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This study aims to analyze the quality of room attendant service based on Standard Operating Procedures (SOPs) at the Lorin Solo Hotel and identify factors influencing its implementation. The research method used a qualitative approach through interviews, observations, and analysis of guest reviews.The results indicate that the implementation of SOPs has been successful, particularly in terms of room cleanliness, linen changes, replenishment of amenities, checking facilities, and friendly service. However, during periods of high occupancy, obstacles were encountered such as limited staff, limited cleaning time, and delays in replenishing amenities. Internal factors (skills, training, work motivation) and external factors (occupancy rate, guest demand, facility condition) influence service quality.This study concludes that the implementation of SOPs at the Lorin Solo Hotel has been successful, but needs improvement in time management, additional staff during busy periods, and regular training to maintain consistent service quality. These recommendations are expected to help the hotel improve service standards and maintain guest satisfaction.

The retail industry is currently facing increasingly intense competition, requiring optimal supply chain efficiency. Warehouses play a strategic role in controlling inventory flows and supporting a company’s operational performance. At K3Mart, stock shortages are still frequently found, caused by low supplier compliance with Service Level Agreements (SLA) and delays in delivery schedules, which negatively affect distribution processes and store service quality. This study aims to analyze the effect of supplier compliance with SLA and delivery timeliness on warehouse operational performance at K3Mart. This research employs a quantitative method by distributing questionnaires to 40 respondents from the warehouse, purchasing, and procurement divisions using a saturated sampling technique. The research instrument uses a Likert scale. Data analysis was conducted using validity and reliability tests, classical assumption tests, multiple linear regression analysis, t-test, F-test, and coefficient of determination with the assistance of SPSS software. The results show that supplier compliance with SLA and delivery timeliness have a positive and significant effect, both partially and simultaneously, on warehouse operational performance. Therefore, improving supplier compliance with SLA and delivery timeliness can enhance optimal warehouse operational performance.

This study aims to analyze the investigation process involving children as perpetrators of trafficking in persons at the Women and Children Protection Unit (PPA) of Bukittinggi Police Resort and to assess the forms of legal protection provided at each stage of the examination. This research applies an empirical juridical approach by combining literature review and field research through interviews with investigators and related parties. The findings indicate that the investigation process has been conducted in accordance with Law Number 11 of 2012 concerning the Juvenile Criminal Justice System, while upholding the principle of the best interests of the child. Legal protection is implemented through legal assistance, closed hearings, limited detention periods, and coordination with relevant institutions to support the child’s recovery and rehabilitation. However, several obstacles remain, including the limited number of investigators, restricted investigation timeframes, and difficulties in presenting witnesses. This study concludes that legal protection for children involved as perpetrators of trafficking in persons has been carried out in line with normative regulations, yet it requires strengthened institutional capacity and inter-agency coordination to ensure more effective implementation.

This study aims to analyze the influence of interest rates, exchange rates, and coffee production on the value of coffee exports in South Sulawesi. The background of this study is based on the condition of South Sulawesi coffee exports which have experienced significant fluctuations in recent years despite coffee production tending to increase. This study uses a quantitative approach with time series data for the period 2009-2023 sourced from the World Bank, International Monetary Fund and the Directorate General of Plantations, the Food Crops, Horticulture and Plantation Service of South Sulawesi Province. Data analysis was conducted using multiple linear regression through the EViews 12 application with the classical assumption test as a model prerequisite. The results show that partially interest rates have a significant effect on coffee exports, while exchange rates and coffee production do not have a significant effect. Simultaneously, the three independent variables do not have a significant effect on the value of coffee exports. This finding indicates that external factors, especially interest rates, are more dominant in determining the performance of South Sulawesi coffee exports than internal factors of production and exchange rates.

International student enrollment is a critical driver of financial sustainability for Higher Education Institutions (HEIs). While advanced forecasting is standard in the corporate sector, its application in educational planning remains limited. This study addresses this gap by comparing the predictive performance of ARIMA, Facebook Prophet, and Long Short-Term Memory (LSTM) models. Using a publicly available annual dataset from a US-based institution (2000–2022), the analysis employed a strategic partition training on 2000–2017 and testing on 2018–2019 to validate models on stable, pre-pandemic data. Empirical results revealed that the statistical ARIMA (2,1,0) model demonstrated superior accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 1.26%. Conversely, Prophet (11.81%) and LSTM (13.84%) struggled with the limited sample size, failing to generalize effectively compared to the linear approach. The findings suggest that for annual enrollment trends, parsimonious statistical models outperform complex deep learning architectures, providing administrators with a robust, accessible framework for data-driven strategic decision-making.

