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Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.

Blockchain and cryptocurrencies have changed the way we transact and interact in the digital age. However, the rapid advancement of these technologies has resulted in major environmental impacts. Efficient implementation of blockchain requires the use of large amounts of energy and computing power, with consensus algorithms as the foundation. The purpose of this study is to investigate the environmental implications of blockchain and cryptocurrency implementation, as well as initiatives to mitigate these issues. The type of research is a library study (library research) using a qualitative method, namely by combining, collecting information or previous scientific papers on relevant topics. Along with the growing popularity of cryptocurrencies, the continuous mining process often results in large energy consumption and carbon emissions, sparking concerns about their long-term viability and environmental impact. Based on the results of previous research and research sources, the author found a solution to the problem of high energy consumption from the use of blockchain, namely: Proof of Stake (PoS), Proof of Authority (PoA), Sidechains and Layer-2 Solutions, Hardware Optimization, Implementation of Consensus Algorithms Based on RUST.

This study investigates optimal retrofit strategies for buildings in tropical climates, focusing on energy efficiency, thermal comfort, and indoor air quality (IAQ). Given the unique challenges of high temperatures, humidity, and energy demands in tropical regions, traditional retrofitting methods often fall short of achieving a balance between these critical factors. By employing a multi-objective optimization approach, this research identifies the most effective combination of retrofit solutions, including insulation, natural ventilation, and high-performance window treatments. The results show that the proposed retrofit strategy significantly reduces cooling energy consumption, while maintaining or improving occupant comfort and IAQ. Insulation, particularly external insulation, proved to be the most effective in reducing heat transfer, while natural ventilation strategies and advanced materials further contributed to improving thermal regulation. The study demonstrates that integrating passive and active retrofit measures, tailored specifically to tropical climates, leads to optimal building performance. The multi-objective optimization algorithm (NSGA-II) allowed for the generation of Pareto-optimal solutions, offering a set of trade-offs between energy efficiency, thermal comfort, and IAQ. These findings are particularly relevant for policymakers and building professionals seeking sustainable retrofit solutions in tropical regions. The study also highlights the importance of integrating energy efficiency and IAQ considerations in retrofit strategies to avoid compromising occupant health. Further research is recommended to explore the integration of advanced materials, such as phase change materials (PCMs), and to enhance IAQ management in retrofitted buildings, ensuring long-term sustainability and occupant well-being in tropical environments.

Blockchain and cryptocurrencies have changed the way we transact and interact in the digital age. However, the rapid advancement of these technologies has resulted in major environmental impacts. Efficient implementation of blockchain requires the use of large amounts of energy and computing power, with consensus algorithms as the foundation. The purpose of this study is to investigate the environmental implications of blockchain and cryptocurrency implementation, as well as initiatives to mitigate these issues. The type of research is a library study (library research) using a qualitative method, namely by combining, collecting information or previous scientific papers on relevant topics. Along with the growing popularity of cryptocurrencies, the continuous mining process often results in large energy consumption and carbon emissions, sparking concerns about their long-term viability and environmental impact. Based on the results of previous research and research sources, the author found a solution to the problem of high energy consumption from the use of blockchain, namely: Proof of Stake (PoS), Proof of Authority (PoA), Sidechains and Layer-2 Solutions, Hardware Optimization, Implementation of Consensus Algorithms Based on RUST.

The development of information technology has encouraged various educational institutions to switch from manual systems to more efficient and integrated digital systems. One important activity in school management is the process of scheduling teachers' lessons, which often takes a long time and has the potential to cause scheduling conflicts when done manually. Common problems that often arise include conflicts between teachers' teaching hours, incompatibility of classroom availability, and difficulties in adjusting schedules to teachers' preferences. Therefore, this study aims to design and develop a web-based teacher scheduling information system by applying the Greedy algorithm method as a solution to optimize automatic schedule compilation. This system was developed using the PHP programming language and MySQL database with a Waterfall development model approach. The implementation results show that the system is capable of producing teaching schedules quickly, accurately, and with minimal time conflicts. Thus, the application of the Greedy algorithm has proven to be effective in improving efficiency, accuracy, and flexibility in managing teacher schedules in a school environment.

