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This study aims to examine customer segmentation through K-Means clustering from a customer data management perspective, emphasizing the interpretive value of analytical results rather than solely their computational outcomes. The research addresses a critical issue in contemporary data-driven organizations, where customer analytics is often reduced to technical modeling without sufficient translation into managerial insights. To respond to this gap, the study adopts a qualitative interpretive approach embedded within a quantitative clustering process, positioning clustering as part of a broader information management cycle. The empirical analysis is based on the Mall Customers Dataset obtained from Kaggle, consisting of 200 customer records with numerical attributes representing age, annual income, and spending score. Quantitative processing using K-Means clustering was employed to identify customer segments, while qualitative interpretation was applied to analyze the managerial meaning of each cluster. Data interpretation was supported by analytical documentation, visualization outputs, and reflective analysis of cluster characteristics. The findings reveal four distinct customer segments with different behavioral and economic profiles, each carrying specific strategic implications for customer relationship management and marketing decision-making. The study demonstrates that the primary value of clustering lies not merely in segment formation, but in its ability to transform raw customer data into actionable managerial knowledge. In conclusion, this research contributes to customer analytics literature by integrating data mining techniques with qualitative interpretation, offering a more human-centered and decision-oriented framework for customer data management. Future research is encouraged to extend this approach using organizational case studies or participatory decision-making contexts.

This study aims to implement an information retrieval system for cryptocurrency data using an attribute-based approach integrated with the Vector Space Model (VSM). The primary objective is to develop a system capable of retrieving the most relevant digital asset information according to specific search attributes, including positive sentiment, price fluctuation, and prediction confidence level. The research adopts a descriptive qualitative method combined with an experimental approach to evaluate the retrieval performance of the cosine similarity algorithm on normalized numerical data. Data preprocessing and attribute weighting were conducted to ensure consistency and improve retrieval accuracy. The experiment demonstrates that the proposed system achieves a Precision@5 value of 1.0, which indicates that all top-five retrieved results are fully relevant to user queries. These findings validate the effectiveness of the attribute-based VSM in analyzing multidimensional cryptocurrency datasets. Overall, this research contributes to the advancement of information retrieval applications in the cryptocurrency domain, particularly for supporting data-driven decision-making and intelligent financial analysis.

The present study was conducted to assess consumer acceptability of taro-based kue talam supplemented with dragon fruit peel and to describe the ingredient composition and nutritional content of the preferred formulation. An experimental design comprising three formulations was employed. The resulting products were subjected to an organoleptic evaluation by untrained panelists, covering the attributes of color, aroma, taste, texture, and overall acceptability. The findings indicated that Formula A received the highest preference scores. The bottom layer of Formula A consisted of 650 g taro, 130 g dragon fruit peel, 250 ml coconut milk, 110 g tapioca flour, 230 g rice flour, 250 g granulated sugar, ½ tsp salt, and 1 tsp vanilla. The top layer comprised 350 ml coconut milk, 25 g granulated sugar, 30 g tapioca flour, 60 g rice flour, and ½ tsp salt. Nutritional analysis per 100 g of product revealed carbohydrate and protein contents of 48.45% and 2.515%, respectively. The study concludes that taro-based kue talam enriched with dragon fruit peel has promising acceptability and may be considered a potential innovation in local tuber-based food products. Further research is recommended to include cost analysis for pricing determination.

This study investigates the influence of Brand Image, Product Quality, and the use of Social Media on consumers’ purchasing decisions to Wardah facial cream. Using a quantitative method with purposive sampling technique, this study involved 120 Wardah product users in the West Semarang area. Data were collected through an online questionnaire and processed using SmartPLS 4.0. The results indicate that product quality and social media usage have a positive and significant effect on purchasing decisions, whereas brand image shows a positive but insignificant effect. This suggests that consumers tend to prioritize tangible attributes such as product quality and active engagement through social media over brand perception The model explains 75.2% of the variance in purchasing decisions (Adjusted R² = 0.752), highlighting the strong explanatory power of the variables examined. Practically, the findings suggest that marketers should prioritize product quality and optimize social media campaigns to boost customer engagement and loyalty, while reassessing brand perception strategies.

