This research designs a system to classify apple leaf diseases using RGB (red, green and blue) color feature extraction. The essence of this research is to design a system to recognize and determine disease on apple leaves based on RGB color features using the Matlab 2024 application. The data in this research uses apple leaf images from kaggle.com, which are then cropped and adjusted to the image shape and precision in the leaf image. , Increasing the contrast of the cropped image and converting it to a grayscale image, Determining the threshold for binarization and converting the grayscale image to a binary image, Detection of green, yellow, and black/gray pixels based on RGB values ??and calculating the proportion of each color, Detection of pixels scab by filtering out black/grey pixels that do not include green or yellow pixels Classification of leaves based on the proportion of detected colors.
With the method that has been passed and uses apple leaf data, namely Healthy, Rust and Scab, each data contains 20 images with a total of 60 images and the level of accuracy is determined using the labeling method for each data and reaches the final result with an accuracy level of 86, 6667% which has a fairly accurate meaning
 

Web-based LPG gas sales system using E-KTP is an application designed to facilitate the process of selling LPG gas online by using E-KTP as a user identity verification tool. This system was built with the aim of increasing efficiency and security in the LPG gas sales transaction process. This system utilizes web technology to allow users to sell LPG gas online through a platform accessed through a web browser. Users will be required to upload a photo of their E-KTP as part of the identity verification process. After identity verification, users can place an order for LPG gas according to their needs. The system is also equipped with an LPG gas stock management feature, so that sellers can monitor their LPG gas stock in real-time and procure new LPG gas if the stock is running low. In addition, the system is also equipped with an LPG gas delivery tracking feature, so that users can track the status of their LPG gas shipments from the warehouse to the delivery location. With this web-based LPG gas sales system using E-KTP, it is expected to increase efficiency in the LPG gas sales process, as well as increase security in online transactions by using E-KTP as a means of verifying user identity.

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%).
 
 

Item-based collaborative filtering is a popular technique in recommendation systems that aims to provide suggestions for films to watch or services to users based on similarities between items. In this approach, the similarity between items is calculated using metrics such as cosine similarity, allowing the prediction of user preferences for items that have never been rated. This research implements Item-based collaborative filtering using datasets from Kaggle. Experimental results show that the resulting model is able to provide recommendations with significant improvements in evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 3.05 and 3.26. This shows that the smaller the value, the better.

The education sector must utilize the latest technology in the classroom in the era of globalization and rapid advances in information technology. Using text, graphics, audio, and video together, interactive learning materials have been proven to increase student understanding and engagement. Kindergarten (TK) is a place where children are introduced to the environment and the basic principles of learning. Introducing children to various types of transportation is believed to be very important because it is the first step in helping them broaden their horizons and become familiar with the types of transportation in their environment. Apart from that, children are very interested in and liked transportation, so it must be packaged attractively so that it can arouse their curiosity in learning.

Unemployment is a persistent problem in the labor market, thus hampering economic development and national prosperity. Indonesia, including its capital Jakarta, continues to face significant levels of unemployment compared to neighboring countries. This research focuses on analyzing the structure of unemployment in Jakarta using K-Means Clustering to categorize unemployment data based on age groups (2020-2022) sourced from the Central Statistics Agency. Analysis carried out via RapidMiner revealed three clusters:-Cluster 0: Age 30-60 years and above, Cluster 1: Age 20-24 years, Cluster 2: Age 15-19 and 25-29 years. The findings show that the 20-24 year age group has the highest unemployment rate (399,167 people), while the 30-60 year and above age group shows the lowest unemployment rate (75,560 people). This clustering approach provides insight into the distribution of unemployment by different age demographics in Jakarta, highlighting areas where targeted interventions may be needed to effectively address this socio-economic challenge

Predicting sales is an important aspect in sales development. Sales prediction simulation is an estimated calculation of the level of product sales in a certain period. Sales of pempers tend to fluctuate due to the choice of many brands available, resulting in sales having a little difficulty in estimating sales of MAKUKU diaper products at Greens Mart stores. This research aims to predict sales Pempers products. This research uses several stages of identifying problems, determining research objectives, collecting data, data collected or obtained from interviews with salespeople, managing data using Monte Carlo stages, implementing/testing data and testing results to see the accuracy of the method used. The analysis results show that Comfit M and Comfit L have almost the same level of accuracy, namely Comfit M is around 90.63% and sales of Comfit L are 90.48%. These values ??provide an indication of the level of accuracy of the sales predictions made.
 
 
 

In Indonesia, tuberculosis is ranked third in terms of prevalence among countries with the highest tuberculosis burden. Radiological examination, such as X-rays or X-rays, is a method generally used to detect tuberculosis. Chest X-ray examination is one method used to detect tuberculosis. To achieve these goals, the research will combine two powerful data processing techniques. First, the K-Means algorithm will be used to group x-ray image data based on similar characteristics, making it easier to identify typical patterns from images infected with tuberculosis. The research results show the highest accuracy of 93% using data division with a ratio of 80 : 20 with parameter K = 1. These results show that the combined model of the two algorithms can be applied to identify tuberculosis in the lungs.
 
 

Asynchronous Transfer Mode (ATM) is a network technology designed to support high-speed data transfer by utilizing small fixed-size data packets called cells. This study aims to analyze the implementation and performance of ATM in the context of the Canva application, a web-based graphic design platform. The study includes a theoretical review of ATM's working principles, advantages, and limitations, as well as the network performance analysis methods used.In this research, we examine how ATM can optimize data transmission in the Canva application, considering the importance of network speed and efficiency in ensuring an optimal user experience. The analysis results show that ATM can provide the high transfer speeds and stability required by the Canva application, especially in managing complex and large-sized graphic data.The conclusion of this study confirms that the implementation of ATM technology in web-based applications like Canva can significantly enhance network performance. This study also offers important insights for further development in integrating advanced network technologies for internet-based applications to support increasing data demands.

Attention to spices and flavorings among the younger generation is still low. The strategy that can be used to overcome this problem is a programmed and computerized arrangement of spices and flavorings using Convolutional Neural Network (CNN) calculations. In this exploration there are 300 images of spices and flavors which will be characterized into 3 classifications. Namely ginseng, ginger and galangal. Information in each classification is divided into two, namely preparation information and testing information with a proportion of 80%: 20%. The CNN model used in computerized grouping of spice and flavor images is a model with 2 convolutional layers, where the first convolutional layer has 10 channels and the second convolutional layer has 20 channels. Each channel has a 3x3 portion frame. The channel size in the pooling layer is 3x3 and the number of neurons in the secret layer is 10. The actuation capability in the convolutional layer and secret layer is tanh, and the actuation capability in the result layer is softmax. In this model, the accuracy of preparation information is 0.9875 and the loss value is 0.0769. The precision of the test data is 0.85 and the loss value is 0.4773. Meanwhile, testing new information with 3 images for each classification resulted in an accuracy of 88.89%.