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Cloud-based resource allocation and VM/container orchestration play a crucial role in ensuring performance, scalability, and energy efficiency in modern distributed computing environments. This study investigates the effectiveness of centralized and decentralized scheduling models combined with heuristic and optimization-based allocation strategies in container-based cloud infrastructures. A quantitative experimental approach was employed to evaluate system performance under varying workload intensities. Key evaluation metrics included response time, throughput, resource utilization, SLA violation rate, and energy consumption. The experimental results indicate that centralized scheduling mechanisms experience scalability limitations and increased latency under high workload conditions. Although optimization-based allocation improves performance within centralized architectures, coordination bottlenecks remain significant. In contrast, decentralized scheduling models demonstrate superior adaptability, reduced response time, and improved throughput due to distributed decision-making and reduced control overhead. The integration of intelligent optimization techniques further enhances resource utilization and energy efficiency, achieving the lowest SLA violation rates and highest system stability. Overall, the findings confirm that combining decentralized scheduling with optimization-driven resource allocation provides a more scalable and sustainable orchestration strategy for modern cloud environments. This approach is particularly suitable for dynamic, large-scale, and latency-sensitive applications in container-based and edge-integrated cloud systems.

Applying ergonomic principles in work tool design for manufacturing industries is a crucial factor in improving productivity while maintaining worker health. This research aims to analyze the effectiveness of adaptive work tool design models based on cognitive and physiological ergonomic principles, identify interaction patterns between workstation design and operational performance, and develop a conceptual framework for integrating ergonomic principles into production cycles. The research method adopts a cognitive-physiological approach with qualitative analysis of human-machine interactions, biomechanical simulations using digital human modeling, and muscle load measurements through electromyography. Implementation was conducted using a participatory ergonomics approach and IMU sensor-based real-time monitoring systems. Results show that using materials with controlled deformation capabilities (15-20%) in work tools reduces muscle work by up to 27%, while adaptive automation system integration improves assembly accuracy by 18%. Workstations with ergonomic adjustments increase assembly speed by an average of 12%, and low-cost ergonomic interventions effectively improve productivity by 11-15% in resource-limited environments. Longitudinal analysis reveals that evidence-based ergonomic investments yield a 230% ROI through increased productivity, reduced injury compensation costs, and decreased employee turnover. IMU-based posture monitoring systems integrated with adaptive feedback loops reduced musculoskeletal disorder incidents by up to 41%. In conclusion, ergonomic optimization based on cognitive-physiological principles creates synergy between production efficiency and worker well-being, making it an essential component in achieving sustainable productivity.

MSMEs are businesses owned by the community, both individuals and entities, that meet the criteria for being a business. Indonesia's success in increasing the scale of the national economy cannot be separated from the role of MSME behavior. MSMEs in Nganjuk Regency have their own attraction which is supported by natural potential, namely the large number of shallot commodities. As time goes by, many people in Nganjuk Regency make a living from processing shallots, so there are many similar products from MSMEs. “Bawang Kita” MSMEs face several obstacles, namely seasonal market demand which is always changing and threats from similar MSMEs which create a competitive market situation both in the national and international markets. Along with fluctuating sales and increasingly fierce competition, “Bawang Kita” MSMEs need to pay attention to marketing mix strategies that are in line with the desires of domestic and foreign consumers. This can also help companies achieve superiority over the competition. This research discusses the effectiveness of implementing international marketing mix strategies in “Bawang Kita” MSMEs which is supported by SWOT analysis. This research uses descriptive research with a qualitative approach by collecting data through in-depth interviews with MSME Owner, Resellers, and Consumers.  Results of this research that the implementation of the international marketing mix strategy in “Bawang Kita” MSMEs is effective in terms of every aspect of the marketing mix which is supported by data on sales, production and distribution as well as the ability to purchase new machines. However, in the SWOT analysis carried out by researchers, there are still weaknesses in the implementation of the marketing mix carried out by MSME “Bawang Kita”. Thus, the implementation of the marketing mix strategy in “Bawang Kita” MSMEs must be further improved

Accurate image segmentation is a pivotal process in medical imaging, essential for supporting diagnosis, treatment planning, and monitoring disease progression. This study evaluates the effectiveness of machine learning algorithms, including U-Net, Fully Convolutional Networks (FCNs), and Mask R-CNN, in achieving high-precision segmentation of medical images. Experimental results demonstrate that these models significantly enhance segmentation accuracy, enabling more precise diagnostic outcomes in clinical settings and advancing the development of automated medical imaging technologies.