The rapid growth of open-source software (OSS) in machine learning (ML) has intensified the need for reliable, automated methods to assess project quality, particularly as OSS increasingly underpins critical applications in science, industry, and public infrastructure. This study evaluates the effectiveness of a diverse set of machine learning and deep learning (ML/DL) algorithms for classifying GitHub OSS ML projects as engineered or non-engineered using a SMOTE-enhanced and explainable modeling pipeline. The dataset used in this research includes both numerical and categorical attributes representing documentation, testing, architecture, community engagement, popularity, and repository activity. After handling missing values, standardizing numerical features, encoding categorical variables, and addressing the inherent class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), seven different classifiers—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Logistic Regression (LR), Support Vector Machine (SVM), and a Deep Neural Network (DNN)—were trained and evaluated. Results show that LR (84%) and DNN (85%) outperform all other models, indicating that both linear and moderately deep non-linear architectures can effectively capture key quality indicators in OSS ML projects. Additional explainability analysis using SHAP reveals consistent feature importance across models, with documentation quality, unit testing practices, architectural clarity, and repository dynamics emerging as the strongest predictors. These findings demonstrate that automated, explainable ML/DL-based quality assessment is both feasible and effective, offering a practical pathway for improving OSS sustainability, guiding contributor decisions, and enhancing trust in ML-based systems that depend on open-source components.

Selecting a faculty that aligns with students’ interests and talents is a strategic step in determining the success of higher education and future career paths. However, most vocational high school (SMK) students still face difficulties in identifying the most suitable faculty due to the lack of data-driven analysis. This study implements the C4.5 classification algorithm within data mining techniques to build an automatic and measurable faculty recommendation system. The dataset consists of attributes such as SMK major, interest level, aptitude test results, academic grade average, and gender, with the output being the recommended faculty. The C4.5 algorithm was chosen for its ability to generate a transparent and interpretable decision tree, which helps both guidance counselors and students understand the rationale behind the recommendations. The experimental results show that the constructed classification model achieved an accuracy rate of 88%, based on cross-validation testing using data from 12th-grade students. The implementation of this system is expected to serve as an objective tool in the faculty selection process and to promote a data-driven decision-making approach in secondary education environments.

This study aims to investigate the impact of packaging type and storage temperature on the physical, chemical, and sensory properties of chili blocks during storage. Additionally, the study aims to identify the optimal packaging and storage temperature combination, as well as to characterize the quality attributes of chili blocks. The experimental design employed was a Completely Randomized Design (CRD) in a factorial arrangement with two factors: packaging type and storage temperature. In this study, using aluminum foil as primary packaging, PP plastic packaging, and paper as secondary packaging, and 3 treatment storage temperatures, each repeated as many as 3 trials, so that it has 18 experimental units. Data analysis using ANOVA with Duncan's Multiple Range Test (DMRT)at a significant level of 5% using the SPSS18 program. The results showed that secondary packaging types and storage temperatures did not significantly affect physical properties (moisture content) and organoleptic properties (color, aroma, texture), but significantly affected the chemical properties (pH, vitamin C, ash content) and secondary packaging types the good for chili block is alufo + plastic packaging and refrigerator storage temperature (10oC), and good chili block characteristics are: Refrigerator storage (temperature 10oC) with alufo + plastic packaging: lowest physical properties (moisture content) 27.29, chemical properties (lowest pH 5.23, Vitamin C 12.91 and ash content 6.35).

The rapid growth of online transportation services has significantly transformed urban mobility patterns, including in Abepura District, Jayapura City. This study is grounded in the concept of smart mobility, which emphasizes technological integration, efficiency, and accessibility within the smart city framework. The theoretical foundation draws on consumer preference theory and the Customer Satisfaction Index (CSI) model. A quantitative approach was applied through questionnaires distributed to 100 respondents, supported by secondary data on digital infrastructure and local transport regulations. The analytical methods included conjoint analysis to identify user preferences, CSI analysis to assess smart mobility readiness, and spatial analysis to map infrastructure support. The findings indicate that fare and safety are the most influential attributes shaping user preferences, followed by application usability, transport mode, and travel time. Maxim emerged as the most widely used application, followed by Grab and Gojek. The CSI score reached 77.60%, categorized as “highly ready,” though gaps remain in intermodal integration and waiting time efficiency. Spatial analysis confirmed that the coverage of 16 BTS towers in Abepura adequately supports online transportation operations. In conclusion, online transportation services in Abepura District demonstrate strong readiness to support the implementation of smart mobility, yet further improvements are needed in modal integration and operational efficiency to ensure sustainable and inclusive urban mobility.

