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In Surabaya City, the City Government through the Transportation Agency developed the Suroboyo Bus and Trans Semanggi programs to address traffic congestion, limited public transportation, and the need for safe, comfortable, and environmentally friendly transportation. However, complaints are still found regarding limited facilities, irregular schedules, and suboptimal communication services, so that service performance evaluation is needed from the user's perspective. This study aims to analyze the performance of Suroboyo Bus and Trans Semanggi public transportation services at the Surabaya City Transportation Agency using five public service performance indicators according to Dwiyanto et al. (2021), namely productivity, service quality, responsiveness, responsibility, and accountability. The approach used is quantitative with the Importance Performance Analysis (IPA) method. The results of the study show an average expectation score (importance) of 4.18 and a reality score (performance) of 3.86 with an overall gap of -0.32, which means that the performance of Suroboyo Bus and Trans Semanggi services still does not meet public expectations. Through the IPA mapping, three attributes are in Quadrant I (high priority): the friendly and professional attitude of staff, the adequacy of on-board facilities, and the transparency of official information regarding schedules and service changes. A total of 13 attributes are in Quadrant II (maintained), 13 attributes in Quadrant III (low priority), and one attribute in Quadrant IV (excessive).

This study explores the profound impact of teacher personality competence on student character formation through a descriptive narrative lens grounded in the sociology of education. Education is not merely a technical transfer of knowledge but a complex social process where the teacher acts as a pivotal moral agent and role model. By synthesizing classical sociological paradigms—Functionalism, Conflict Theory, and Symbolic Interactionism—with contemporary empirical evidence, this article argues that a teacher's personality is a dynamic social construct that shapes the "hidden curriculum" and the overall school climate. The narrative analysis reveals that traits such as empathy, integrity, and social justice are not just individual attributes but essential tools for moral socialization and the internalization of collective values. The study finds that teachers who embody these virtues foster a sense of social belonging and ethical responsibility in students, effectively bridging the gap between individual identity and societal expectations. The article concludes that strengthening teacher personality competence is a strategic imperative for developing a resilient and morally grounded generation, recommending integrated professional development that emphasizes the teacher's role as a moral authority in the 21st-century social landscape.

The retail industry generates an extremely large and continuously growing volume of transactional data along with the advancement of digital technology, thereby requiring sophisticated and systematic data analysis approaches to support effective and evidence-based business decision-making. This study aims to analyze retail sales data by utilizing the Retail Sales Dataset obtained from the Kaggle platform, which consists of 100,000 transaction records and broadly represents the characteristics of retail transactions. The main focus of this study is to classify product categories and predict customer segments, including the identification of high-spending customers (high spenders), based on demographic attributes such as age and gender, as well as various transaction-related features. The research methodology includes data preprocessing, label encoding, and feature engineering to generate additional variables, including Age_Group, Is_Holiday, and Spender_Group, which are expected to enhance the predictive capability of the models. Several machine learning algorithms, namely Decision Tree, Random Forest, and XGBoost, were implemented and evaluated to compare their respective performance. The experimental results indicate that multiclass product category classification achieves relatively low accuracy, ranging from 27% to 34%. These findings suggest the high complexity of retail data and highlight the need for further model optimization, class balancing techniques, and feature refinement to improve predictive performance in future studies.

This study aims to: (1) examine the effect of an entrepreneurship program using a Design thinking approach on the entrepreneurial spirit of Grade V students at UPT SPF SD Inpres Baraya 1, Makassar City, and (2) examine the effect of an entrepreneurship program using a Design thinking approach on student independence of Grade V students at UPT SPF SD Inpres Baraya 1, Makassar City. This research employed a quantitative approach with a quasi-experimental method using a Non-Equivalent Control Group Design. The research subjects consisted of two groups: an experimental group that received an entrepreneurship program based on Design thinking and a control group that did not receive the treatment. Data were collected using questionnaires measuring students’ entrepreneurial spirit and independence, supported by documentation. Data analysis was conducted using descriptive statistics, classical assumption tests, and hypothesis testing through an independent samples t-test. The results indicate that the entrepreneurship program using a design thinking approach had a significant positive effect on students’ entrepreneurial spirit and independence. The independent samples t-test revealed that the program significantly improved students’ entrepreneurial spirit (p = 0.002 < 0.05) compared to the control group. Likewise, a very significant effect was found on student independence, with a significance value of p = 0.001 < 0.05. These findings confirm that the application of a design thinking–based entrepreneurship program effectively enhances key entrepreneurial attributes, including creativity, initiative, responsibility, and independence among fifth-grade elementary school students.

This study aims to improve the quality of gallon water products and strengthen the competitiveness of CV. Anugerah Gemilang through the implementation of Quality Function Deployment (QFD) using the House of Quality (HoQ). The QFD approach is employed to translate the Voice of Customer (VoC) into technical product characteristics as well as service quality attributes that influence customer satisfaction. The research method applied is quantitative-descriptive with the support of qualitative data. Quantitative data were collected through questionnaires distributed to customers to measure the importance and satisfaction levels regarding product and service attributes, while qualitative data were obtained through in-depth interviews to reinforce and validate the questionnaire results. Subsequently, the VoC data were analyzed using the HoQ matrix to determine quality improvement priorities based on customer importance weights. This study is expected to produce strategic recommendations in the form of prioritized product and service quality enhancements focused on customer satisfaction, thereby supporting the sustainable improvement of the company’s competitiveness.

