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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.

The issue of determining the number of students' graduation times is one of the important indicators in transmitting the quality and effectiveness of the higher education process in universities. The rate of on-time graduation not only impacts accredited institutions, but also becomes a concern for campus management in designing learning strategies and academic guidance. This study aims to apply and compare two classification algorithms in data mining, namely C4.5 and K-Nearest Neighbor KNN, in predicting the accuracy of students' graduation times. Predictions are made based on academic attributes such as Grade Point Average GPA, number of credits that have been achieved, and Semester Grade Point Average IPS as input variables. The method used in this study is Knowledge Discovery in Database KDD which includes data selection, preprocessing, transformation, data mining, and evaluation of results. The study was conducted using the RapidMiner tool, with a dataset of 279 Informatics Study Program students from the 2015 to 2019 intake. The data was classified into two categories: "graduated on time" and "not graduated on time". The test results showed that the KNN algorithm provided better performance compared to C4.5. KNN produced an accuracy of 76.08%, with a precision of 73.11% and a recall of 41.92%. Meanwhile, the C4.5 algorithm produced an accuracy of 73.49%, with a precision of 64.62% and a recall of 41.89%. This difference in accuracy indicates that KNN is more effective in capturing patterns in the data and providing more accurate predictions in this context. Thus, the KNN algorithm can be considered a more optimal method to assist universities in predicting potential student admissions in a timely manner, thus enabling early intervention for students at risk of late graduation. This research also contributes to the development of data mining-based academic decision support systems in higher education.

The growing demand for affordable transportation has significantly expanded the used car market. This study aims to develop optimal sales strategies by analyzing seller types and vehicle categories through exploratory data analysis and linear regression techniques. Using a cleaned public dataset of over 150,000 used car listings in Germany, key variables such as registration year, mileage, vehicle type, and seller type were examined. Results indicate that individual sellers dominate the market, although dealers set higher and more stable prices. SUVs and limousines typically hold higher market value. The linear regression model achieved an R-squared value of 0.34, suggesting that registration year and mileage account for 34% of the price variance. These findings offer practical insights for stakeholders in the used car business to tailor pricing strategies based on vehicle attributes and seller classification

Chronic Kidney Disease (CKD) is a major global health issue that can lead to serious complications and long-term medical care. This study aims to identify key clinical factors associated with CKD status using binary logistic regression analysis. The dataset, obtained from Kaggle, contains 400 patient records with various clinical and demographic attributes. The dependent variable is CKD status (positive or negative), while the independent variables include age, blood pressure, hemoglobin level, urine albumin level, and serum creatinine. Initial analysis involved descriptive statistics and multicollinearity checks, followed by model estimation and evaluation using likelihood ratio and Wald tests. The final model identified four significant predictors: blood pressure, hemoglobin, urine albumin, and serum creatinine. The model achieved a high classification accuracy of 95.50% and an Area Under the ROC Curve (AUC) of 98.78%, indicating excellent predictive performance. These results highlight the importance of these clinical indicators in early CKD detection and support their use in risk assessment models for kidney disease screening Keywords: Chronic Kidney Disease, Binary Logistic Regression, Likelihood Ratio Test, Wald Test, Classification Accuracy

Lungs are vital organs in the human respiratory system, responsible for fulfilling the body's oxygen needs. If the lungs experience health problems, it can have adverse effects on the human respiratory system. Common causes of lung diseases are usually due to inhaling air contaminated by dust, smoke, viruses, and bacteria. This study aims to compare the performance of two classification algorithms, namely Random Forest and Naive Bayes, in predicting lung diseases. The data used was obtained from the Kaggle website and processed using RapidMiner software. The attributes involved include smoking habits, pre-existing conditions, staying up late, exercise activities, age, and outcomes. Based on the test results, the Random Forest algorithm demonstrated the best performance with an accuracy of 93%, while the Naive Bayes algorithm achieved an accuracy of 87%. These findings indicate that the Random Forest algorithm outperforms the Naive Bayes algorithm in terms of lung disease prediction accuracy.

