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

The rapid development of AI-assisted programming has encouraged the emergence of vibe coding, an approach to software development in which developers focus more on formulating intentions, contexts, and constraints through prompts rather than writing code manually. However, existing evaluations of AI-generated code still tend to emphasize functional correctness and productivity, and therefore have not fully addressed the relationship between user intent, technical code reliability, and developers’ understanding of the generated artifacts. This study aims to propose a new evaluation method called TD-VCEM (Three-Dimensional Vibe Coding Evaluation Method) to assess vibe coding practices in a more comprehensive and auditable manner. The proposed method consists of three primary dimensions: Intent Alignment to evaluate the conformity of code with prompt requirements, Code Reliability to assess the technical quality of the generated code, and Developer Cognition to measure developers’ understanding of AI-generated code. TD-VCEM is designed through several stages, including prompt decomposition, prompt-to-code traceability matrix, code reliability assessment, and developer cognition evaluation. Each dimension employs indicator-based scoring rubrics normalized on a scale of 0–100, enabling the construction of a Vibe Coding Evaluation Score (VCES). This study does not present empirical experimental results; instead, it offers a methodological framework that can serve as a foundation for evaluating AI-generated code in modern software engineering environments. The proposed TD-VCEM is expected to improve review process transparency, reduce security risks, strengthen software maintainability, and ensure that developers maintain control and understanding of AI-generated code artifacts.

Background: Class II restoration according to the classification of Greene Vardiman Black is one of the procedures in dental conservation that often presents a challenge for operators or dentists during treatment. Class II restorations have their own level of difficulty, particularly during the placement of restorative materials into the cavity. One of the possible failures in Class II restorative treatment is marginal leakage, which can lead to microleakage at the restoration margins. Purpose: To report the success of class II restoration with composite resin using tofflmire matrix. Case report: A 23-year-old female patient came with a complaint of cavities, the complaint was felt since 1 year ago in the lower left back tooth, there was no pain and had never been treated. In the case, preparation and restoration were carried out using composite resin with isolation using tofflmire matrix, applied using an Incremental technique. Discussion: Class II posterior tooth restorations have challenges such as leakage due to the techniques and materials used. Composite resin is chosen because of its good adaptation, esthetics, and wear resistance. Incremental techniques are preferred because they reduce polymerization stress and provide optimal curing results. Tofflemire matrix is ​​used for stability and soft tissue protection.Conclusion: The selection of composite resin with an Incremental technique and the use of a Tofflemire matrix provides more optimal, aesthetic, and minimal risk of failure results for class II posterior tooth restorations.

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

The rapid development of digital technology has led to an increased adoption of digital payment methods in online transaction-based businesses. However, in practice, failures and limitations in the implementation of digital payment systems still occur, potentially disrupting transaction processes and reducing customer convenience. Payment related obstacles may result in transaction cancellations and increase the risk of customer churn. This study aims to analyze the impact of failures and limitations in digital payment methods on customer churn using a classification-based approach. The data used in this research are secondary e-commerce customer data obtained from the Kaggle platform, including transaction information, payment methods, customer behavior, and historical transaction records. The research methodology consists of data preprocessing, time-based feature engineering, and classification modeling using logistic regression, decision tree, and random forest algorithms. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the decision tree model demonstrates superior capability in identifying churn customers compared to the other models, although it does not always achieve the highest accuracy. In addition to digital payment methods, other factors such as purchase value, transaction frequency, purchase timing patterns, and product return rates also influence customer churn. The findings highlight the importance of optimizing digital payment systems as part of customer experience enhancement strategies and customer retention efforts in online transaction–based businesses.

