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Natural resource management involves complex decision-making processes that often result in non-linear optimization problems. This study explores the application of genetic algorithms (GA) and particle swarm optimization (PSO) to manage resources like water and forest reserves more efficiently. We compare the effectiveness of these algorithms in achieving sustainable utilization while minimizing environmental impact. The results show that GA outperforms PSO in forest management scenarios, while PSO is more suitable for water resource distribution.

Big data has revolutionized the landscape of natural sciences by providing extensive datasets that enable deeper insights and more accurate predictions. However, effectively analyzing such vast and complex data requires optimized machine learning algorithms tailored to specific applications. This study focuses on enhancing the performance of machine learning models in big data analysis for applications in natural sciences. The research aims to identify key optimization techniques, including feature selection, hyperparameter tuning, and algorithm customization, to improve model accuracy and computational efficiency. A combination of supervised and unsupervised learning approaches was applied to real-world datasets in fields such as climate science, genomics, and ecology. The findings demonstrate significant improvements in predictive accuracy and processing speed, highlighting the potential of optimized machine learning techniques in solving complex problems in natural sciences. The implications of this research extend to more efficient resource utilization and improved decision-making in scientific exploration and environmental management.

Data transmission efficiency is crucial in wireless sensor networks (WSNs), where limited battery life and signal reliability are significant concerns. This research explores various machine learning algorithms aimed at optimizing data transmission in WSNs, focusing on reducing energy consumption and enhancing network stability. Simulation results indicate marked improvements in efficiency, making WSNs more viable for long-term deployment across diverse environments.

Research This investigate consumer sentiment analysis to halal products using social media data with utilise intelligence artificial intelligence (AI). With background behind increasing estimated market value of halal products reach USD 2.02 Trillion in 2024, understanding deep about opinion consumer become very important. Research This adopt approach quantitative, using secondary data from social media platforms such as Twitter, Instagram, and Facebook. Through Natural Language Processing techniques and algorithms learning machine, sentiment analysis is performed For identify pattern positive, negative and neutral in perception consumers. Research results show that 60% of the total 10,000 reviews had positive sentiment, with halal food products receiving the highest positive sentiment. Factors influencing consumer sentiment include product quality, price, and transparency of information. In addition, the study found that the use of AI in sentiment analysis provides advantages in efficiency and accuracy, and is able to capture nuances in consumer opinions that are not Possible done by manual analysis. From the analysis this, can concluded that the marketing strategy of halal products must focus on improving quality and providing clear information about halal certification. This study not only provides insight for halal industry players, but also enriches the literature related to AI, sentiment analysis, and sharia economics.

The rapid adoption of artificial intelligence (AI) in e-commerce is revolutionizing the business landscape. This study explores the rise of AI-powered e-commerce and its impact on business models, strategies, and market dynamics in 2024. Through a multiple case study approach, focusing on leading e-commerce companies such as Amazon, Alibaba, and Shopee, the research reveals that AI is fundamentally transforming the way businesses operate in the digital economy. AI enables enhanced personalization, operational efficiency, and improved customer experiences, driving the emergence of new business models and competitive advantages. However, the adoption of AI also creates significant challenges, including implications for the workforce, ethical concerns surrounding data privacy and algorithmic bias, and potential impacts on market dynamics and competition. The study highlights the need for a strategic and ethical approach to AI adoption, collaboration among stakeholders, and adaptive regulatory frameworks. It concludes with recommendations for businesses, policymakers, and future research to navigate the transformative impact of AI in e-commerce. The findings contribute to the literature on digital transformation and disruptive innovation, offering valuable insights for managers, practitioners, and researchers.