Background: Public opinion analysis has become increasingly important in the digital era, where social media platforms generate large-scale textual data reflecting public perceptions toward environmental policies. Advances in Natural language processing (NLP) and machine learning enable systematic sentiment classification to support data-driven decision-making. Objective: This study aims to evaluate the effectiveness of several sentiment classification models in analyzing Indonesian-language social media data related to environmental policies. Method: The research employed a text mining pipeline including data crawling, preprocessing (case folding, tokenization, stopword removal, and stemming), and vectorization using TF-IDF. Three classification models Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) were trained and evaluated using accuracy and F1-score metrics. Results: Experimental findings indicate that LSTM achieved the highest performance with 91.7% accuracy and 91.2% F1-score, outperforming SVM (88.5%) and Logistic Regression (84.2%). Sentiment distribution analysis shows that public opinion is dominated by positive sentiment (47.5%), followed by neutral (32.0%) and negative (20.5%). Overall: The results demonstrate that deep learning-based models provide more robust contextual understanding and more reliable sentiment mapping for environmental policy analysis.

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.

Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal with non-linear problems. At the same time, hyperparameter tuning is done through Random Grid Search to find the best combination of parameters. Where the best parameter settings are C = 10, Gamma = 10, Probability = True. Test results show that the tuned SVM improves accuracy, precision, specificity, and F1 score significantly. However, there was a slight decrease in recall, namely 0.02. Even though recall is one of the most important measuring tools in disease classification, especially in imbalanced datasets, specificity also plays a vital role in avoiding misidentifying negative cases. Without hyperparameter tuning, the specificity results are so poor that considering both becomes very important. Overall, the best performance obtained by the proposed method is 0.99 for accuracy, 1.00 for precision, 0.98 for recall, 0.99 for f1-score, and 1.00 for specificity. This research confirms the potential of tuned SVMs in addressing complex data classification challenges and offers important insights for medical diagnostic applications.

The rapid spread of infectious diseases remains a major global health threat, and early detection is vital to minimize their impact. This research investigates the role of predictive modeling using big data in the early detection of infectious disease outbreaks. The primary objective of this study is to assess the effectiveness of big data systems in forecasting potential outbreaks and the implications of these forecasts for public health systems. The study employs machine learning-based predictive models to process large health datasets, including electronic health records, sensor data, and social media information. The results demonstrate that the predictive model achieved an accuracy rate of 87%, significantly surpassing traditional methods in terms of early detection. By integrating various data sources such as medical records, sensor networks, and real-time digital traces, the system is capable of providing more accurate, timely predictions, which can greatly improve the ability of public health authorities to respond effectively to emerging health threats. Furthermore, the application of big data in public health not only improves the speed of response but also enhances the allocation of resources, allowing for more targeted and efficient interventions. Despite these successes, challenges remain, particularly in relation to data quality, privacy, and regulatory issues, which could hinder the broader implementation of such systems. Thus, collaboration between government agencies, healthcare institutions, and technology developers is essential to overcome these obstacles and ensure the sustainable integration of big data into public health infrastructures. This research highlights the significant potential of big data to transform public health responses, offering valuable insights for future epidemic management strategies.  

The rapidly evolving landscape of cryptanalysis necessitates an urgent and detailed exploration of the high-degree non-linear functions that govern the relationships between plaintext, key, and encrypted text. Historically, the complexity of these functions has posed formidable challenges to cryptanalysis. However, the advent of deep learning, supported by advanced computational resources, has revolutionized the potential for analyzing encrypted data in its raw form. This is a crucial development, given that the core principle of cryptosystem design is to eliminate discernible patterns, thereby necessitating the analysis of unprocessed encrypted data. Despite its critical importance, the integration of machine learning, and specifically deep learning, into cryptanalysis has been relatively unexplored. Deep learning algorithms stand out from traditional machine learning approaches by directly processing raw data, thus eliminating the need for predefined feature selection or extraction. This research underscores the transformative role of neural networks in aiding cryptanalysts in pinpointing vulnerabilities in ciphers by training these networks with data that accentuates inherent weaknesses alongside corresponding encryption keys. Our study represents an investigation into the feasibility and effectiveness of employing machine learning, deep learning, and innovative random optimization techniques in cryptanalysis. Furthermore, it provides a comprehensive overview of the state-of-the-art advancements in this field over the past few years. The findings of this research are not only pivotal for the field of cryptanalysis but also hold significant implications for the broader realm of data security.