Earnings management is an action that can affect the quality of a company's financial information. As the highest leader, the CEO plays a critical role in strategic decision-making, including in earnings management practices. This study aims to examine the influence of CEO characteristics—namely age, education level, and tenure—on earnings management in food and beverage sub-sector companies listed on the Indonesia Stock Exchange for the 2019–2023 period. A quantitative approach is employed using secondary data from annual reports of 21 companies, with a total of 99 firm-year observations. The data were analyzed using multiple linear regression with leverage, profitability, and sales growth as control variables. The results show that CEO age has a negative effect on earnings management, CEO tenure has a positive effect, while CEO education level shows no significant effect. These findings indicate that the personal characteristics of CEOs influence a company’s tendency to engage in earnings management. This study provides insights for investors, management, and regulators to consider CEO attributes when assessing the risk of financial reporting manipulation.

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.

The rapid development of information technology has created significant opportunities for secondary schools to improve the quality of learning through the use of digital-based applications. This study aims to design and develop a web-based tutoring application for students of Junior High School (SMP) Negeri 1 Tana Righu by applying Object Oriented Technology (OOT) as the software development approach. OOT was chosen for its ability to simplify the system design process through structured modeling using the concepts of classes, objects, attributes, and relationships between objects. The developed application provides various features, including the presentation of learning materials, interactive practice questions, learning outcome evaluations, and student progress reports accessible in real time by both teachers and students. The research method consisted of needs analysis, system design using UML diagrams, implementation, and application testing. The testing results indicate that the tutoring application is effective in improving the learning process, assisting teachers in monitoring and evaluating student progress, and providing students with a more engaging and interactive learning experience. Therefore, this application is expected to serve as an innovative solution to support tutoring activities in secondary schools and contribute positively to enhancing the quality of education in the digital era.

Indonesia, as an archipelagic nation, faces considerable challenges in meeting its food requirements in accordance with Law No. 18 of 2012 on Food Security. The country’s heavy reliance on wheat imports, ranking as the largest wheat importer globally, highlights the urgency of diversifying local food sources. This study aims to explore the utilization of jali flour (Coix lacryma-jobi L.), mocaf flour (Modified Cassava Flour), and soybean flour as raw materials for producing fettucini pasta, with the addition of green spinach extract for natural coloring and nutritional enhancement. A quantitative experimental method was employed, testing three formulations of jali and mocaf flour combined with soybean flour. Sensory evaluations were conducted with 35 panelists—comprising trained, semi-trained, and untrained participants—to assess color, aroma, taste, texture, and overall acceptability. The findings revealed that flour composition significantly influenced pasta’s sensory attributes. The F2 formulation (50% jali flour, 50% mocaf flour, 20% soybean flour) achieved the highest acceptance, with average scores across all parameters rated as “liked” to “highly liked.” The incorporation of spinach extract not only imparted an appealing natural green hue but also enhanced the product’s nutritional profile, particularly in iron and dietary fiber content. These results underscore the potential of combining jali and mocaf flours as a nutritious, gluten-free pasta alternative that supports local food diversification and reduces reliance on imported wheat.

Earnings management is a deliberate strategy based on multidimensional considerations. The impact of earnings management practices is not necessarily positive, so mitigation measures are necessary to mitigate any negative impacts. This article aims to observe and analyze research developments regarding the role of the Board of Directors (BOD) in reducing discretionary accruals in Islamic banking. This article uses a qualitative literature study approach by analyzing articles from previous studies based on criteria that include article quality and research focus. Analysis of the answers to the research questions is conducted based on the results of empirical research in previous studies. Based on the analysis, a review of previous articles provides a strong argument that the role of the Board of Directors (BOD) plays a significant and complex role in influencing earnings management practices in the Islamic banking sector. The effectiveness of the BOD is not universal, but is highly dependent on its specific attributes, including competence, size, and activeness. It has been proven that competence is the attribute that plays the most role in reducing discretionary accruals. Meanwhile, characteristics such as board size and activeness show mixed results, indicating that mere physical and administrative presence is not enough without being balanced with substantive expertise, independence, and effective board work dynamics. This complexity is exacerbated by inconsistent findings and limited scope of studies on accrual techniques. Therefore, strengthening governance in Islamic banking must focus on improving the quality of human resources on the board of directors, especially in terms of technical expertise in finance and accounting. There is an academic gap for future research by exploring moderating variables, real manipulation techniques, and the dynamics of interactions between governance elements in the dual structure that characterizes Islamic banking