This study aims to improve the quality of gallon water products and strengthen the competitiveness of CV. Anugerah Gemilang through the implementation of Quality Function Deployment (QFD) using the House of Quality (HoQ). The QFD approach is employed to translate the Voice of Customer (VoC) into technical product characteristics as well as service quality attributes that influence customer satisfaction. The research method applied is quantitative-descriptive with the support of qualitative data. Quantitative data were collected through questionnaires distributed to customers to measure the importance and satisfaction levels regarding product and service attributes, while qualitative data were obtained through in-depth interviews to reinforce and validate the questionnaire results. Subsequently, the VoC data were analyzed using the HoQ matrix to determine quality improvement priorities based on customer importance weights. This study is expected to produce strategic recommendations in the form of prioritized product and service quality enhancements focused on customer satisfaction, thereby supporting the sustainable improvement of the company’s competitiveness.

Cloud native architectures are essential for modern software systems due to their ability to handle dynamic environments, scalability, and high availability. However, ensuring resilience in these systems remains a significant challenge, particularly under varying operational conditions such as high-load periods and failure scenarios. This study aims to assess the resilience of cloud native architectures using quantitative metrics that objectively evaluate key attributes such as availability, fault tolerance, recovery time, and scalability. Through the application of these metrics, the study identifies the strengths and weaknesses of the architecture, providing insights into how the system performs under stress and recovers from failures. The results show that while the architecture demonstrates strong availability and scalability under typical conditions, recovery time and scalability under extreme load conditions reveal areas for improvement. Specifically, issues with resource allocation and self-healing capabilities were identified as key weaknesses affecting the overall resilience of the system. These findings highlight the importance of using data-driven metrics to gain detailed insights into system resilience and to guide architectural improvements. The study also emphasizes the need for continuous monitoring and adaptation of the architecture to optimize fault tolerance and recovery processes. The implications of this research extend to cloud application developers and architects, offering actionable recommendations for improving system resilience. Future research could focus on integrating real-time monitoring systems, developing more advanced resilience metrics, and incorporating AI-driven scaling techniques to further enhance the adaptability and robustness of cloud native systems. By addressing these challenges, cloud native architectures can be better equipped to maintain high performance and reliability in dynamic, real-world environments.

Modern retail expansion in Situbondo Regency has intensified competitive interaction with traditional markets, making it important to map differences in market structure, firm conduct, and performance outcomes. This study compares (i) market structure using concentration indicators (CR4 and the Herfindahl–Hirschman Index/HHI), (ii) competitive conduct (pricing practices, promotional intensity, service attributes, and relationship patterns), and (iii) performance proxies (sales turnover and selected price efficiency measures) within the SCP framework. The analysis applies a descriptive quantitative approach supported by targeted primary observations and questionnaire-based information, and complemented by official statistics and regulatory documents. Traditional-market samples include Panji, Besuki, and Panarukan markets, while modern-retail samples include local outlets of Indomaret, Alfamart, and Basmalah. Results indicate that traditional markets are relatively unconcentrated (CR4 = 38.0%; HHI = 744), consistent with a competitive structure dominated by many small vendors. Modern retail is more concentrated (CR4 = 77.0%; HHI = 1,644), suggesting moderate concentration and a tendency toward local oligopoly. Average monthly turnover per unit is higher for modern retail (IDR 36.36 million) than for traditional vendors (IDR 15.63 million). Price efficiency varies across commodities: some items show near parity, while several fresh commodities remain cheaper in traditional markets. Policy implications point to balanced local governance: zoning and permitting for modern stores, continuous revitalization of traditional markets, and strengthened MSME partnership schemes to ensure healthy and inclusive competition.

Understanding God in Christian education plays a crucial role in the moral formation of children, as the theological concept of God forms the foundation for values, character, and ethical actions, developed from an early age. In Christian education, the process of moral formation does not simply rely on rules or customs, but rather rests on the child's personal relationship with God as a loving, guiding Being, and the source of truth. This study uses qualitative methods with a library research approach to explore how the concept of God according to Christian theology can serve as a foundation for the comprehensive moral formation of children. The aim of this study is to analyze how God's attributes such as love, holiness, justice, and faithfulness can be applied in the educational process so that the moral values formed are not formalistic but grow from a deep spiritual awareness. The research findings show that a proper understanding of God helps children recognize moral standards rooted in God's character, develop ethical attitudes, and strengthen the internal drive to live according to His will. This research is also expected to contribute to Christian educators in developing theological, contextual, and effective learning strategies to continuously cultivate children's morality in accordance with the principles of the Christian faith.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms

This study compares the effectiveness of MAUT (Multi-Attribute Utility Theory), SMART (Simple Multi-Attribute Rating Technique), and WASPAS (Weighted Aggregated Sum Product Assessment) methods in a decision support system for determining the best employees at Sisilia Boutique. The quality of human resources is crucial in the retail business, but performance evaluation is often influenced by subjectivity. To address this, a multi-criteria-based decision support system is needed. MAUT translates preferences into a numerical scale, SMART calculates the average value of attributes based on weights, while WASPAS combines weighted summation (WSM) and weighted multiplication (WPM) for more balanced results. Employee performance data from Sisilia Boutique in June 2025, including attendance, store layout, customer service, and discipline, were used as the research object. The comparison results show consistency in the highest (K3) and lowest (K7) ratings across the three methods, with differences in the middle ratings. WASPAS offers a more balanced distribution of final scores, making it a comprehensive alternative for performance evaluation.

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 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 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.

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.

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.

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 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.  

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.

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.