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Analysis of the Relationship Between Understanding Mathematical Logic and Managerial Decision-Making Effectiveness
No full textManagerial decision-making, a cornerstone of organizational success, often relies on logical reasoning to address complex scenarios and develop effective strategies, forming the basis of this research. While logical reasoning has been widely recognized, the integration of mathematical logic as a foundational tool for enhancing decision-making effectiveness has remained underexplored. This study investigates how understanding mathematical logic, particularly propositional and predicate logic, impacts managerial capabilities in analyzing problems, formulating strategies, and implementing decisions. To achieve this, the study employs a quantitative approach, utilizing a survey distributed to 150 managers across diverse industries, with data analyzed using Structural Equation Modeling (SEM) to identify relationships between variables. The findings demonstrate the practical applications of mathematical logic, including its utility in strategic planning for technology firms, operational optimization in manufacturing, and improving decision-making frameworks in healthcare. These insights lead to the understanding that mathematical logic is a valuable tool for strengthening managerial decision-making processes, offering significant theoretical and practical contributions to organizational practices
The Role of Automation and IoT in Enhancing Operational Efficiency: Evidence from PLS-SEM Analysis
No full textIn today\u27s competitive business environment, operational efficiency is crucial for organizations to maintain a competitive edge. The integration of automation and the Internet of Things (IoT) has emerged as a transformative approach to streamline processes and enhance productivity. However, the synergistic impact of these technologies on operational efficiency remains underexplored. This study aims to evaluate the individual and combined effects of automation and IoT on operational efficiency. It seeks to provide empirical evidence on how these technologies contribute to optimizing workflows and decision-making processes. Methodology Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from organizations across multiple industries. Constructs were measured through validated survey instruments, and hypotheses were tested for direct and synergistic effects. The findings indicate that automation significantly enhances operational efficiency by reducing errors and improving process consistency. IoT adoption complements this by enabling real-time insights and improved decision-making. The combined implementation of these technologies demonstrates a moderate synergistic effect, amplifying operational gains. This study underscores the transformative potential of integrating automation and IoT. By leveraging their complementary strengths, organizations can achieve higher levels of efficiency, providing valuable guidance for digital transformation strategies.
In today\u27s competitive business environment, operational efficiency is crucial for organizations to maintain a competitive edge. The integration of automation and the Internet of Things (IoT) has emerged as a transformative approach to streamline processes and enhance productivity. However, the synergistic impact of these technologies on operational efficiency remains underexplored. This study aims to evaluate the individual and combined effects of automation and IoT on operational efficiency. It seeks to provide empirical evidence on how these technologies contribute to optimizing workflows and decision-making processes. Methodology Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from organizations across multiple industries. Constructs were measured through validated survey instruments, and hypotheses were tested for direct and synergistic effects. The findings indicate that automation significantly enhances operational efficiency by reducing errors and improving process consistency. IoT adoption complements this by enabling real-time insights and improved decision-making. The combined implementation of these technologies demonstrates a moderate synergistic effect, amplifying operational gains. This study underscores the transformative potential of integrating automation and IoT. By leveraging their complementary strengths, organizations can achieve higher levels of efficiency, providing valuable guidance for digital transformation strategies
Innovative Strategies Using Gojek and Grab Digital Platforms to Boost Brand Sales
No full textIn the digital era, shifting consumer behaviors and advancing technology ne- cessitate innovative marketing strategies. This study examines how brands leverage on-demand platforms like Gojek and Grab to enhance sales, market reach, and engagement, situated within global digital transformation trends akin to platforms like Uber Eats. Employing a qualitative case study and literature analysis, it identifies strategies grounded in theories such as consumer loyalty and customer value, including location-based promotions (used by 70% of businesses), data-driven personalization, and loyalty programs (adopted by 80%). These approaches improve distribution and create personalized cus- tomer experiences, with reviews and analytics informing decisions. However, challenges like high commission fees and algorithm dependency, particularly for MSMEs, require strategic solutions. Findings highlight that platform col- laborations drive competitive advantage, mirroring global platform economies. This research offers actionable insights for MSMEs and enterprises to design effective digital strategies and recommends exploring platform applications in non F&B sectors for future studies, contributing to digital marketing literature and practice in dynamic markets
