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This study aims to apply the K-Nearest Neighbors (KNN) algorithm to predict stunting status in young children based on height and weight data. Stunting is a growth failure condition caused by chronic malnutrition that negatively impacts children's physical and mental development. The dataset includes height, weight, and stunting status of children. The results show that the KNN model with k=3 achieved 100% accuracy on the test data. Evaluation using the confusion matrix and classification report indicates perfect precision, recall, and F1-score for each class. Data normalization with StandardScaler improved the model's performance by ensuring all features are on the same scale. The KNN algorithm proves to be a simple yet effective method for predicting stunting, demonstrating significant potential for early detection and health intervention in children. This study recommends using a larger and more diverse dataset, as well as incorporating additional relevant features to enhance model accuracy. Implementing the model in a web or mobile application is also suggested to assist healthcare professionals in the field.

PT Astra International BMW Semarang operates in the automotive sector, focusing on sales, aftersales, and spare parts for BMW cars. The availability of spare parts is crucial for customer satisfaction, as stock shortages can lead to disappointment. Using data from 52,162 spare parts sales transactions from January 2019 to June 2023, the study applies data mining techniques with the a priori and eclat algorithms to identify consumer purchasing patterns and prevent stock shortages. The research aims to provide recommendations for prioritizing spare parts stock. Utilizing the CRISP-DM methodology and R programming, the study found that the highest confidence in purchasing patterns occurs with a combination of three itemsets: if a customer buys an oil filter set (B11.42.8.593.186) and washer cleaner (B83.12.5.A1A.683), they will also buy BMW engine oil (Z99000000333) with 100% confidence. These findings can help PT Astra International BMW Semarang manage spare parts stock more effectively.

The main objectives are to identify emotion patterns hidden in K-Pop music based on audio features extracted from the Spotify API and to build an emotion classification model that can predict the emotions of K-Pop songs.In this approach, the K-Means algorithm is used to cluster K-Pop songs based on audio features such as energy, valence, tempo, danceability, and speechiness. The clustering results reveal several main groups that represent variations in musical characteristics and emotions. Next, the C4.5 algorithm was used to build an emotion classification model based on the clustering results. The C4.5 model showed high performance with accuracy reaching 99.48% on a 90:10 dataset split, 99.21% on an 80:20 split, and 98.95% on a 70:30 split.The Streamlit application was developed to visualize emotion predictions from K-Pop songs with a web-based user interface. In addition, Ngrok was used to provide remote access to this application, allowing users to test and use the application remotely.The results of this study show that the combination of K-Means and C4.5 can effectively cluster and classify emotions in K-Pop music, providing valuable insights into the musical characteristics that influence emotions. This application has the potential to be used in further analysis, development of intelligent features in music applications, and improvement of user experience in listening to K-Pop music.

All PT Astra International BMW Semarang transactions are recorded in the database but the problem is that the stock management is  efficientless so  the part stock that buyers are interested is not available. This research aims to conduct a comparative mining results using the association rule with apriori algorithm for year 2021, 2022 and 2023 sales transaction dataset with total of 43.694 records using the Rstudio. Data mining process in each year uses the same parameters for each itemset combination. The best association pattern occurs in 2023 with support value 0.05913841 and confidence value 100%. This can be concluded that the rules formed from each year could be different eventhough using same parameters. The item that always appears in the association rule from 2021 – 2023is Z99000000333 (BMW Engine OIL) which is often purchased with items named “Set fil-oil” so it can be a recommendation for  item stocking  in the warehouse.

Education in the digital era requires strong integration of computer programming in the educational curriculum. Programming algorithms are the main key in developing critical thinking and problem solving skills among students. The Merdeka Curriculum reinforces the importance of teaching programming algorithms as part of a curriculum that is flexible and adaptive to meet future demands. This research explores the benefits of programming algorithm strategies in the Merdeka Curriculum, including improving students' analytical, logical and systematic abilities, as well as their preparation for challenges in the digital era.

Typing errors in documents are fairly common. The process of checking typing errors manually will take a lot of time. Therefore, a system called Indonesian spelling correcting with Jaro Winkler is needed. Spelling Correcting is a feature that can check and correct word writing errors automatically. Jaro Winkler is an algorithm used for the word correction process by calculating the value obtained from the results of the operation of modifying one word with another word with the help of a matrix. Based on the results of trials on 13 words, namely "Alaman Amus Seperti Pada Gamba Tersebut Digunakan Untuk Memasukkan Data Kata Pada KBBI ", this spelling correcting application can produce 3 correct word corrections to " halaman kamus seperti pada gambar tersebut digunakan untuk data pada kbbi ", each Word improvement is calculated using the Jaro Winkler formula, getting a score above 0.94 for each word tested.