Data mining is a technique to dig up new information from a data warehouse, information is seen as very important and valuable because by mastering information it is easy to achieve a goal, this makes everyone compete to obtain information, as well as in trading businesses such as the Iblite Luxury store.  This store is located in Medan close to residents' houses, Sales transaction data will continue to grow, causing data storage to be even larger. Sales transaction data is only used as an archive without being properly utilized. Basically, a dataset has very useful information. Market basket analysis with a priori algorithm is one of the data mining methods that aims to find association patterns based on consumer shopping patterns, so that it can be known what items of goods are purchased in a At the same time, the results of this study found that the highest support and confidence values were Ysl and Chanel with a support value of 50% and confidence of 75%.

Currency classification is one of the challenges in the field of digital image processing and computer vision which can be applied in various applications, such as ATM machines, automatic money exchange machines, and mobile banking applications. This research aims to develop a classification model that is able to differentiate between 5000 thousand rupiah and 2000 thousand rupiah currency using the Convolutional Neural Network (CNN) algorithm. CNN was chosen because of its ability to recognize complex visual patterns and specific features from images. The dataset used in this research consists of 10 currency images of 5000 thousand rupiah and 10 images of 2000 thousand rupiah taken in lighting conditions and viewing angles vary and are classified into 2 classes. The data is then processed and normalized to increase model accuracy. The proposed CNN model, namely the Squential Model, consists of several convolution layers, pooling layers, and fully connected layers which are optimized to detect visual differences between the two types of currency.  

Market Basket Analysis (MBA) is an analytical technique used to identify relationships between items in purchasing transactions. This notebook uses retail transaction datasets and the Apriori algorithm to discover hidden associations and patterns that retailers can leverage in optimizing marketing strategies, store layouts, and product recommendations. Through initial data processing, data exploration, and application of the Apriori algorithm, this analysis succeeded in identifying various significant associations between items that are frequently purchased together. The results provide valuable insights for retailers to develop targeted promotions and improve customer shopping experiences, while emphasizing the importance of selecting the right parameters to obtain accurate and relevant results.

Modern technological advances cannot be separated from society, and significant developments in the use of digital devices have changed the media environment. Social media is now used for various purposes, including interaction, information, news and entertainment. Platforms like TikTok have become the main tool in digital marketing in Indonesia. TikTok offers creative short video formats, advanced algorithms, and features like “Hashtag Challenges” and “Branded Effects” that attract brands and companies. Students at SMK Negeri 1 Semarang face challenges in attracting an audience on TikTok due to lack of market research, creativity, and limited resources. This service aims to improve the understanding and skills of SMK N 1 Semarang students in creating quality video content on TikTok. The participatory method used involves discussion and direct practice. The evaluation results showed a significant increase in students' understanding of video content creation, with 55.8% of participants understanding the material presented. This education is expected to increase students' competence and creativity in digital marketing using TikTok.    

Bullying is a verbal or non-verbal bullying activity through the media (cyber bullying) or directly, carried out by a child or group of children against other children. Aggressive behavior such as bullying among teenagers results in problems such as anxiety. The problem at SMKN 2 Binjai in 2024 is the difficulty of identifying children's anxiety about bullying disorders because there is no strong reference as evidence for cases of bullying carried out by perpetrators against victims. So research is needed to cluster gender, type of bullying and children's anxiety levels, with many parties still not monitoring children's activities enough to see how big the impact is on children who experience bullying. The aim of this research is to determine gender, type of bullying and different levels of anxiety among children who experience bullying. Based on the results of grouping bullying cases using the K-Means algorithm, 3 clusters and 3 iterations were obtained, where cluster 1 contained 9 data. , cluster 2 has 4 data and cluster 3 has 7 data, so it can be concluded that bullying cases tend to occur in women who experience types of bullying in the form of cyber and psychological with a mild level of anxiety.

Hijaiyah letters are the alphabet used in Arabic and the Koran. Automatic recognition of hijaiyah letters has many benefits, especially in the fields of education and learning Arabic and the Koran. This research aims to classify hijaiyah letter recognition using image processing techniques and the Support Vector Machine (SVM) algorithm. We collected a dataset of images of 5 hijaiyah letters with a total of 400 images obtained from Google and also Iqro'.  The train:test ratio is 8:2. Experimental results show that the proposed approach can achieve high accuracy in recognizing hijaiyah letters with an accuracy rate of 99.16%.