This study was conducted to analyze the causes of occupational accidents and to identify potential hazards in the material cutting process at PT Kanaan Global Indonesia using the Job Safety Analysis (JSA) approach. The high number of occupational accidents, totaling 15 cases during the period of December 2024–May 2025, indicates the existence of gaps in occupational safety control in this area. Data were collected through direct observation, interviews with workers, and the collection of historical data from the Occupational Health and Safety (OHS) unit. Risk analysis was carried out using a risk matrix to determine likelihood and severity values, followed by JSA for each work stage. The results identified 25 potential hazards, consisting of 4 risks in the extreme category, 9 in the high category, 7 in the medium category, and 5 in the low category. High-risk hazards were predominantly found in roll cutting and cutting press machines, which are characterized by mechanical hazards. Control recommendations focus on the implementation of engineering controls such as the installation of guarding systems and safety light curtains, improvements in safe work procedures, and increased discipline in the use of personal protective equipment (PPE). These findings demonstrate that the JSA method provides a comprehensive overview of risk sources and serves as an effective basis for formulating occupational accident control strategies within the company.

This article proposes wasathiyah—a balanced Islamic ethic grounded in the maqasid al-shari‘ah—as a practical framework for East–West civilizational dialogue. It critiques “clash of civilizations” narratives for reducing cross-cultural relations to identity antagonism, while historical experience points to more complex patterns of exchange, negotiation, and mutual correction. By using maqasid as a normative compass, dialogue is oriented toward publicly verifiable aims: safeguarding human dignity, advancing justice, protecting reason and knowledge, strengthening social cohesion, and promoting sustainable governance. The discussion draws on Nahdlatul Ulama’s tradition of Aswaja An-Nahdliyah and contemporary discourse on fiqh al-hadlarah (civilizational jurisprudence) to formulate an indicator matrix across key domains—education, law and human rights, economy, media, and emerging technologies (including AI)—along with a 3–5 year implementation roadmap and risk-mitigation strategies. The article concludes by highlighting implications for Islamic education, particularly academic integrity, digital-ethical literacy, and interdisciplinary projects oriented to the public good (maslahah).

This study aims to develop a predictive model using linear regression to identify potential arrhythmias in the elderly based on electrocardiogram (ECG) data. Data were collected through observations at healthcare facilities from elderly patients with indications of arrhythmia, then preprocessed such as cleaning, normalization, feature selection, and outlier checking were carried out. The features used include PR interval, QRS duration, QT interval, and heart rate. The dataset was divided into training data (80%) and test data (20%) to build and evaluate the model. The training results showed that the model was able to predict the risk of arrhythmia with a Mean Squared Error (MSE) value of 0.15 and a coefficient of determination (R²) close to 1. Evaluation using a confusion matrix showed an accuracy of 76.19%, precision of 82.80%, recall of 76.19%, and F1 score of 72.70%. These results prove that linear regression can be used as an initial approach in the early detection of arrhythmias non-invasively in the elderly. This study provides a foundation for the development of ECG data-based clinical decision support systems and suggests future exploration of more complex models and integration with real-time monitoring technologies.

Guaranteeing the geotechnical stability of slopes is an absolute prerequisite for the sustainability of open pit mining operations, considering the potential for multidimensional losses due to slope failure. The specific geological conditions at PIT B1 PT. Pancaran Surya Abadi, which is composed of sedimentary rocks (coal, sandstone, and claystone), are susceptible to degradation and softening, especially due to high rainfall that causes an increase in pore water pressure and a decrease in rock shear strength. This study aims to analyze the stability of highwall slopes using the Morgenstern-Price Method to determine the Safety Factor (SFF) value according to Ministerial Decree number 1827 K/30/MEM/2018, and continued with a semi-quantitative risk analysis. The analysis results show that the initial slope has a static SFF of 0.77 (Not Safe). After redesign, the recommended optimal single slope geometry is: sandstone (Height 5 m, Angle 20°, Berm 5 m) and claystone (Height 10 m, Angle 60°, Berm 5 m). This redesign resulted in a FK of 1.34 (Safe). Sensitivity analysis to groundwater level rise (GTL) showed that the GTL value remained safe (GTL ≥1.30) up to a 30% increase in GTL. However, a 40% to 80% increase in GTL caused the GTL to decrease (1.28–1.21), classified as Medium Risk. A 100% increase in GTL drastically reduced the GTL to 1.05, classified as High Risk. This study emphasizes the need for close monitoring and additional drainage to maintain the long-term stability of slopes under the influence of rainfall.  