The footwear industry has experienced significant growth, particularly in the fashion segment, where shoes are no longer considered solely as functional items but also as lifestyle products that reflect personal identity and social expression. Among various global brands, Dr. Martens has become one of the premium footwear products with a distinctive image and loyal consumer base. This study aims to examine the influence of Product Quality, Brand Image, and Brand Awareness on Purchasing Decisions of Dr. Martens shoes in Jakarta. The research employed a quantitative method with non-probability sampling, using the accidental sampling technique. The population consisted of consumers who had purchased Dr. Martens shoes, and a total of 100 respondents were collected through questionnaires distributed among university students. The instrument used was a Likert scale questionnaire, and the data were analyzed using multiple linear regression with SPSS version 22. The findings show that Product Quality and Brand Awareness have a positive and significant influence on Purchasing Decisions, while Brand Image does not show a significant effect. These results indicate that consumers are more influenced by tangible product attributes and the level of brand recognition than by brand perception alone. This highlights the importance for premium footwear companies to maintain consistent product quality and strengthen brand awareness strategies, especially through digital marketing channels, to influence purchase behavior. The study contributes to the literature on consumer behavior and provides practical insights for fashion industry players to formulate effective marketing strategies in a competitive market.

The quality of service towards customer satisfaction in the automotive industry of Toyota car transportation in Indonesia is growing rapidly from year to year, especially in big cities in Indonesia this car is very much needed. The market conditions that are becoming wider and market opportunities are everywhere, this results in increasingly open and tight competition competing to be able to meet the needs and demands required by customers, customer satisfaction is a level where the estimated product performance is in accordance with the buyer's expectations and one of the ways of PT Astra International Tbk - TSO Auto2000 Salemba Branch to maintain the quality of service to customers. Customer Satisfaction Index (CSI) is a method for measuring customer satisfaction based on attributes to be measured such as compliance, responsiveness, assurance, and empathy. The purpose of the study is to improve the performance of service, facilities and customer handling of PT Astra International Tbk - TSO Auto2000 Salemba Branch. The overall level of customer satisfaction is in the good category with a CSI value of around 97%, indicating that customers are satisfied with the services provided by PT Astra International Tbk - TSO Auto2000 Salemba Branch. The factors that most influence customer satisfaction are the quality of staff service, including intimidation and speed of service.

The aim of this study is to know and understand holistically the things that Jesus taught about perfect character in Matthew 5:48, so that with this understanding, all workers or servants of God and the congregation of GSKI Medan City can learn and live what Jesus taught related to perfect character. The research method used in this study is a Qualitative method with a phenomenological approach. The data sources used in this study are semi-structured interview guides and field notes. The data analysis technique used in this study involves several steps, namely bracketing, horizontalization, phenomenological reduction, and synthesis of essential meaning. The results of the study show that: The congregation and servants of God GSKI in Medan City understand the teachings of Matthew 5:48 as a call to perfection exemplified by the Father in heaven; The congregation and servants of God GSKI in Medan City identified several key attributes in achieving perfect character which include high moral quality, holiness, commitment to following God's will, and growth in faith through deepening the Word and fellowship; The congregation GSKI in Medan City faces several major challenges in building perfect character according to the teachings of Matthew 5:48, such as limitations in understanding the teachings of the Bible and incomplete doctrines, worldly influences and attachment to worldly pleasures, and the lack of consistent role models from church leaders; An effective teaching model for building perfect character based on Matthew 5:48 in the context of the congregation GSKI in Medan City consists of: a structured teaching approach, practical and contextual application, as well as role models and leadership

The aim of this study is to develop and produce findings on an effective teaching model to build the perfect character of the congregation of the Suara Sejati Gospel Church in Medan. The research method used in this study is a qualitative method with a phenomenological approach. The data sources used in this study are a semi-structured interview guide and field notes. The data analysis technique used in this study involves several steps, namely bracketing, horizontalization, phenomenological reduction, and synthesis of essential meaning. The results of the study indicate that: The congregation and servants of God GSKI in Medan City identified several key attributes in achieving perfect character which include high moral quality, holiness, commitment to following God's will, and growth in faith through deepening the Word and fellowship; The congregation of GSKI in Medan City faces several major challenges in building perfect character according to the teachings of Matthew 5:48, such as limitations in understanding the teachings of the Bible and incomplete doctrines, worldly influences and attachment to worldly pleasures, and the lack of consistent role models from church leaders; An effective teaching model for building perfect character based on Matthew 5:48 in the context of the GSKI congregation in Medan City consists of a structured teaching approach, practical and contextual application, and role models and leadership; An effective teaching model for building perfect character based on Matthew 5:48 is a model that is transformative, andragogical, and empowering. This study produces the TAP Model (Transformation - Andragogy - Empowerment) which includes the stages of introduction, reflection, practice, as well as evaluation and mentoring. This model encourages active congregational involvement, connects the Word with real life, and forms character through a contextual and spiritual learning process.