Stunting is a condition of failure to thrive in children, in Indonesia it is still a serious problem with a fairly high prevalence. The government is trying to reduce stunting rates with various health programs, and early detection through routine measurements is very important. This research uses the Extreme Gradient Boosting (XGBoost) algorithm to classify stunting status in children under five years. This study uses a relevant dataset containing anthropometric information on children, such as gender, age, birth weight and length, current weight and length, and breastfeeding status. The research stages include dataset search, preprocessing, classification, evaluation, and implementation in a local web-based prediction program. The XGBoost algorithm was chosen because of its advantages in speed, scalability, and efficiency. After preprocessing and data sharing, the model was trained and tested, resulting in 86% accuracy, 89% precision, 95% recall, and 92% F1-score. Evaluation using the confusion matrix and classification report shows that this model is quite effective in classifying stunting status.  

In the digital era, universities play an important role in developing policies and technology to ensure students' academic integrity. This research aims to explore and analyze holistically the contribution and effectiveness of universities in maintaining academic integrity in the digital era. The research method used is a literature review with a semi-systematic review approach, which is then expanded with bibliometric analysis using the VOSViewer tool. Of the 115 papers identified in the bibliometric analysis with the keyword “college plagiarism”, it was found that the term “plagiarism” was often associated with “student”, “college”, “academic”, and “plagiarism”. Key relevant terms include “training”, “locus of control”, “rabin-karp”, “text mining”, “hash”, “levenshtein distance algorithm”, “similarity”, and “publication”. Universities should conduct training and strengthen students' locus of control to instill integrity values. The concept of “similarity” in plagiarism detection helps maintain the originality of academic work, while “rabin-karp” and “text mining” technologies are used to recognize plagiarized text and find suspicious patterns. The “hash” concept and “Levenshtein distance algorithm” are important in maintaining academic integrity, and “publication” ensures research meets academic integrity standards. Universities leaders are responsible for preventing plagiarism by implementing code of ethics and promoting awareness and understanding within the entire academic community.

This article examines a potential solution to the well-known Travelling Salesman Problem (TSP), which is classified as an NP-hard problem. We also provide a theoretical synopsis of several approaches that have been employed to tackle this problem. A prominent example of a combinatorial problem is the traveling salesman problem (TSP). To address the fundamental PSO algorithm's premature convergence issue and stagnation behavior on TSP, a scout characteristic-based PSO algorithm is suggested.We address Particle Swarm Optimization (PSO), a member of the evolutionary methods class, and outline the methodology for applying PSO to the TSP. Among population-based metaheuristic optimization methods, Particle Swarm Optimization (PSO) is one of the most widely used. Scientific domains such as engineering, chemistry, medicine, advanced physics, and humanities have all effectively employed PSO. Numerous theoretical and empirical results on the convergence and parameterization of PSO versions have been produced as a result of the method's extensive investigation since its introduction in 1995. Hundreds of PSO versions have been developed. It is well recognized that population size has a significant impact on the effectiveness of metaheuristics; nevertheless, no comprehensive research has been done on the appropriate selection of PSO swarm size to date.Through the application of this approach, we examine the effects of various control settings. The ideal solution and the quality of the solution are contrasted.    

The advent of Artificial Intelligence (AI) has heralded a transformative era in Computer Science, revolutionizing various facets of technology. This paper explores the profound impact of AI on the field of Computer Science, delving into its advancements, innovations, and the potential future trajectory of technology. The primary objective of this paper is to elucidate the significant role of AI in shaping the landscape of Computer Science. Through comprehensive research and analysis, it aims to provide insights into the evolution of AI, its applications, and the implications for future technological developments. This study employs a library research approach, gathering information from academic journals, conference proceedings, books, and reputable online sources. By synthesizing existing literature and scholarly works, it seeks to construct a comprehensive overview of the advancements and innovations in AI within the realm of Computer Science. The findings reveal a dynamic and rapidly evolving field driven by AI technologies. From machine learning algorithms to neural networks and deep learning models, AI has revolutionized data analysis, pattern recognition, and decision-making processes. Moreover, AI-driven applications such as natural language processing, computer vision, and robotics have reshaped various industries, including healthcare, finance, transportation, and manufacturing. However, alongside these advancements come ethical considerations, privacy concerns, and challenges related to algorithmic biases and societal implications. Looking ahead, the future of technology promises further integration of AI into various domains, leading to unprecedented opportunities and challenges in the realm of Computer Science