The rapid advancement of modern computing has driven extensive research on numerical algorithms for solving large-scale systems of linear equations. Classical methods such as LU decomposition, Jacobi, and Gauss–Seidel have been revisited and optimized to leverage parallel architectures, GPUs, and even quantum platforms. Recent studies demonstrate that optimized algorithms can reduce computation time by more than 50% while maintaining high accuracy in solving high-dimensional problems. LU decomposition, particularly in its parallel and GPU-based implementations, has shown superior performance in batch processing and industrial-scale simulations. Meanwhile, iterative methods such as Jacobi and Gauss–Seidel remain relevant due to their flexibility in numerical modeling, with further developments for block matrix systems, finite element applications, and FPGA architectures. The integration of these enhanced algorithms is not only beneficial for the advancement of scientific software development but also supports practical applications in engineering simulations, large-scale data optimization, and machine learning. Therefore, an integrative review of modern numerical algorithm developments is crucial in bridging the gap between industrial demands and research progress in scientific computing.

This research aims to address the issue of uneven bandwidth distribution in large organizational networks by implementing Quality of Service (QoS) using FIFO and the Hierarchical Token Bucket (HTB) algorithm on Mikrotik routers. Uneven bandwidth distribution can disrupt productivity and operational efficiency. This study creates a fair and efficient traffic management system, allowing bandwidth allocation according to user needs. The methodology involves detailed configuration of Mikrotik RouterOS to optimize QoS with adjusted HTB settings. Testing was conducted using IPerf3 to measure bandwidth variations received by clients in different conditions, including scenarios with two and three active clients. The results indicate that the HTB method provides more stable and consistent bandwidth distribution compared to FIFO. In the two-active client scenario, the unused bandwidth by the third client is allocated to higher priority clients, demonstrating HTB's effectiveness in managing traffic priorities. This research is expected to enhance user satisfaction by providing a network that is both stable and responsive to the needs of various operational applications, and contribute significantly to the development of best practices for bandwidth management in complex organizational environments.

This journal focused on the juridical analysis of legal protection for the victims of Artificial Intelligence misuse based on Indonesia’s Law. In this context, AI misuse refers to the use of AI technology that harms an individual or group, physically, mentally, and financially. Firstly, this journal explored how Indonesia’s law protects the victims of AI misuse. Although Indonesia has developed some regulations and policies, the legal protection of the victims of AI misuse still has become a challenge. The existing laws did not cover the specific aspects of AI misuse, including data privacy, algorithm discrimination, and other social impacts. Secondly, this journal discussed the challenges and obstacles in implementing legal protection for the victims of AI misuse in Indonesia. Some vital challenges included the lack of a general understanding of AI, specific laws for AI, and the gaps between the technology advancement and the existing law. Moreover, another challenge came from the lack of an international law framework that regulates the use of AI. This journal suggested the need for regulation improvement, public education about AI, and international cooperation in regulating AI. Besides, further research is needed to understand the bad impact of AI misuse and how the law can be more effective in protecting the victims.

This research uses a quantitative approach with the help of Python to solve infimum and supremum problems involving measurements, calculations and data analysis to draw conclusions. This research process consists of several stages: problem identification, modeling a set of numbers or functions, and implementing algorithms in Python to calculate the infimum and supremum. The calculation results are compared with manual analytical solutions to ensure accuracy and efficient use of Python. Sets as a basic concept in programming allow organizing data and logical operations more efficiently. These findings show that Python is not only effective in calculating supremum and infimum, but also speeds up the solution process compared to manual methods. The results of the program execution show that the analyzed set has an infimum fan supremum which is in accordance with the theory, where set 1 has an infimum of 2, set 2 has a supremum of 5, and set 3 has an infimum of 1 and a supremum of 4.