The rapid growth of e-commerce has intensified the need to understand transaction patterns and customer purchasing behavior as a foundation for strategic decision-making. This study aims to analyze e-commerce transaction patterns and customer purchasing behavior based on demographic characteristics and transaction timing. By utilizing e-commerce transaction data, this research seeks to provide a more comprehensive understanding of customers’ purchasing tendencies and the factors influencing their behavior. This study employs Exploratory Data Analysis (EDA) as the primary method to descriptively explore data characteristics through various statistical visualizations, including histograms, bar charts, line graphs, and boxplots. The analysis conducted to identify transaction trends, the distribution of purchase values, and behavioral differences across demographic groups and specific time periods. The results indicate that e-commerce transaction patterns tend to increase during certain periods, particularly in the latter part of the observation timeframe, suggesting the influence of seasonal factors and promotional strategies. The distribution of transaction values is asymmetric, with most transactions occurring in the low to medium value range, while high-value transactions are conducted by a relatively small proportion of customers. Furthermore, variations in purchasing behavior are observed across demographic groups in terms of transaction frequency and value, despite relatively balanced transaction volumes. The findings confirm that e-commerce customer purchasing behavior is influenced by a combination of temporal factors and demographic characteristics. These results are expected to serve as a basis for e-commerce practitioners in developing more targeted marketing strategies and as a reference for future research in the field of e-commerce data analytics.

This study examines the seismic performance of slender Air Traffic Control (ATC) towers in high‑hazard regions (PGA > 0.4g), where vertical taper, torsional eccentricity, and top‑heavy cab mass can significantly increase drift, base shear, and collapse risk relative to conventional buildings. Existing studies often rely on linear procedures and outdated provisions, leading to underestimation of nonlinear behaviour and limited guidance for ATC towers designed to SNI 1726:2019. The research aims to quantify these irregularity effects and formulate design recommendations that satisfy Immediate Occupancy, Life Safety, and Collapse Prevention performance targets. The methodology couples response spectrum analysis, using a site‑specific Padang spectrum consistent with SNI 1726:2019 and ASCE 7‑16, with nonlinear pushover analysis interpreted through FEMA/ATC performance‑based criteria. A parametric study is performed on three cab configurations small, medium, and large modelled as 5%, 15%, and 25% mass ratios at the tower head, while keeping a 10 m × 10 m hybrid core–frame shaft constant. Results indicate that larger cab mass produces systematic but moderate increases in global displacement, story drift, and base shear, while plastic hinges localize primarily in the upper stories and cab‑support region, yielding performance levels from Immediate Occupancy to Collapse Prevention. Overall, the tower meets code drift limits and acceptable performance if local strengthening is provided around the shaft–cab interface, offering a calibrated reference for top‑heavy ATC tower design in Indonesian high‑seismic settings and identifying priorities for future time‑history and soil–structure interaction studies.

The 50 meter freestyle is a sprint event that requires explosive strength, efficient technique, and optimal body stability. Arm muscle strength contributes directly to propulsion, while core muscle strength supports stability and force transfer. This study aimed to analyze the influence of arm and abdominal muscle strength on 50-meter freestyle swimming performance among athletes of the Science Swimming Team (SST) Sidoarjo. A quantitative correlational design was employed with a sample of 10 athletes aged 10–12 years (5 males, 5 females). Arm muscle strength was measured using the pull-up test, abdominal strength with a 30-second sit-up test, and swimming performance with the best time in a 50-meter freestyle trial. Data were analyzed using Spearman correlation and multiple linear regression with a 0.05 significance level. Results indicated a significant correlation between arm strength and swimming performance (r = 0.773; p = 0.009), while abdominal strength showed a positive but non-significant correlation (r = 0.609; p = 0.062). Regression analysis revealed that both variables together explained 57.7% of the variance in swimming speed. It is concluded that arm muscle strength has a dominant influence on freestyle sprint performance, whereas core muscles serve as supporting factors for stability and efficiency. These findings highlight the importance of integrated training programs combining arm and core strengthening to optimize sprint performance in young swimmers.