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.  

Soil contamination by hydrocarbons, pesticides, heavy metals, and complex pollutants is rapidly increasing and degrading essential ecosystem functions. Physical or chemical treatments offer faster results, yet they are often costly, energy-intensive, and risk disrupting soil biological integrity without fully eliminating pollution sources. Microorganism-based bioremediation provides a more sustainable alternative by utilizing microbial metabolism to degrade or immobilize pollutants into less toxic and less mobile forms. This article presents a structured literature review on the roles and applications of microorganisms for bioremediation of contaminated soils, covering comparisons between single isolates and microbial consortia, dominant biological mechanisms, and ecological challenges in field application. A Systematic Literature Review approach was applied, using narrative synthesis and thematic clustering of national and international journals published between 2020 and 2025. The review indicates that single microbial isolates are commonly selected for specific pollutant targets, whereas microbial consortia are preferred for mixed or persistent contaminants due to metabolic synergy that enhances microbial adaptability and stepwise pollutant breakdown in highly polluted soils. Adaptive mechanisms such as EPS production and biofilm formation contribute to microbial resilience under stress and help retain contaminants within the soil matrix. Key challenges identified include inoculum stability under extreme conditions and limited microbial access to pollutants trapped in micro-soil pores. The findings highlight that microbial selection strategies must be tailored to pollutant characteristics and soil environmental conditions, while also emphasizing the potential of biofilm-based systems and organic carriers to support broader field implementation of microbial bioremediation.

Gestational Diabetes Mellitus (GDM) is a pregnancy-related metabolic disorder that poses health risks to both mother and fetus if not detected early, requiring accurate prediction methods for early screening and clinical decision-making. This study applies the Random Forest algorithm to detect GDM risk using clinical data from the Pima Indian Dataset. Data preprocessing included handling missing values, standardization, feature engineering, and a 70:30 train–test split. Two models were developed: a baseline and an optimized model using GridSearchCV hyperparameter tuning, validated with 5-fold cross-validation. Performance was assessed using a classification report, confusion matrix, and ROC–AUC. Results show that the optimized model outperforms the baseline, achieving 88% accuracy, an AUC of  93%, and average recall of 81%–85%. Compared to previous studies, this approach demonstrates improved predictive performance. The findings indicate that combining Random Forest with comprehensive preprocessing, feature engineering, and model optimization is effective and feasible for developing a medical decision support system for early GDM risk screening.

Enhanced Recovery After Surgery (ERAS) is a multidisciplinary, evidence-based perioperative care approach designed to minimize the stress response to surgery, preserve organ function, and improve clinical outcomes. A substantial body of evidence has demonstrated that implementing ERAS protocols in elective procedures not only accelerates patient recovery but also reduces healthcare costs. Surgery, one of the most commonly performed medical interventions worldwide particularly major procedures such as abdominal and colorectal surgery carries a high risk of postoperative complications. These complications contribute to increased morbidity, mortality, and economic burden for both patients and healthcare systems. This situation presents a particular challenge in the era of universal health coverage, which demands efficiency in terms of time, cost, and resource utilization. ERAS implementation has been proven to enhance postoperative recovery, shorten hospital stays, and expedite the return of normal physiological function compared to conventional surgical care, especially in lower abdominal surgeries and colorectal resections. A literature review was conducted by searching relevant articles through Google Scholar using inclusion criteria such as publications from 2018 onwards, focused on ERAS in abdominal surgery, full-text availability, and academic journal sources. The data were analyzed using a matrix table comparing research methods, study populations, research locations, and reported outcomes. ERAS protocols have shown to be effective in abdominal surgical procedures for improving patient recovery and reducing postoperative complications.