The detection of computer network attacks is becoming increasingly important as the complexity and frequency of cyber-attacks threatening information systems and network infrastructure continue to rise. These attacks may lead to severe consequences, including data breaches, service disruptions, and financial losses. To address these challenges, artificial intelligence techniques have become a major focus in the development of more effective, adaptive, and reliable intrusion detection systems. Among various classification algorithms, the C4.5 decision tree has demonstrated strong performance due to its simplicity, interpretability, and high classification accuracy. This study aims to apply the C4.5 algorithm for network attack detection using a comprehensive dataset that includes multiple categories of attacks and normal network activities. The proposed methodology consists of several stages, including data preprocessing, feature selection, decision tree model construction, and performance evaluation using standard metrics such as accuracy, precision, recall, and F1-score. Data preprocessing is performed to handle missing values, normalize data, and reduce noise, thereby improving the overall quality of the dataset and enhancing classification results. The experimental results indicate that the C4.5 decision tree algorithm effectively classifies network traffic into attack and normal categories with a satisfactory level of accuracy. The model successfully identifies attack-related patterns and highlights significant features that influence detection performance. Further analysis reveals that appropriate feature selection and parameter tuning significantly contribute to improving model reliability and robustness. This research provides a valuable contribution to the development of efficient, accurate, and practical network intrusion detection systems. The proposed approach is expected to strengthen information security frameworks and support proactive defense strategies against increasingly sophisticated cyber threats, thereby enhancing the protection of critical network infrastructures.

In this paper, Spline techniques have become prominent lately due to their effectiveness in addressing singular perturbation boundary value problems. These issues, defined by a boundary layer or a minor parameter associated with a derivative term, pose difficulties due to swift fluctuations in solutions adjacent to the boundary. Solutions to singularly perturbed involving both positive and negative changes in a spatial variable are shown in this review article. The methods presented here are algorithmic in nature. The singular perturbation that arises in neural activity simulation and the approaches proposed by numerous investigators between 2004 and 2024 are the only ones covered in this review. A variety of types of singularly perturbed were reviewed, including ordinary delay differential . Discovering what numerical and analytical strategies have been created in the past decade to address these types of issues is the primary objective of this review. Its secondary objective is to encourage academics to come up with novel, strong approaches to resolving related issues.

Image classification is widely used in everyday life such as in car steering, closed-circuit television (CCTV), traffic cameras, etc. The implementation of image classification can be done using several methods, including neural network and support vector machine models. The neural network method is able to find the right weights that allow the network to show the desired behaviour while the support vector machine method has many dimensions and can overcome linear and non-linear data. In this research, feature extraction was carried out using VGG16 to increase accuracy. This research aims to find out how to implement the neural network and SVM algorithms to classify images and determine the results of analyzing the performance of the two methods. The data used in this study is secondary data consisting of 10 types of large wild cats with a total of 2339 training image datasets and 50 testing image datasets. The research stages consist of data augmentation, model design, model training, and model evaluation. Classification with the neural network model produced an accuracy of 96% and the support vector machine model produced an accuracy of 96%, which means that in a consistent training environment, the two models have the same performance.

This research studies the application of the nth order Runge-kutta method as a numerical solution to ordinary differential equations. This method was chosen because it is able to provide high accuracy and flexibility in various PDB problems. We implement the nth-order Runge-Kutta algorithm in MATLAB and compare with other numerical methods, such as Euler's method. The results show that the nth order Runge-Kutta method is able to produce more accurate solutions, especially for nonlinear systems. This research makes a significant contribution to the development of numerical solutions for PDB and shows the potential of MATLAB as an effective tool for numerical simulation. Sensitivity analyzes of parameters and time steps were also performed to understand the impact of variations on stability and convergence.

Admission of new students to an educational institution is an activity that is always carried out every new academic year, where prospective new students always increase from year to year (Muwardah and Pramunendar, 2015). Admission of students can be held from elementary to middle school, from middle school to high school / vocational school. The focus of this research is the registration of new students at SMK. As is known, SMK is a Vocational High School or abbreviated as (SMK) and where there are many majors provided which ultimately makes prospective new students confused about which major is right for them because will take a long time.. Based on C4.5 as a Classification Algorithm: C4.5 is a popular algorithm for building decision trees. It works by dividing a dataset into smaller subsets based on attribute values, thus forming an easy-to-understand tree structure. Classification results using decision trees provide a clear visualization of the decision-making process and the variables that contribute to student choices.

The growth of the internet has influenced the tourism industry because the internet makes it easier for individuals to obtain reviews about places to visit and because the internet is a tool used by tourist site managers to assess the quality of their offerings. The increase in the number of tourists of almost two million in just three years in West Sumba is proof of this influence. Social media is a tool that people use to interact with each other online; some people have multiple accounts on platforms such as Instagram, WhatsApp, Facebook, Telegram, Twitter, and so on. Tourists can receive recommendations for tourist attractions based on price and type of trip desired through a tourist attraction recommendation system that uses the KNN algorithm. Three factors were used in this research: activity, type of tourism, and type of price. An accuracy of 63.16% is found in the test results using the KNN algorithm and the Rapid Miner application with a K value of 5. The analysis results show that the K-Nearest Neighbor (K-NN) approach can be used as a guideline for recommending tourist destinations to visitors in West Sumba.