This study examines the use of dynamic programming in the context of container filling optimization, known as the knapsack problem. This problem requires selecting a number of goods with a certain volume and value to be loaded into a container with a certain capacity. We develop a knapsack-based algorithm using dynamic programming techniques to maximize container space utilization. By considering the volume and value of goods, our algorithm is able to achieve optimal results. Through a case study involving 25 items with predetermined volumes and values, we demonstrate the effectiveness of our algorithm in improving container space utilization. Our experimental results show significant improvements in container space utilization compared to naive filling methods. This research shows that the knapsack approach with dynamic programming can be an effective solution to the container filling problem in the context of logistics and optimization.

Data security is an important aspect of information systems, especially in the widely used windows operating system environment. Modern encryption technology offers a variety of ways to increase data protection from security threats. This method utilizes literature insights and comparative analysis of various cryptographic techniques applied to the Windows operating system. Data sources were obtained from academic journals, books and technical documents from Microsoft and cyber security institutions. This article describes various modern cryptographic techniques that can be applied to the Windows operating system, including symmetric and asymmetric algorithms and end-to-end encryption. Protocol implementation security. This research also examines the effectiveness of cryptographic techniques in overcoming cyber security threats and provides the best recommendations for improving data security. It also provides a comparative analysis of various cryptographic techniques implemented in Windows operating systems. This study shows that evaluating encryption techniques can help overcome cyber threats and provide optimal solutions to improve data security. Data sources were obtained from academic journals, books, and technical documents from Microsoft and cyber security.

Political dynasty is a political power exercised by a group of people who are related by family, with the aim of obtaining power and ensuring that this power remains within the group by passing it on to other family members. This study conducts a sentiment analysis on comments related to the Supreme Court decision which is believed to pave the way for Kaesang Pangarep in support of Jokowi's political dynasty. Sentiment analysis is carried out using the Naive Bayes method, a commonly used algorithm for text classification based on probability. The data used consists of comments from videos taken from social media platforms. These comments are then categorized into positive, negative, and neutral sentiments. The results of the study show the distribution of public sentiment towards this issue, providing an overview of how the public responds to the decision. The Naive Bayes method is chosen for its simplicity and its ability to provide reasonably accurate results in text analysis.

Computational thinking is the process of solving issues by using mathematical ideas and concepts. Systems problem solving, data analysis, abstraction, algorithms, and information representation are some of these ideas. The purpose of this study is to determine how mathematical problem-solving techniques for computational thinking function. Systematic literature observation was the research methodology employed in this study. A topic area of interest's existing research as well as pertinent, targeted research questions are found, reviewed, disseminated, and interpreted using the systematic literature review (SLR) technique. Journals may be found and evaluated methodically using the SLR approach, with each stage according to preset guidelines or rules.

Data mining is a technique for extracting new information from data warehouses, 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 is the case with the Dimsum business at Dimsum Madani.toko. This is located on Jalan Lampu gg. Pelita 4, Brayan Bengkel, East Medan, the location is close to many Brayan Resident's Houses. This of course affects sales levels. Increasing daily sales activity results in an accumulation of sales transaction data that continues to increase, thereby burdening data storage. Unfortunately, this data is only stored without further processing. In fact, this data collection holds valuable information.This research uses Market Basket Analysis with the Apriori Algorithm to find association patterns based on consumer shopping behavior. The goal is to identify items that are often purchased together. The research results showed that the combination of Seaweed Dimsum with Tofu Skin Spring Rolls had the highest support value (50%) and the highest confidence (75%).

In the era of rapid digitalization, understanding consumer behavior through data is becoming increasingly important for retail businesses. Shopping trends, such as those contained in this study, provide in-depth insights into various aspects of consumer behavior, from demographics to purchasing preferences and patterns of discount usage. This data is invaluable in formulating effective marketing strategies, improving customer experience, and optimizing business operations. The data used in this study included a variety of relevant variables, such as age, gender, location, product categories purchased, number of purchases, payment methods, and frequency of purchases. This information allows for a comprehensive analysis of how these factors affect consumer spending decisions. For example, analytics can reveal seasonal trends in purchases, product color and size preferences, and the impact of discounts and promo codes on sales volume. In addition, this dataset also reflects the changes in consumer behavior that have occurred over the past few years. Quantitative methodology is a research approach used to collect and analyze numerical data to understand patterns, relationships, and events in a given population. Data is collected from various sources such as online sales transactions, consumer surveys, Naive Bayesian algorithms are applied to the dataset that has been processed. The data was divided into two sets: training (80%) and testing (20%).    