Implementation of Naive Bayes for Optimizing Asset Condition Classification in a Web-Based Information System
No full textImproving the quality of work performance is an essential aspect for employees at the Office of Investment and Integrated One-Stop Services of Central Sulawesi Province. Many challenges remain in managing asset data, especially because the recording and monitoring processes are still performed manually. This manual approach often leads to inconsistencies, inefficiencies, and difficulties in determining asset eligibility. Therefore, an information system capable of supporting accurate and efficient data management is highly needed. The main objective of this study is to apply the Naive Bayes algorithm to classify asset conditions in a web-based system, enabling faster decision-making and improving the accuracy of asset feasibility assessments within government institutions. The dataset used in this study consists of three key attributes asset functionality, asset age, and physical condition. These attributes serve as indicators for classification using the Naïve Bayes probabilistic approach. The developed web-based application was evaluated through black-box testing to ensure that all system functions performed according to expectations and produced consistent outputs. Black-box testing results show that the system successfully provides correct outputs for each test scenario, verifying that the classification and data management processes operate properly. The application is able to classify assets into feasible or non-feasible categories based on calculated probabilities. Findings indicate that implementing the Naïve Bayes algorithm significantly improves the efficiency of asset data processing and enhances data management quality. The system also supports more objective decision-making regarding asset feasibility. This study demonstrates that probabilistic classification can be effectively integrated into governmental asset management systems to optimize operational performance.Improving the quality of work performance is an essential aspect for employees at the Office of Investment and Integrated One-Stop Services of Central Sulawesi Province. Many challenges remain in managing asset data, especially because the recording and monitoring processes are still performed manually. This manual approach often leads to inconsistencies, inefficiencies, and difficulties in determining asset eligibility. Therefore, an information system capable of supporting accurate and efficient data management is highly needed. The main objective of this study is to apply the Naive Bayes algorithm to classify asset conditions in a web-based system, enabling faster decision-making and improving the accuracy of asset feasibility assessments within government institutions. The dataset used in this study consists of three key attributes asset functionality, asset age, and physical condition. These attributes serve as indicators for classification using the Naïve Bayes probabilistic approach. The developed web-based application was evaluated through black-box testing to ensure that all system functions performed according to expectations and produced consistent outputs. Black-box testing results show that the system successfully provides correct outputs for each test scenario, verifying that the classification and data management processes operate properly. The application is able to classify assets into feasible or non-feasible categories based on calculated probabilities. Findings indicate that implementing the Naïve Bayes algorithm significantly improves the efficiency of asset data processing and enhances data management quality. The system also supports more objective decision-making regarding asset feasibility. This study demonstrates that probabilistic classification can be effectively integrated into governmental asset management systems to optimize operational performance
Opinion Mining for Customer Satisfaction in Culinarypreneur Ventures Using Naive Bayes
No full textExamining consumer evaluations of food on social media provides relevant in- formation for anyone searching, especially immigrants and tourists. This infor- mation is also highly valuable for food stall owners and restaurant managers be- cause it helps them improve the quality of the food they serve based on customer feedback. However, sentiment analysis of food reviews often faces challenges due to inadequate data preprocessing, which leads to low classification accuracy. This study aims to improve sentiment recognition accuracy in food reviews by optimizing the feature attribute selection process in the classification model. The classification model employed in this research is Naive Bayes (NB), enhanced through a hybrid feature selection approach that combines the information gain (IG) algorithm and the genetic algorithm (GA). This combination is designed to maximize the selection of the most relevant feature attributes, thereby improving the model’s ability to identify positive, negative, and neutral sentiments in con- sumer food reviews on social media. The experimental results show that the hybrid IG-GA model achieved the highest accuracy rate of 93%, outperform- ing models that use individual algorithms. These findings demonstrate that the hybrid feature selection method effectively enhances the sentiment analysis performance of the Naive Bayes model. This study contributes to the develop- ment of food recommendation systems, the improvement of service strategies for culinary businesses, and supports the achievement of SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure).Examining consumer evaluations of food on social media provides relevant in- formation for anyone searching, especially immigrants and tourists. This infor- mation is also highly valuable for food stall owners and restaurant managers