The purpose of this study is to examine the contribution of leg muscle explosive power and foot-eye coordination to the shooting ability of PUPUS FC football players in the Mentawai Islands Regency. The type of research used is quantitative descriptive. The population in this study were all PUPUS FC players, while the sampling technique used total sampling of 22 people. The tests used in this study were the standing broad jump test, the foot-eye coordination test, and the soccer shooting test. The sample in this study was 22 people. From the results of the tests conducted on PUPUS FC soccer players, the results obtained were that there was a contribution between leg muscle explosive power to soccer shooting results of 12.40% and the remaining 87.60% was influenced by other factors, there was a contribution between foot-eye coordination to soccer shooting results of 2.70% and the remaining 97.30% was influenced by other factors, and there was a contribution of leg muscle explosive power and foot-eye coordination to soccer shooting results of 73.10% and the remaining 26.90% was influenced by other factors. The tests used in this study included the standing broad jump test to assess leg muscle explosive power, the foot-eye coordination test to evaluate the players' coordination, and the soccer shooting test to measure their shooting accuracy and effectiveness. These tests are commonly used in sports science to evaluate physical attributes that directly impact athletic performance. The results obtained from the tests indicate that leg muscle explosive power contributed 12.40% to the soccer shooting performance of the players, with the remaining 87.60% influenced by other factors such as technical skill, tactical awareness, and mental readiness. Additionally, foot-eye coordination accounted for 2.70% of the variation in shooting ability, suggesting that while important, it is not the dominant factor in determining shooting success. The remaining 97.30% of the variation could be attributed to other physical, technical, or psychological factors.

This study aims to investigate the sensory characteristics and nutritional content of macarons with the addition of kecombrang flower essential oil (Etlingera elatior). The research was conducted to determine the effects of various concentrations of kecombrang flower essential oil (3%, 5%, and 7%) on macaron quality. The sensory attributes evaluated included color, aroma, taste, texture, and overall preference, while nutritional analysis focused on key macronutrients such as carbohydrates, sugar, fat, protein, and ash content. The sensory evaluation utilized a single-factor ANOVA and Duncan's multiple range test to compare the effects of the different concentrations of essential oil. The results indicated that the addition of 5% kecombrang essential oil yielded the most favorable sensory scores, with the highest ratings in color (4.20) and texture (4.29). Furthermore, aroma and taste were rated highly, confirming that this concentration enhanced the overall quality of the macarons. Although the likeability score did not show significant statistical differences between treatments, all treatments were generally categorized as “liked” by the panel. For the nutritional content analysis, the macaron sample with 5% essential oil was selected for testing through a proxy method. The results indicated that the macaron had a carbohydrate content of 46.31%, sugar content of 31.62%, fat content of 3.05%, protein content of 19.08%, and ash content of 0.14%. These findings are noteworthy, as the macaron with kecombrang flower essential oil showed lower carbohydrate and fat content compared to a typical macaron, while it exhibited higher protein content. These results suggest that macarons with kecombrang essential oil may offer a healthier alternative to conventional macarons, particularly for those seeking lower sugar and fat content in their diet.

The public’s demand for excellent service encourages the government to continuously innovate. In response, the Population and Civil Registration Office of Pangandaran Regency developed an innovation called the mobile service pickup program. This innovation integrates offline services to accelerate and simplify the processing of population documents, particularly in the issuance of birth certificates. The mobile service pickup program is designed to improve the efficiency, effectiveness, and transparency of public services in the field of population administration in Pangandaran Regency. This study refers to the innovation theory by Everett M. Rogers, as cited in Prabowo H. (2022), which states that there are five attributes of innovation that influence its acceptance: relative advantage, compatibility, trialability, and observability. The research method used in this study is qualitative descriptive, with data collected through interviews, documentation, and observation. Meanwhile, informants were selected purposively to obtain relevant and in-depth data. The research results reveal that the mobile service pickup program innovation has not yet been optimally implemented, with time effectiveness being a crucial factor for the community. Therefore, this innovation must continue to be developed to gain wider acceptance among the public.