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

Labor productivity is a critical factor in construction project success as it directly affects time, cost, and work quality. In the Palm Vista Residence housing development project, delays in ground floor structural works indicate suboptimal human resource performance. This study aims to analyze labor productivity levels, identify factors influencing productivity, and formulate strategies to improve productivity in sloof beams, columns, and floor slab works. The research employs a quantitative approach with descriptive-analytical methods through field observations, questionnaires, and interviews. Labor productivity is measured using the work sampling method and the Labour Utilization Rate (LUR), while influencing factors are analyzed using multiple linear regression. Strategy prioritization for productivity improvement is determined using the Analytical Hierarchy Process (AHP). The results show that labor productivity is at a moderate level and is significantly influenced by work experience, managerial aspects, and coordination and planning. The priority strategies include improving worker competence and strengthening project management practices. These findings are expected to provide practical guidance for construction labor management to enhance efficiency and ensure timely project completion.

The era of globalization and the rapid advancement of information technology have transformed the landscape of dakwah (Islamic propagation), shifting from conventional pulpits to virtual communication platforms (social media, podcasts, streaming). This transition, in line with the Diffusion of Innovation Theory, enables Islamic messages to reach a broad audience without geographical boundaries. Digital dakwah is highly relevant and urgent to research given the dominant use of social media by Muslim Youth, a group that seeks attention but also possesses a tendency toward critical thinking. This research has a dual aim: (1) to analyze the Interpretation of Muslim Youth towards virtual dakwah studies, and (2) to analyze the Effectiveness of Implementing Dakwah Studies through social media platforms. The accessibility of fragmented and competitive content makes the interpretation process active and crucial, where youth are no longer passive recipients but choose sources based on algorithms and their own interests. This study uses the Constructivism Paradigm to understand a dynamic and complex social reality, employing a Descriptive Qualitative Approach and the Online Ethnography Method. Data is collected through Observation, Interviews, and Documentation, with Data Triangulation (source, technique, and time) to ensure the validity of the findings. Social media holds great potential as an effective and inclusive dakwah tool. However, the effectiveness of its expansion must be balanced with the quality of the content and the capacity of Muslim Youth to perform critical, contextual, and responsible interpretation.

Online transportation services have become an important part of student life because they provide ease of access, time efficiency, and mobility flexibility. The Gojek application is one of the most frequently used services by students in Medan City to support academic and non-academic activities. This study aims to analyze students' experience in using the Gojek application and its effect on the effectiveness of the application in online transportation services. The research method used was a quantitative approach with survey techniques through a Likert scale questionnaire (1–5) to 50 students who were active users of Gojek. Data were analyzed using descriptive statistics and simple linear regression. The results showed that user experience (UX) was in the category of quite good with an average value of 3.58, while the effectiveness of the application was in the category of quite good with an average of 3.47. The reliability test showed Cronbach's Alpha values of 0.935 (UX) and 0.951 (Effectiveness), which means the instrument is very reliable. The results of the regression analysis showed that user experience had a positive and significant effect on the effectiveness of the application (β = 0.961; R² = 0.881; p < 0.001). Thus, the better the student experience in using the Gojek application, the higher the effectiveness felt in online transportation services. These findings confirm that improving the quality of user experience is a key factor to maintain and increase the effectiveness of online transportation applications among students.

This study aims to design, implement, and compare the performance of the Backpropagation algorithm from Artificial Neural Networks and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model in predicting the optimal daily rice requirement at Grillme Restaurant in Pontianak. The main problem faced by the restaurant is the uncertainty in determining the required daily rice stock, which periodically results in either understocking (shortage) or overstocking (wastage), leading to operational losses. To address this, the study utilizes historical daily rice sales data from January 2023 to April 2025 as the database for training and testing both predictive models. The SARIMA approach is employed to capture time series components (trend and seasonality), while Backpropagation is utilized to model non-linear patterns. Comparative test results indicate that the SARIMA model achieved superior accuracy compared to the Backpropagation model. This is confirmed by the Mean Absolute Percentage Error (MAPE) value of the SARIMA algorithm being 17.35%, which is lower than the MAPE value of Backpropagation at 19.62%. The MAPE values obtained by both models demonstrate good predictive capability, but it is concluded that SARIMA is more recommended for a more efficient and planned management of rice stock at Grillme Restaurant in Pontianak.

This article examines the integration of constructivist theory in Christian Religious Education (PAK) by emphasizing the role of personal experience as the basis for faith formation. Through a reflective-theological qualitative approach, this research combines the constructivist thought of Piaget, Vygotsky, and Fosnot with a theological perspective on the incarnation of Christ and the work of the Holy Spirit. The analysis suggests that constructivism can be theologically reinterpreted into a new paradigm called the Theo-Constructive Epistemology of Incarnational Faith, in which faith is not simply taught cognitively, but is constructed through life experiences infused by the presence of God. This approach emphasizes that the learning of faith is relational, participatory, and contextual, in line with the nature of God who works in human history. The findings of this study offer a new direction for PAK praxis to be more relevant to digital contexts and cultural plurality, without losing its theocentric orientation. Thus, the integration of constructivism and incarnational theology opens up space for more dynamic, contextual, and transformative learning of faith, while strengthening the relevance of PAK in facing the challenges of modern times.