This research aims to formulate a business development strategy for AK Laundry, a micro, small, and medium enterprise (MSME) located in Palembang that operates in the laundry service industry. As competition in this sector continues to intensify, MSMEs are required to adapt quickly through effective strategic planning. The study employed a descriptive qualitative approach, with data collected through interviews, direct observations, documentation, and questionnaires, ensuring a holistic understanding of both internal and external conditions. The analysis was carried out using the SWOT framework, which identifies internal strengths and weaknesses, as well as external opportunities and threats. The findings reveal that AK Laundry possesses several strengths, including good relationships with its loyal customer base, competitive pricing that appeals to a wide range of market segments, and a strategic location that facilitates accessibility. However, weaknesses were also identified, such as limited promotional efforts, particularly in digital channels, and occasional delays in completing customer orders, which may affect satisfaction and trust. From an external perspective, AK Laundry has opportunities to expand its services, particularly through the growing demand for pickup and delivery facilities, as well as changes in consumer lifestyles that increasingly prioritize practicality and efficiency. Nevertheless, the enterprise must also address potential threats, such as intense competition in pricing strategies among similar businesses and the risk of item loss, which could undermine its reputation. Based on the SWOT matrix, AK Laundry is positioned in Quadrant I, indicating that it holds considerable potential for aggressive growth. Therefore, the recommended strategies include strengthening digital marketing initiatives, introducing innovative services to differentiate from competitors, enhancing employee competencies through training programs, and upgrading equipment to improve service quality and speed. These strategies are expected to help AK Laundry leverage its strengths and opportunities effectively, ensuring sustainable development and competitiveness in the MSME laundry service sector.

According to the circular letter from the Head of the Inspectorate General (KaIT) regarding the review of mining accident cases in September 2024 and the review of mining accidents in the third quarter of 2024, the percentage of accidents occurring in workshops reached 16.13%. Over the past five years, the Plant Department of PT Saptaindra Sejati Jobsite Sera has experienced two major incidents classified as Lost Time Injury (LTI) resulting from working with lifting equipment on undercarriage components. The purpose of this study is to identify risks, analyze risk levels, and provide recommendations for risk control in the overhaul work of the PC210-10M0 undercarriage. This research applies the HIRADC method by identifying potential hazards through calculations of likelihood and severity levels to obtain the risk level using a risk matrix. Control measures are then carried out through administrative actions such as documentation and the use of personal protective equipment (PPE). The results of this study indicate a decrease in risk levels after implementing risk controls—from extreme risk to medium risk, and from high risk to low risk. Suggestions from this study include the need to develop updated HIRADC for each section, actively conduct socialization regarding Job Safety Analysis (JSA) before work, and perform inspections as well as observations related to work behavior.

This study aims to develop an accurate predictive model for identifying students at risk of academic dropout using Decision Tree and Random Forest algorithms. The research utilizes a publicly available dataset sourced from Kaggle, which includes academic and demographic features such as GPA, attendance, credit load, financial aid status, and exam scores. The methodology involves several stages: data collection, preprocessing (handling missing values, encoding categorical variables, and feature scaling), model training, and evaluation using performance metrics such as Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. Results show that the Random Forest algorithm outperforms Decision Tree in terms of accuracy and robustness, with notable feature importance on math, reading, and writing scores. The findings highlight the potential of machine learning in early detection of dropout risks and provide actionable insights for academic institutions to design timely interventions. This research contributes to the growing field of educational data mining and supports data-driven decision-making processes in higher education management.