This study aims to evaluate community satisfaction with the CSR Program of PT Pertamina Patra Niaga Fuel Terminal Samarinda focused on talent and interest development for children of the Butonese community in Sidodadi Urban Village. A quantitative approach was applied using the Community Satisfaction Index (IKM) survey instrument based on Ministerial Regulation PANRB No. 14 of 2017, supported by Importance Performance Analysis (IPA). Results indicate an IKM score of 84.18, which falls under the “very good” category. However, several priority attributes, such as scheduling accuracy and supporting infrastructure, require improvement. Recommendations include service quality enhancement, community engagement, and sustainability-based planning.

The growth of the snack food industry in Indonesia encourages MSME actors to innovate in improving product competitiveness, including through packaging development. UMKM Nambawang, as a producer of fried shallots, faces challenges in packaging design that lacks visual appeal and does not reflect the product’s identity, despite its superior taste quality. This study aims to design more functional, economical, and competitive fried shallot packaging by integrating the Quality Function Deployment (QFD) and Value Engineering (VE) methods. QFD is applied to translate consumer needs (voice of customer) into technical packaging attributes, while VE is used to evaluate the performance and cost-efficiency of design alternatives. The analysis results indicate that key attributes prioritized in packaging development include color aesthetics, product protection, ease of opening and resealing, clarity of product information, and storage convenience. These main attributes are translated into technical elements such as the use of standing pouches, zipper locks, full-print designs, added onion illustrations, and complete product information. The application of QFD and VE methods results in a mockup visually appealing, functional, and durable packaging design, enhancing the product’s added value based on consumer needs and increasing its competitiveness in the domestic market.

The bodywork industry in Indonesia is under high competitive pressure, requiring companies to be more adaptive in understanding customer behavior in order to maintain business continuity. PT. Bengawan Karya Sakti as one of the national bodywork companies, has not optimally utilized historical transaction data to assess customer loyalty. This study aims to identify customer loyalty segmentation through the application of the RFM (Recency, Frequency, Monetary) method, which is used to analyze sales transaction data in 2022 and 2023. The study uses the CRISP-DM approach which includes the stages of business understanding, data exploration, data cleaning and processing, modeling, evaluation, and implementation of results. The transaction data analyzed includes attributes of transaction date, customer, number of transactions, and transaction value, which are then processed into RFM scores based on the transaction year and classified into categories such as Very Loyal, Loyal, At Risk, and others. The segmentation results show an increase in the number of very loyal customers from 2022 to 2023, as well as a significant decrease in inactive and at-risk customers. The chi-square statistical test shows that the difference in customer distribution between years is statistically significant (p-value <0.05), indicating a real influence from the company's strategy or external factors. The main conclusion of this study is that the RFM method is effective in the bodywork industry to support data-based marketing decision making and more targeted customer retention strategies.

Crime is a social issue that continues to evolve alongside increasing community activity and regional development. This study aims to Cluster crime data in Binjai City based on the location of incidents using the K-Means algorithm and the Cross Industry Standard Process for Data Mining (CRISP-DM) approach. The data were obtained from the Binjai Police Department, with attributes including the type of crime, time of occurrence, and location, categorized by district. A comprehensive data preprocessing stage was carried out, involving the extraction of information from raw data, normalization of crime type labels, and conversion of categorical data into numerical form using label encoding. The optimal number of Clusters was determined using the Silhouette score method, which yielded the best result at K = 10. The Clustering results were further evaluated using the Davies-Bouldin Index (DBI) to ensure Cluster quality. The analysis revealed that Binjai Utara District has the highest number of crimes, particularly aggravated theft (curat), which frequently occurs from early morning to late morning. This Clustering is expected to provide valuable insights for authorities in formulating more targeted and data-driven regional security strategies.

This study examines the role of perception in individual and organizational decision-making through a Systematic Literature Review (SLR) and content analysis approach. Perception is understood as an internal process that shapes understanding of objects, influencing preferences and decisions. Contextual factors such as culture, socio-economics, and technology also contribute to shaping perception. This study adopts a theoretical framework in which perception mediates the relationship between external attributes—such as price, quality, and brand image—and final decisions. By analyzing 993 scholarly articles from 2021 to 2025, this research maps perception models in consumer and organizational domains and identifies research gaps related to the integration of digital factors such as algorithms and AI. The validation process was conducted through triangulation between AI-generated prompts and human-based thematic analysis. The results highlight the need for perception models to adapt to digital dynamics and emphasize the importance of ethical integration in modern decision-making systems.