This research explores the impact of Cache Aware optimizations on signal processing pipelines in High Throughput computing systems. The growing demand for efficient memory management in modern computing systems, especially for data-intensive applications such as artificial intelligence (AI) and multimedia processing, necessitates the development of optimized memory hierarchies. Traditional memory systems often suffer from memory bottlenecks, significantly reducing the performance of these systems. This study investigates how memory hierarchy optimizations, particularly cache line aware optimization, dependency-aware caching, and adaptive cache replacement algorithms, can mitigate these challenges and improve system performance. Through analytical modeling and experimental benchmarking, this work evaluates various memory hierarchy configurations, including processing-in-memory (PIM) and three-dimensional integrated circuits (3D ICs), comparing them to conventional systems. The results demonstrate that Cache Aware optimizations lead to a reduction in memory access latency by up to 30%, while throughput improved by up to 40%. Additionally, cache hit rates increased by 25%, and energy consumption was reduced by up to 20%, highlighting the effectiveness of optimized memory management. The research contributes to the field by providing valuable insights into the design and implementation of efficient signal processing pipelines. It also identifies key challenges, including the need for dynamic occupancy mechanisms and DAG-aware scheduling algorithms, and suggests potential areas for future research, such as the exploration of collaborative caching approaches and further optimization of cache-adaptive algorithms. This work lays the foundation for more efficient, high-performance computing systems that can handle large datasets and complex tasks in real-time applications.

The majority of Kebomas District's residents are connected to working hours during government office hours (Monday through Friday), which creates access obstacles in administering public services due to the high population density and busy community life. In response to these time constraints, the SOS MAS (Service On Saturday in Kebomas District) invention was introduced, which offers services on Saturdays. However, the implementation of this innovation still faces challenges in the form of a low social integration rate and a restricted range of public services that have the potential to sway public opinion. The goal of this research is to assess how the SOS MAS innovation affects community happiness in Kebomas District. A quantitative technique with an associative approach is used in this research. Through an unintentional sampling method, questionnaires were given to 86 individuals in order to gather primary data. With the aid of SPSS, the determinant coefficient and a simple linear regression test were used to analyze the data. The study's findings reveal that the SOS MAS invention has a favorable and substantial influence on community happiness. The regression coefficient of 0.969 and a significance level of 0.000 <0.05 support this. The innovation variable accounted for 59.7% of public happiness, while the remaining 40.3% was impacted by variables not included in the study model, according to the coefficient of determination (R2) test. This result supports the notion that the efficacy of service innovation outside of working hours is a major element in boosting happiness for individuals with limited time. The SOS MAS innovation has been shown to greatly improve public satisfaction. Agencies are encouraged to improve their communication and outreach efforts as well as create SOS MAS innovations that maximize the advantages of innovations that meet community needs.

The concept of Human Rights in the perspective of modern Islamic law according to Abdullah Ahmed An-Naim emphasizes the importance of prioritizing humanitarian values ​​as a central part of Islamic law. An-Naim proposes that Islamic law needs to undergo a comprehensive reconstruction to remain in line with the development of the times without losing its Islamic elements. According to him, the Universal Declaration of Human Rights is a very important tool to maintain the dignity, honor, and rights of all individuals throughout the world. An-Naim also encourages the elimination of all forms of violence and emphasizes the importance of recognizing humans as subjects that must be valued and respected, as well as providing sufficient space for human rights values ​​in public contexts and rejecting the application of sharia as an oppressive state law, but rather as a moral guideline that is more flexible and appropriate to the conditions of today's society.

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.

This study addresses the urgent need for digital transformation at Percetakan Rajawali, a printing company currently reliant on manual order processes, which leads to service delays, recording errors, and lack of transparency. The research aims to design and implement a web-based online ordering information system to facilitate customer orders, accelerate confirmation and order tracking, and improve the efficiency of order data management. Adopting the linear and sequential Waterfall methodology, the study progressed through stages of system analysis, design, coding, testing, and maintenance. The key findings include the successful design of a comprehensive system featuring product catalogs, an online ordering module with real-time cost calculation, integrated payment management, and an admin dashboard for order processing. The system is designed with user-centered and secure principles, expected to significantly enhance operational efficiency, service speed, and customer satisfaction by providing an integrated, transparent, and accessible online ordering platform.