The rapid growth of Internet of Things (IoT) and edge computing technologies has introduced new security challenges due to the distributed, heterogeneous, and dynamic nature of these environments. Conventional static security mechanisms, such as rulebased authentication and fixed trust models, are often inadequate for addressing evolving threats and abnormal behaviors in largescale IoT systems. To overcome these limitations, this study proposes a machine learningbased trust evaluation framework for enhancing security in distributed IoT environments. The proposed approach dynamically assesses the trustworthiness of IoT nodes by analyzing behavioral and interactionbased features collected at the edge layer. Machine learning models are trained to classify nodes into trusted and malicious categories and continuously update trust values in response to changing network conditions. Based on the predicted trust levels, adaptive security decisions are enforced to allow or restrict node participation in data sharing and computation processes. A quantitative experimental evaluation is conducted using simulated distributed IoT scenarios that include both normal and malicious behaviors. The performance of the proposed framework is evaluated using standard metrics such as accuracy, precision, recall, F1score, and detection effectiveness, and is compared against conventional static trust and rulebased security mechanisms. The results demonstrate that the proposed machine learningbased trust evaluation approach achieves significantly higher detection accuracy and robustness while maintaining low computational overhead. Overall, the findings confirm that integrating machine learning into trust management provides an effective and scalable solution for securing distributed IoT systems under dynamic and adversarial conditions.

PT Angkasa Pura I provided operational support to better the passenger time with self check-in. Self check-in is a service system that allows passengers for self check to reduce queues Check-In Counter. The study aims to understand significantly the impact and influence of the effectiveness of the use of the self check-in machine on the passenger satisfaction of the citilink airline at Yogyakarta International Airport. The research is a quantitative using primary data of the survey (questionnaire) and observation in its collection. The sample in this research uses a nonsampling technique type adhesive with the slovin formula gets 100 respondents. Data analysis techniques for analyzing this research are instrument tests, classic assumptions test, simple linear regression analysis. The results of the research suggest that the effectiveness of the use of the self check-in engine has significantly affected the passenger satisfaction greater than t table (21.756 > 1.660), which means h0 was rejected and ha received. The effectiveness of the use of the self check-in machine to affect passenger complacency by 82.8% while the remaining 172% are affected by variables or other factors not used in the research.

In this research, a machine learning approach, especially a decision tree model, is implemented to improve the analysis and visualization of weighbridge data in Indonesia. The evaluation results show that the decision tree model provides better insight in predicting the carrying capacity, dimensions and loading procedures of vehicles. The advantage of this model lies in its combination of low Mean Squared Error (MSE) and high R-squared, indicating its effectiveness in capturing data variance and providing accurate predictions. The use of decision tree models can be a valuable tool in improving the visualization of bridge weighing data, allowing users to gain additional insights and understand the complex dynamics within the data. In addition, the model's adaptability to various types of data makes it a versatile analysis tool. The positive implications of using this model open up opportunities to understand more deeply the logic of predictions and make more informed decisions. As a suggestion, increasing the number and quality of weighing equipment, wider application of information and communication technology, human resource training, and cross-sector collaboration can further strengthen weighbridge management in Indonesia.

In this era, technology is developing very rapidly in various sectors ranging from industry, health, education, retail, and various other sectors. The development of technology is linear with human needs that must be met quickly, easily, and practically. A sector that can fulfill human needs is the trade sector. Currently, the trade sector is one of the sectors that is highly developed along with the trend of online shopping. Although online shopping is currently very popular, it does not mean that offline shopping is necessarily abandoned. Conventional payment systems usually require customers to queue at the cashier to make payments after choosing grocery items. Therefore, there is a need for technological innovation that can overcome this. The solution to overcome these problems is by planning to create a Smart Shopping Scanner machine by considering lean project management. Based on the results of the research that has been done, the application of the Lean Project Management method to the Smart Shopping Scanner project planning at PT ABC has resulted in significant improvements. Reduction of project completion time by 33.33%, reduction of component inventory value by 40%, and improvement of team communication by 66.67% show the effectiveness of this method in overcoming waste and increasing efficiency.

The pellet molding machine has high efficiency to make it easier to process animal feed pellets. The pellet processing process requires a series of activities such as grinding, mixing, pelleting and drying. Therefore, research was carried out to identify the effect of convenience on the level of effectiveness of using an animal feed pellet printing machine. The method used in this research is a quantitative method by distributing questionnaires via the Google Form platform. Validity tests, reliability tests and simple regression analysis are used to analyze the data obtained from filling out the questionnaire

The advancement of Industry 4.0 demands production systems to operate more efficiently, adaptively, and securely in facing global challenges. One promising technology that addresses these needs is the Digital Twin (DT), a digital representation of physical systems that enables integration between the real and virtual environments. Through DT, production processes can be modeled, monitored, and tested in real time, allowing for evaluation and optimization before implementation in actual systems. This study aims to explore the effectiveness of DT in modeling automated industrial systems, particularly in relation to improving production efficiency, quality control, energy savings, and operational safety. The research employed an experimental approach based on simulation within a robotic production line consisting of machines, sensors, actuators, and conveyors. The research stages included identifying system components and workflows, developing a DT model that integrates physical and virtual layers with Internet of Things–based data connectivity, and conducting simulations representing diverse operational scenarios. The findings indicate that DT implementation enhances operational efficiency, reduces production errors, and optimizes energy utilization. Furthermore, DT proves effective in strengthening safety aspects by enabling early detection of potential disruptions and providing preventive recommendations before significant impacts occur. Compared to conventional simulations, DT offers a more realistic, adaptive, and relevant approach to the needs of modern industry. The implications of this study highlight DT’s strong potential to become a new standard in the development and control of automation-based production systems, driving the creation of smarter, more efficient, and sustainable industries.