This study aims to apply the C4.5 algorithm in classifying athlete performance based on the 2023 award recipient list. The C4.5 algorithm was chosen for its ability to construct decision trees that can identify patterns and characteristics distinguishing high-performing athletes. The data used in this study includes various attributes such as gender, age, sport, number of medals, and level of competition participation. The results show that the C4.5 algorithm can classify athletes with high accuracy. The resulting decision tree provides valuable insights into the key factors contributing to athlete performance. The implementation of this algorithm is expected to assist sports organizations in more effectively identifying and developing potential talents.    

Market basket analysis is an important technique in data mining used to understand consumer purchasing patterns. This research uses the Apriori algorithm to identify relationships between products in the shopping basket, aiming to improve sales and marketing strategies in the retail industry. The focus of this study is on retail transaction data from West Java Province, which has a large and diverse population, reflecting complex consumer purchasing patterns. The research identifies several key issues: limited understanding of consumer behavior, unoptimized business strategy opportunities, and challenges in managing large transaction data. As a solution, the application of the Apriori algorithm can help find frequent consumer purchasing patterns and design more effective marketing strategies. The results show that market basket analysis using the Apriori algorithm is effective in understanding consumer purchasing patterns in the retail industry. This algorithm allows companies to discover itemsets that frequently appear together in transactions, which can be used to design more effective marketing and sales strategies.

Detecting diseases in durian plants is a significant challenge in the agricultural sector, impacting yield and quality. This research aims to improve disease identification in durian plants by applying the Convolutional Neural Network (CNN) algorithm. The method involves collecting images of durian leaves, stems, and fruits infected by various diseases and using data augmentation techniques to expand the dataset and reduce overfitting. With the CNN model trained using this dataset, the accuracy reached 62% on validation data, with the highest accuracy for the “Healthy” class at 83%. The research results show that CNN is effective in recognizing diseases in durian plants, although there is still room for improvement through model optimization and the addition of training data. The implications of this research include the development of an AI-based disease detection system that can help farmers care for durian plants more efficiently and timely.

This study aims to apply the C4.5 algorithm in classifying athlete performance based on the 2023 award recipient list. The C4.5 algorithm was chosen for its ability to construct decision trees that can identify patterns and characteristics distinguishing high-performing athletes. The data used in this study includes various attributes such as gender, age, sport, number of medals, and level of competition participation. The results show that the C4.5 algorithm can classify athletes with high accuracy. The resulting decision tree provides valuable insights into the key factors contributing to athlete performance. The implementation of this algorithm is expected to assist sports organizations in more effectively identifying and developing potential talents.

Quantum computing offers promising alternatives to classical approaches for solving complex linear algebra problems. This paper presents a comparative study of the performance of quantum algorithms versus classical algorithms in solving systems of linear equations and matrix operations. Through simulation and analysis, we demonstrate that while quantum computing holds advantages in specific problem sets, classical computing remains efficient for general applications. These findings highlight the current limitations and potential of quantum computing.

Quantum computing has emerged as a transformative technology with the potential to revolutionize numerical methods in scientific research. This study explores the integration of quantum algorithms to enhance the efficiency and accuracy of computational techniques used in solving complex scientific problems. The objective of this research is to investigate how quantum computing can address limitations in classical numerical methods, particularly in areas such as optimization, simulation, and data analysis. By employing quantum-enhanced algorithms, such as quantum Monte Carlo and quantum machine learning, the study demonstrates significant improvements in processing speed and solution quality. The findings highlight the capability of quantum computing to tackle challenges in high-dimensional computations and provide novel insights into scientific phenomena. These advancements have profound implications for disciplines ranging from physics and chemistry to material science and beyond, paving the way for a new era of computational-driven discoveries.

Artificial intelligence (AI) is transforming paediatric diabetes management, offering innovative solutions for monitoring, treatment, and prediction. This mini-review explores how AI is being utilised to improve the care of children with diabetes mellitus, focusing on its application in glucose monitoring systems, predictive algorithms, and personalised treatment plans. The study synthesises recent advancements in AI technologies, examining their impact on enhancing the accuracy of diagnosis, reducing the burden on healthcare providers, and improving patient outcomes. Through a systematic review of the literature, key AI tools and models that have shown promise in paediatric diabetes care are identified. The findings highlight the potential of AI to revolutionise diabetes management, with implications for both clinical practice and future research. However, challenges remain in ensuring the ethical implementation and integration of these technologies into existing healthcare systems. The paper concludes with recommendations for advancing AI applications in this field, emphasising the need for continued innovation and collaboration between healthcare professionals and AI developers.

This research explores various machine learning approaches, including deep learning and ensemble methods, to predict climate change indicators. We focus on temperature and precipitation trends using large datasets spanning multiple decades. By comparing the performance of algorithms like CNN, RNN, and random forests, we identify the most accurate models for specific climate variables. Our findings demonstrate that ensemble models provide better accuracy and reliability, especially for temperature predictions.