This research use the Naive Bayes algorithm to classification of user reviews of the Sky Childern Of The Light application from the Google Play Store. The Sky Childern Of The Light application is a popular online game, because it offers a unique and immersive playing experience. This method was chosen because of its simplicity, speed, ease of interpretation, and suitability for high-dimensional data. The advantages of Naive Bayes are the accuracy and efficiency of calculations, fast results and presentation. The data collected was 1500 data with a classification ratio of 8:2 with an accuracy value of 87% using the Naïve Bayes algorithm. This method is very good at analyzing the sentiment of the Sky Children Of The Light application.      

This research explores increasing the efficiency of network resource use through load balancing techniques. Load balancing distributes workload evenly among servers to optimize resource usage, maximize throughput, and minimize response time. We compare various load balancing algorithms, such as round-robin, least connections, and least response time, in various network scenarios. Experimental results show that proper load balancing techniques can reduce response time by up to 30% and increase resource usage efficiency by up to 40%. This study highlights the importance of selecting appropriate algorithms based on network traffic and workload characteristics. Implementing effective load balancing strategies can improve the quality of network services and ensure even distribution of workloads, providing practical guidance for network administrators to optimize network performance and efficiency.

. CV. Aneka Kaca is a company engaged in the supply of the best glass materials, because CV. Aneka Kaca is a direct distributor from Japan. In a company like CV. Aneka Kaca, employees are one of the important assets in helping to improve and stabilize the company. Employees are people who work in a company or agency to carry out an operational task and expect remuneration in the form of a commission or salary. Usually employees will also get bonuses from the company. Bonuses that will be received by employees are one of the ways companies use to increase the motivation of their employees' performance. But giving bonuses to CV. Aneka Kaca still uses the conventional method, so it can take a long time if only managers do the math. The determination of bonuses can sometimes be missed due to one or two problems that can have an impact on awarding bonuses. The Naive Bayes algorithm is a simple probabilistic classifier that computes a probabilistic set by summing the frequencies and value combinations from a given dataset.  

This study employs a linear regression algorithm to measure CO2 emissions from motor vehicles based on the type of fuel used—gasoline, premium, diesel, and ethanol—as well as the distance traveled per kilometer. With growing concerns about the environmental impact of vehicle emissions, this research aims to develop a predictive model to estimate CO2 emissions based on these variables. Despite the limited data used, the results indicate that the linear regression model has high accuracy in predicting CO2 emissions. This model can serve as an effective tool to mitigate the environmental impact of motor vehicles and assist policymakers in designing better emission mitigation strategies  

Computational thinking is the ability to solve problems and design systems using concepts and techniques generally associated with computers and computer programming. The aim of this research is to conduct a literature review, namely to determine the application of computational thinking in mathematics learning. The technique used is the Systematic Literature Review (SLR) strategy. SLR is a research method that aims to identify, study and interpret data in journals systematically with specified stages. The conclusion obtained from the research results is that the application of computational thinking in mathematics learning can be done using four foundations of computational thinking, namely: (1) Decomposition, namely the problem is divided into small parts, (2) Pattern recognition, namely the process of identifying patterns or sequences of problems, (3) Abstraction, namely consideration of the important parts of a problem, and (4) Algorithm, namely a series of instructions for solving the problem. Examples of its application include the use of interactive mathematics software, mathematical simulations, problem-based learning with technology, adaptive learning and the use of educational games.