faithfull The Brand, and teamwork had a positive and significant effect on employee performance at PT. Faithfull The Brand.

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.

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.

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.

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.

Diabetes Mellitus is a chronic disease that requires early detection to prevent serious complications. This study aims to implement the Artificial Neural Network (ANN) algorithm with the Backpropagation method to predict the risk of diabetes. The dataset used is the Pima Indians Diabetes Dataset, consisting of 768 medical records with 8 feature attributes. This study employs the Multi-Layer Perceptron method with an architecture of 8 input neurons, two hidden layers, and 1 output neuron. Model evaluation is conducted using a Confusion Matrix to measure accuracy levels. The test results show that the model is capable of predicting diabetes diagnosis with an accuracy rate of 76.62%. Based on these results, it can be concluded that the Backpropagation algorithm is effective as an alternative method for early detection of diabetes, although further development is needed to improve the model's sensitivity to positive cases.  

This study investigates the impact of board characteristics on the financial performance of non-financial companies listed in the JII70 index in Indonesia. Motivated by the ongoing debate on the effectiveness of corporate governance mechanisms in enhancing firm outcomes, particularly within Sharia-compliant markets, this study focuses on three key board attributes: board size, board independence, and female representation on the board. Using a quantitative causal approach and panel data from 25 companies over the period 2020–2023, the study employs a fixed effect model to evaluate the relationship between board structure and financial performance measured by Return on Assets (ROA). The results show that board size has a positive and significant effect on firm performance, indicating that larger boards may enhance oversight capacity and provide broader resources beneficial to strategic decision-making. Conversely, board independence and board female representation do not exhibit significant effects on financial performance, suggesting that their roles may be more symbolic or constrained by institutional and contextual factors in the sampled companies. These findings highlight the importance of understanding corporate governance not merely in structural terms, but in relation to functional effectiveness and contextual maturity. The study offers implications for regulators, companies, and governance reform initiatives, particularly regarding strengthening substantive roles of independent and female commissioners in improving firm performance within Sharia-compliant markets.

This research is purposely conducted to evaluate and compare the effectiveness, efficiency, applicability  and appropriateness of modern firefighting systems installed on board ships. As a result, the review attempt to  ascertain the significance of firefighting systems on ships in terms of their safety, types, structure, principles as  well as installations. In this way, the review tries to justify the importance of these systems on board ships. The design of this survey revolves around three basic elements that incorporate the complete firefighting  procedure. These components include: modern technology of firefighting, different types of systems that are  used on board ships and people traits in terms of drilling, firefighting training to deal with any incident of fire.  In the previous decade, the globe has experienced massive destructions, harms and injuries resulting from fire incidents on ships. This has triggered and ignited enhanced fabrication and staging of new practices,  technologies and inventions aimed at efficiently deal with fire incidents so as to support the safekeeping and  reliability on these fire systems on board ships. Equally, through this technological enhancement, the  firefighting systems have been designed to facilitate rapid detection capable of differentiating between real  smokes or flames indicators in case of a fire event on board ship. In recent times, these technological  developments have been viewed as strategies that help ship owners to ensure better protection of the crew’s life,  ship’s inventories as well as minimising on the possible losses that occur as a result of fire events on board ship.  Therefore, with the review of the recent or latest methods and technologies for speedy fire detection on ships,  the review demonstrates enhancement attributes, features and qualities of these new systems. Additionally, the  paper critically evaluates the components of these firefighting systems, as well as looking at their competences,  capabilities, benefits and applicability on board ships.

Laskar Buah is a modern retail chain specializing in the sale of fresh fruit. At present, the company operates one hundred branches across ten regencies. One of its outlets, Laskar Buah Ngumpakdalem, ranks among the top three branches in terms of transaction volume; however, it has received a considerable number of customer complaints regarding the quality of service provided. This situation has prompted management to conduct a thorough evaluation of the store’s service quality.This study was conducted with the aim of analyzing and evaluating service quality using the Importance–Performance Analysis (IPA) method based on the Retail Service Quality Scale (RSQS). The results of the IPA analysis were subsequently used as a foundation for determining priority areas for service improvement.The findings reveal that four service attributes fall within Quadrant B, indicating that they should be prioritized for immediate improvement. These attributes include the cleanliness of the shopping area, store layout, employee product knowledge, and product quality. Additionally, twelve attributes fall under Quadrant C, where performance should be maintained due to their already strong results. On the other hand, eleven attributes fall into Quadrant A, meaning they are considered lower  priority, while one attribute is located in Quadrant D, suggesting that Laskar Buah Ngumpakdalem is providing excessive performance in that particular aspect.