be- cause it helps them improve the quality of the food they serve based on customer feedback. However, sentiment analysis of food reviews often faces challenges due to inadequate data preprocessing, which leads to low classification accuracy. This study aims to improve sentiment recognition accuracy in food reviews by optimizing the feature attribute selection process in the classification model. The classification model employed in this research is Naive Bayes (NB), enhanced through a hybrid feature selection approach that combines the information gain (IG) algorithm and the genetic algorithm (GA). This combination is designed to maximize the selection of the most relevant feature attributes, thereby improving the model’s ability to identify positive, negative, and neutral sentiments in con- sumer food reviews on social media. The experimental results show that the hybrid IG-GA model achieved the highest accuracy rate of 93%, outperform- ing models that use individual algorithms. These findings demonstrate that the hybrid feature selection method effectively enhances the sentiment analysis performance of the Naive Bayes model. This study contributes to the develop- ment of food recommendation systems, the improvement of service strategies for culinary businesses, and supports the achievement of SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure)
Cost Decision Making Using Activity-Based Costing Approach in Digital Information Systems
No full textThis study aims to develop and apply the Activity-Based Costing (ABC) method in determining overhead costs in the rice processing industry at MGS Tanjung Selamat Rice Mill. Conventional methods often cause distortions in the allocation of overhead costs, which have an impact on the inaccuracy in the calculation of the cost of goods manufactured (COGS). To overcome this problem, this research uses a Research and Development (R&D) approach with a system development model based on the Waterfall method. Data were collected through observations, interviews, and documentation studies, which were then analyzed to identify the main production activities and the most influential cost drivers. The results showed that the ABC method was able to improve the accuracy of overhead cost calculation, optimize cost allocation to each production activity, and support more strategic business decision-making. In addition, this research produced a software-based system to facilitate the implementation of the ABC method in the company. With the implementation of ABC, MGS Tanjung Selamat Rice Mill can set a more competitive selling price and identify less efficient activities to be improved.This study aims to develop and apply the Activity-Based Costing (ABC) method in determining overhead costs in the rice processing industry at MGS Tanjung Selamat Rice Mill. Conventional methods often cause distortions in the allocation of overhead costs, which have an impact on the inaccuracy in the calculation of the cost of goods manufactured (COGS). To overcome this problem, this research uses a Research and Development (R&D) approach with a system development model based on the Waterfall method. Data were collected through observations, interviews, and documentation studies, which were then analyzed to identify the main production activities and the most influential cost drivers. The results showed that the ABC method was able to improve the accuracy of overhead cost calculation, optimize cost allocation to each production activity, and support more strategic business decision-making. In addition, this research produced a software-based system to facilitate the implementation of the ABC method in the company. With the implementation of ABC, MGS Tanjung Selamat Rice Mill can set a more competitive selling price and identify less efficient activities to be improved
Workplace Digitalization and HR Innovation in the Era of Industry 5.0
No full textThis study investigates the impact of workplace digitalization on human resource (HR) innovation within the context of Industry 5.0. While Industry 4.0 empha- sized automation, Industry 5.0 highlights the synergy between advanced tech- nologies and human-centric practices. To analyze this relationship, a survey was conducted involving 150 HR professionals across multiple industries in Indonesia, focusing on the adoption of digital HR tools and innovative practices. The findings reveal that workplace digitalization has a positive and significant ef- fect on HR innovation (β = 0.52, p < 0.01), particularly in recruitment, digital training, and performance management. However, barriers such as employee resistance and cybersecurity risks negatively moderate this relationship. This re- search contributes to the literature by linking digital transformation to HR prac- tices in the Industry 5.0 era and offers practical implications for managers in improving organizational readiness through digital HR innovation
Risk Management Strategies in Blockchain Adoption within Financial Institutions Analyzing Challenges and Opportunities