Increasingly, more and more changes and advances are occurring, especially in the technology sector which requires users to understand and understand the meaning of an algorithm command. Basically, algorithms are steps used to solve problems. In the world of computing, programming algorithms are packaged by collaborating machine language with human desires. This article aims to compare the stability and effectiveness of machine languages that are often used by programmers. This article uses a systematic literature review method with sources from several studies and papers that have been conducted. The results of this study show that there are advantages and disadvantages of python and C++ respectively.

Transportation of oil palm Fresh Fruit Bunches (FFB) from the plantation to the harvest collection point (HCP) must be done as quickly as possible. This is due to an increase in free fatty acids after the FFB is harvested. Some of the obstacles found were the FFB transportation equipment which was not yet capable of reaching the fruit in the garden easily and the transportation time which was quite long due to the uneven road conditions. With technological advances and the rapid development of innovation, the discovery of track model wheelbarrows that use a driving engine means that further studies are needed to determine the effectiveness of these transport machines. In this research, we will compare wheelbarrows without engines and wheelbarrows with track models, there are 3 (three) treatments given, namely on flat roads, bumpy roads and damaged roads. Researchers search for and collect data on transport capacity, transport load, transport speed and transport costs. The results of this research show that carrying capacity and road conditions have a effect on travel time and speed. This is due to the ability/power of the machine which is quite large and stable in transporting fruit. and, other results from this research show that the use of motorized wheelbarrows can reduce transportation costs because the transportation speed increases so that the travel time is shorter.

Rangawak is a wood-working workshop in Barulak, Tanah Datar that produces hand-processed products without using massive manufacturing machines. Rangawak has been running since 2019, by utilizing the surrounding wood resources which are designed and processed to produce valuable furniture. However, this is still not enough to make Rangawak a sustainable brand. Rangawak needs more than just a quality product, but also a strong brand image, so it is easily recognized by potential consumers. Branding steps are deemed appropriate to overcome this problem using the design thinking method which focuses on empathy, defining problem, ideation and refining solutions. This method includes experimentation, development of ideas in visual form and physical implementation based on existing problems. The branding process results in the design of ideal branding elements, compiled into a brand book so that it becomes a reference for acting both internally and externally. These elements are in the form of values held firmly by the brand, brand positioning in the market, calm explanation of brand naming, brand personality which becomes a reference for how the brand interacts (in the form of tone of voice, prohibited and recommended terms, pronunciation and choice of words in copywriting), as well as visual identity with guidelines and rules for its application in various supporting media such as websites, catalog books, posters, member cards, packaging and Instagram accounts selected based on indicators of the media's effectiveness and efficiency in the brand activation process.

The world of aviation in this modern era is increasingly developing. This is evidenced by the self-check-in facility provided by the company which greatly facilitates flight service users at Sultan Aji Muhammad Sulaiman Sepinggan Airport Balikpapan where this facility can not only reduce interaction between humans and reduce the intensity of the spread of the Covid 19 virus at that time, this facility can also maximize the effectiveness of passenger time, so that there are no long queues in the use of manual check in. This study aims to determine the Use of Self Check In Technology Affects the Effectiveness of Flight Service Users at Sultan Aji Muhammad Sulaiman Sepinggan Airport Balikpapan and How Much Influence the Use of Self Check In Technology Affects the Effectiveness of Flight Service Users at Sultan Aji Airport Muhammad Sulaiman Sepinggan Balikpapan. This research uses quantitative research methods with data sources in the form of primary data and secondary data. The population in this study is service users who have used the Self Check-In System and the sample in this study is 95 respondents from 2042 existing populations. Data analysis techniques consist of research instruments, namely validity tests and reliability tests and hypothesis tests consisting of simple linear analysis tests, t tests, and determination coefficients. The result of this study is that the use of Self Check In Technology affects the effectiveness of flight service users at Sultan Aji Muhammad Sulaiman Sepinggan Airport Balikpapan has a partial effect in a negative and significant direction. The magnitude of the correlation / relationship value (R) is 0.461. From this output, a coefficient of determination (R Square) of 0.212 was obtained which explained that the effect of the independent variable of self-check-in machine technology facilities on the effectiveness of aviation service users was 21.2.% The remaining 78.8% was influenced by other variables not explained in this study.