The development of value-added corn-based beverages representation an emerging opportunity within the agribusiness sector, particularly for student-led entrepreneurial initiatives seeking to utilize local agricultural resources. This study examines consumer perceptions of Zeagrain corn milk, a sweet-corn-derived drink positioned as a healthy, practical, and affordable alternative within the ready-to-drink market segment. The research aims to identify key attributes influencing consumer interest, evaluate perceived product performance, and assess the potential competitiveness of Zeagrain as a new agribusiness product. Data were collected using an online quantitative survey targeting respondents aged 17–35 years who represent potential urban consumers. The questionnaire measured demographic characteristics, consumption patterns of similar beverages, perceived importance and performance of product attributes, and purchase intention. The findings are expected to provide insights into consumer expectations regarding taste, packaging, nutritional value, and price. The results further serve as empirical input for improving product development strategies and enhancing the market readiness of Zeagrain. Ultimately, this study contributes to strengthening the commercialization prospects of corn-based beverages and supports innovation efforts among young agripreneurs seeking to optimize local commodities.  

This study aims to determine the optimal pectinase enzyme concentration and hydrolysis duration for the most preferred physical, chemical, and organoleptic characteristics of kweni mango syrup. The research was conducted from October 2024 to August 2025 at the Integrated Laboratory and Basic Science Laboratory of the Agricultural Institute (INTAN) Yogyakarta using a Completely Randomized Design (CRD) with a factorial pattern consisting of two factors and two replications. The first factor was the pectinase enzyme concentration (0%, 0.25%, 0.50%, and 0.75%), and the second factor was the hydrolysis duration (0, 30, and 60 minutes). Observation parameters included yield, vitamin C, viscosity, pH, total soluble solids, and organoleptic attributes. Data were analyzed using ANOVA at a 5% significance level followed by Duncan’s Multiple Range Test (DMRT). The results showed significant effects of both pectinase enzyme concentration and hydrolysis duration on yield, viscosity, total soluble solids, and organoleptic characteristics (color, taste, aroma, and overall acceptability), while no significant effects were observed on vitamin C content and pH. The best formulation was obtained using 0.50% pectinase enzyme concentration with a hydrolysis duration of 30 minutes.

Axiology is a branch of philosophy that examines values ​​in human life, such as goodness, truth, beauty, and the benefits of knowledge. In the context of Islamic education, axiology plays a crucial role in determining the direction and goals of education, namely, to develop individuals who are balanced intellectually, morally, and spiritually, known as insan kamil (the perfect human being). Values ​​in axiology are divided into two primary sources: divine values ​​derived from God's revelation and His attributes, and human values ​​derived from human experience and civilization. These two types of values ​​serve as the foundation for developing a comprehensive Islamic education. The axiology of Islamic education emphasizes not only the attainment of knowledge but also the formation of character and morals in students. Teachers and educational institutions act as agents of values, instilling ethics, aesthetics, and socio-political awareness based on Islamic teachings. By integrating these values, Islamic education aims to produce a generation that is not only intellectually intelligent but also possesses noble morals and is capable of making a positive contribution to society and global civilization. Axiology, therefore, serves as a crucial foundation for realizing a meaningful and humanity-oriented education.

The selection of a thesis topic that aligns with students’ interests and competencies often poses a challenge in academic environments. Inappropriate topic selection can lead to decreased motivation and delays in completing the final project. This study aims to develop a thesis topic recommendation system based on a genetic algorithm that considers students’ interests and academic abilities. The data used include grades from core courses, results of research interest questionnaires, and a list of thesis topics provided by academic supervisors. Each topic is represented as a chromosome, while the fitness function is calculated based on the level of compatibility between student attributes and topics. The selection process employs the roulette wheel method, with single-point crossover and random mutation to generate an optimal solution population. The test results show that the recommendation system based on the genetic algorithm achieves an accuracy rate of 86.7%, higher than the keyword-matching method, which only reaches 71.2%. Therefore, this approach is proven effective in assisting students to determine thesis topics that are suitable, objective, and efficient.