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This study aims to thoroughly examine the influence of menu variety, taste, and price on customers' purchasing decisions at CEZ-ROS Restaurant, located in Kediri Regency. Understanding these factors is crucial for the restaurant to enhance customer satisfaction and improve sales performance. The research employs a quantitative approach with an associative research type, designed to explore relationships between variables. The sampling technique used is accidental sampling, where respondents were selected based on convenience and their availability during the data collection period. This method allows for practical and efficient data gathering from actual restaurant customers. Data analysis involves several statistical tests to ensure accuracy and reliability. Initially, validity and reliability tests were conducted to confirm the suitability of the questionnaire. Then, multiple linear regression analysis was used to determine how menu variety, taste, and price individually and collectively influence purchasing decisions. Additionally, t-tests assessed the significance of each independent variable, while the F-test evaluated their simultaneous effect. The coefficient of determination (R²) provided insight into how much variation in purchasing decisions could be explained by these variables. The results indicate that menu variety, taste, and price significantly impact purchasing decisions both individually and together. Specifically, customers prefer restaurants offering a diverse menu, delicious food, and reasonable pricing. The F-test confirms the strong combined effect of these factors on consumer behavior. However, the coefficient of determination also suggests that while these variables are important, other external influences such as service quality, ambiance, and promotions may also affect purchasing decisions. Overall, this study highlights that CEZ-ROS Restaurant should focus on maintaining menu diversity, enhancing taste quality, and setting competitive prices to attract and retain customers effectively.

This study explores the role of Big Data in crisis communication in the digital age, where information dissemination happens quickly through various platforms. This transformation poses challenges such as the risk of spreading fake news and reputation crises. Big Data plays an important role in detecting potential crises early, as well as in understanding the dynamics of public opinion and designing more responsive and effective communication strategies. Through real-time data collection and analysis from various sources, such as social media and news reports, organizations can build smart and adaptive monitoring systems. This research uses a descriptive qualitative approach to explore the role of Big Data in crisis communication in the digital era. The results show that the crisis detection process starts from data collection, cleaning, to sentiment analysis that helps organizations measure public response. However, the application of Big Data also faces considerable challenges, including the complexity of processing data with high accuracy, privacy issues, and the readiness of infrastructure and human resources in the organization. This research provides clearer insights into the strategies that can be used to optimize Big Data in crisis communications, so that organizations can strengthen communication effectiveness, maintain reputation, and build public trust amidst evolving information challenges.

This study aims to analyze the optimization of seaweed seedling inventory management using the Economic Order Quantity (EOQ) method to enhance cost efficiency for farmers in Tarakan City. The research employs a quantitative descriptive approach, integrating EOQ with forecasting techniques (Moving Average and Exponential Smoothing) to predict raw material needs accurately. Data were processed using Microsoft Excel and POM-QM for Windows to ensure precision. The results indicate that EOQ yields an optimal order quantity of 878 ropes per order, with a frequency of 6 orders per year, a reorder point of 16 ropes, and a total inventory cost (TIC) of IDR 842,681. Compared to traditional methods (TIC IDR 2,132,083), EOQ reduces costs by 60.5%. Forecasting analysis reveals that Exponential Smoothing (MAPE 19.67%) outperforms Moving Average (MAPE 22.5%) in accuracy. These findings highlight EOQ’s effectiveness in minimizing waste, preventing stockouts, and improving productivity. The study provides practical insights for coastal small-scale farmers and policymakers in the marine sector.

This research aims to analyze the level of student satisfaction with academic administration services at the Catholic Religious Education Study Program (PKK) STPKat Santo Fransiskus Assisi Semarang in the 2024/2025 Academic Year. Effective and efficient academic administration services are an important factor in supporting the smooth teaching and learning process and improving the overall quality of education. The research method used is descriptive quantitative with a survey approach. Data was collected through a questionnaire distributed to active students of the PKK Study Program. The variables studied include speed of service, accuracy, attitude of officers, ease of procedures, and supporting facilities. The research results show that in general students feel quite satisfied with the academic administration services provided. Aspects of officer attitude and correct information received the highest satisfaction scores, while aspects of ease of procedures and supporting facilities still require improvement. It is hoped that these findings can provide input for the management of the PKK Study Program to improve the quality of academic administration services, so as to create a more conducive academic environment and support student achievement.

In determining sales targets, each Dealer determines sales targets only based on previous sales, without any fixed calculation method. The aim of this research is to find a calculation method that is considered closest, so that it can be used to determine sales targets for the next period. The sample studied was sales of automatic motorbikes in October, November and December for the 2022 period, using actual company sales data as a reference as a comparison. The research results show that the Least Square Method can be implemented as a fixed calculation to determine sales targets for Honda brand motorbikes, with an average MAPE value of 5.3% or an accuracy value of 94.7%.    

Recent developments in information and communication technology such as artificial intelligence (AI) provide new opportunities to increase the efficiency and accuracy of performance assessment. The impact of the COVID-19 pandemic and the shift to a remote work model has created a need for technology solutions to unify and transmit the performance of employees working from multiple locations. The use of technology in performance management evaluations may raise concerns regarding data security and privacy, inconvenience or resistance to employees or stakeholders. Not all employees or managers have the same level of skill and understanding of the technology being implemented and it requires a high initial investment in maintenance and upgrades. This article provides an understanding of how technology can be applied in management performance evaluation and its benefits in increasing efficiency, objectivity and accuracy in employee assessment as well as providing recommendations for innovation and changes in the use of technology in management performance evaluation. The methodology used is a qualitative method by collecting data from various information sources. Some of the positive impacts of implementing technology in performance evaluation management are increased efficiency and productivity, real-time feedback, increased objectivity and fairness, more accurate and measurable performance monitoring. The technologies that can be used are Go Talent, Feedback and Recognition Systems, Analytics Technology, Application-Based Performance Evaluation Systems, Use of AI for Objective Evaluation and Chabots for Feedback.