No full textThe integration of blockchain technology in financial institutions has introduced both groundbreaking opportunities and significant risks, necessitating a comprehensive approach to risk management. Blockchain’s potential to enhance transparency, security, and efficiency in financial processes makes it an attractive technology for financial institutions. However, issues like regulatory uncertainty, data privacy, and technological readiness present unique challenges. This study aims to identify, evaluate, and provide insights into the primary risks associated with blockchain implementation in financial institutions, focusing on both challenges and opportunities to aid in effective risk management. Using the Structural Equation Modeling (SEM) technique with Partial Least Squares (PLS), also known as SmartPLS, this study examines data collected from financial industry stakeholders, including risk managers and IT experts. Variables assessed include data security, regulatory compliance, and technological infrastructure, allowing for a nuanced understanding of the risk dynamics within blockchain adoption. The analysis reveals that data security risks and regulatory concerns significantly impact blockchain implementation success, while technological readiness serves as a moderating factor, influencing the ease of adoption and operational success. Findings underscore the need for a balanced approach to blockchain integration in financial services, where risk management strategies address both regulatory and technological challenges. By identifying these core risks and their implications, this study contributes to the body of knowledge on blockchain risk management and offers practical recommendations for financial institutions aiming to adopt blockchain effectively while minimizing associated risks
Risk Management Model for Compliance and Security in Blockchain Powered Payment Platforms
No full textBlockchain technology has revolutionized financial services by enabling decen- tralized, transparent, and tamper-resistant payment platforms. However, these innovations bring significant challenges related to regulatory compliance and security management, which threaten platform adoption and user trust. This study aims to develop and empirically validate a comprehensive risk management model that integrates both regulatory oversight and security auditing dimensions specific to blockchain-powered payment systems. A cross-sectional survey was conducted among 215 industry practitioners involved in blockchain payment platforms. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study tested hypothesized relationships among regulatory over- sight, smart contract auditing, perceived compliance and security risks, risk mit- igation intent, and platform adoption intention. The results demonstrate that regulatory oversight and smart contract auditing significantly increase perceived compliance and security risks. These heightened risk perceptions positively in- fluence intentions to mitigate risks, which in turn significantly drive platform adoption. The model explains 58% and 42% of the variance in risk mitigation intent and platform adoption intention, respectively, confirming its strong ex- planatory power. This research contributes a validated, unified risk manage- ment framework that guides policymakers, platform operators, and auditors in addressing intertwined compliance and security risks. The findings support the advancement of safer, more trustworthy blockchain payment systems, fostering broader adoption and aligning with evolving regulatory landscapes.
Digital Based Estimation of Residential Property Losses from Liquefaction in West Jakarta
No full textLiquefaction poses a significant threat to urban areas with water-saturated alluvial soils, especially in seismically active zones like West Jakarta. Using Cone Penetration Test (CPT) data from 25 locations, soil susceptibility was evaluated through Cyclic Stress Ratio (CSR), Cyclic Resistance Ratio (CRR), and Magnitude Scaling Factor (MSF). Areas with safety factor (FS) values below 1 were identified as having high liquefaction potential. Residential buildings were categorized by floor area and assessed using the 2024 Government Property Sales Value (NJOP) to estimate potential financial loss. Structural damage percentages were determined using seismic intensity thresholds and empirical damage functions. Analysis showed that residential areas with moderate to loose soil conditions, particularly in the northern and western zones, are most vulnerable. The total estimated loss reached IDR 189.8 billion, with the highest concentration of damage in medium and large sized residential properties. These findings emphasize the critical need to integrate geotechnical parameters into spatial risk mapping and urban disaster mitigation planning. A digital loss estimation model combining soil characteristics, seismic parameters, and economic valuation provides a scalable approach for early warning systems and resilience-oriented urban planning. The study contributes to data-driven risk management strategies aligned with sustainable development objectives and adaptive infrastructure policies.Liquefaction poses a significant threat to urban areas with water-saturated alluvial soils, especially in seismically active zones like West Jakarta. Using Cone Penetration Test (CPT) data from 25 locations, soil susceptibility was evaluated through Cyclic Stress Ratio (CSR), Cyclic Resistance Ratio (CRR), and Magnitude Scaling Factor (MSF). Areas with safety factor (FS) values below 1 were identified as having high liquefaction potential. Residential buildings were categorized by floor area and assessed using the 2024 Government Property Sales Value (NJOP) to estimate potential financial loss. Structural damage percentages were determined using seismic intensity thresholds and empirical damage functions. Analysis showed that residential areas with moderate to loose soil conditions, particularly in the northern and western zones, are most vulnerable. The total estimated loss reached IDR 189.8 billion, with the highest concentration of damage in medium and large sized residential properties. These findings emphasize the critical need to integrate geotechnical parameters into spatial risk mapping and urban disaster mitigation planning. A digital loss estimation model combining soil characteristics, seismic parameters, and economic valuation provides a scalable approach for early warning systems and resilience-oriented urban planning. The study contributes to data-driven risk management strategies aligned with sustainable development objectives and adaptive infrastructure policies