Along with the development of the times, the use of information technology using computers as its media has increased rapidly. Indonesia is currently starting to adopt the concept of society 5.0 or what is known as Society 5.0, where this kind of activity focuses on community settings that are oriented towards IT-based human activities (Wijayanti et al., 2022). The application of the concept of "society 5.0" has brought many technological advances in various fields of life that are connected to the internet, especially in Indonesia. The base64 technique produces values ​​during text encoding that are difficult for laypeople to understand. This encryption program has the ability to convert plain text to cipher text and reverse cipher text after it is received. Several recommendations for the development of better applications in further research can be made based on the test findings of this study and the conclusions mentioned above. These include the following: The Vigenere Chipper application can only encrypt text messages    

Customer churn prediction models have become an important tool in the telecommunications industry to reduce churn rates and improve customer retention. This research focuses on building an accurate customer churn prediction model using machine learning algorithms for TELCO Company. By applying diverse feature engineering techniques and prediction models such as RandomForestClassifier, DecisionTreeClassifier, and XGBoost, this study showcases a significant improvement in prediction accuracy compared to previously implemented rule-based methods. The findings of this research allow TELCO Company to identify high-risk customers more effectively and implement targeted retention strategies. Results show that the resulting model can identify customers at risk of churn more effectively, enabling more targeted retention actions..

As cloud computing advances, organizations' IT infrastructure and application deployment processes are moving to the cloud because cloud computing provides everything as a service over the Internet. The performance of a cloud-based application is based on proper datacenter selection and workload distribution within the selected datacenter. Service broker policies are used for suitable datacenter selection, and load balancing algorithms(LBA) are applied to distribute workloads. This paper is to evaluate the effect of a proposed service broker policy (PSBR) on the performance of cloud-based applications with LBA. To achieve the objective, the behavior of the TikTok application was modeled using the worldwide users’ statistics on the cloud simulation framework, namely CloudAnalyst. As a result, the average response time and data center processing time are measured. Next, the PSBR provides better results than the existing service proximity-based policy. This paper supports cloud service providers' benefits, from coordination between data center configuration, data center selection, and workload distribution to cloud users' identification of the appropriate procedures for their organization or application. PSBR with Active Monitoring had the best average response time of 75.1 ms, while SPR consistently exhibited higher average times across all algorithms, with the highest being 84.5 ms for Round Robin. Under the PSBR policy, Throttled had the lowest average processing time (4.67), while Round Robin had the highest (5.72). Similarly, under the SPR policy, Throttled maintained its efficiency with the lowest average (4.8), while Round Robin showed the highest (5.79).

It has been demonstrated that using computers greatly improves our ability to perform our duties. Information services are vital because, while employee performance may still be predicted manually, the process takes a long time. Data mining technologies, on the other hand, make it easier to anticipate employee success for loyal employees. Employee performance evaluation criteria are necessary in order to increase the accuracy of the assessment results, as Toko Merpati Simpang's employee performance assessments cannot be conducted carelessly. Employee performance has to be analyzed and categorized because up until now, manual employee performance evaluations have only used subjective criteria. The C4.5 Algorithm data mining approach is used in this evaluation of employee performance. The degree of accuracy will be ascertained by comparing these two approaches. Positive and negative emotions are the two categories of sentiment. The aim of this study is to ascertain the degree of accuracy of the comparison between the two tested techniques and to offer information on the quality of one of Toko Merpati Simpang's employee performance assessments using visitor sentiment. The test results will be evaluated using the Rapidminer tool to demonstrate the degree of accuracy for both testing approaches.   Keywords: , 

Social assistance is an important aspect of government and non-government programs that can help on a large scale for the community so that the impact is to lighten life in the short term, but social assistance has several criteria such as income, social conditions, family status and the impact of the economic situation. . Knowing the criteria for social assistance is done by applying data mining to social assistance using the Naive Bayes algorithm procedure which produces accuracy calculations from 100 testing data, obtained good values, namely accuracy of 95.00%, precision of 92.31%, and recall of 97.95%.