This study analyzes the implementation of Integrated Reporting (IR) in Indonesia based on the International Integrated Reporting Council (IIRC) framework, aiming to evaluate the trends in IR adoption over the past three years and assess the extent to which companies in Indonesia have aligned their reporting practices with IIRC standards. Using content analysis and one-way repeated measures ANOVA, this research examines integrated annual reports of companies listed on the Indonesia Stock Exchange (IDX) for the 2020-2022 period, measuring the quality of IR implementation through a checklist derived from the International Integrated Reporting Framework (IIRF). The findings reveal that IR practices in Indonesia have significantly improved over time, with key dimensions experiencing notable enhancements, including stakeholder relationship, consistency and comparability, operating context, risk, governance, and performance, critical indicators for ensuring transparency and effectiveness in integrated reporting. Furthermore, this study contributes to the development of a matrix or quality checklist for IR, based on a normative interpretation of the IIRF, providing valuable insights into IR implementation in emerging markets, particularly Indonesia, which serves as an interesting case study since previous research has primarily focused on IR adoption in developed regions such as Europe and South Africa. Practically, this study emphasizes the importance for companies to enhance their IR reporting quality by focusing on aspects such as strategic focus and future orientation, connectivity, consistency, reliability, and comparability, thereby ensuring more transparent, accountable, and globally aligned financial and non-financial reporting practices.

This study aims to analyze the product diversification strategy implemented by Pegadaian Purwokerto Branch and its impact on the company's financial stability. A qualitative descriptive approach was applied using a case study method through structured interviews, direct observations, and internal document analysis. The research also integrates the General Electric (GE) Matrix to map the business strength and market attractiveness of each diversified product. The findings reveal that Pegadaian’s diversified products—such as gold savings, vehicle financing, and Hajj/Umrah financing—have significantly contributed to expanding market reach and increasing revenue. This success is supported by comprehensive feasibility studies, structured risk management systems, and the operational role of branch-level human resources. Nevertheless, challenges remain, particularly in service digitalization and HR readiness. This research contributes both theoretically and practically to product development strategies in financial services, especially for state-owned enterprises seeking sustainable business growth through data-driven diversification.

Decoction water betel leaf is a traditional Balinese medicine containing the active compound hydroxychavikol, has antioxidant and antidyslipemic activity. From the results of the study it was reported that decoction water of betel leaf contains the active compound hydroxycavicol (HC). The active compound hiroksikavikol has activity as an antioxidant and antidyslipidemia. As an anti-oxidant, it can scavenge ROS and inhibit the activity of free radicals. As an antidyslipidemia, it can normalize lipid metabolism by lowering total cholesterol, triglyceride, LDL and VLDL levels and increasing blood serum HDL levels. Oxidative stress and dyslipidemia are major risk factors for heart disease caused by atherosclerosis. Atherosclerosis is the occurrence of plaque formation in the lumen of blood vessels triggered by oxidative stress through endothelial cell dysfunction, inflammation and lipid peroxidation. Oxidative stress causes endothelial cell dysfunction, increased contractility, VSMC growth, monocyte invasion and lipid peroxidation, inflammation and increased deposition of extracellular protein matrix. Based on these things, it was concluded that HC loloh boiled water of betel leaf has antioxidant and antidyslipidemic activity to prevent heart disease.

PT Rhoda Bakti Jaya is a company located on Jl. Pegangsaan Dua, Blok A1 Km 1.6, Kelapa Gading, Rt 05 / Rw 02 Kec. Kelapa Gading, North Jakarta .PT Rhoda Bakti Jaya is a company engaged in manufacturing PT. Rhoda Bakti Jaya is a company operating in the industrial sector which is located at JL. Pegangsaan Dua, Blok A1 Km 1.6, Kelapa Gading, Rt 05/ Rw 02 Kec. Kelapa Gading, North Jakarta. This company produces various types of components for trucks, namely truck frame or chassis components, brake components and pistons and brake cylinders. This job has a high risk of accidents so that it causes many accidents to workers. Therefore, in this research, using the HAZOP method, we can analyze the level of risk using likelihood, consequences and put it into the risx matrix scale.