The study described investigates the application of Brain-Computer Interface (BCI) technology, focusing on Motor Imagery (MI) signals which enable individuals to control movements through mental visualization. A major challenge in this field is accurately distinguishing between different movements, particularly when dealing with data from multiple subjects and recording sessions, known as inter-subject and inter-session variability. To address this, the authors employ the Wavelet Packet Transform-Common Spatial Patterns (WPT-CSP) method to enhance the resolution of MI signals. They visualize the results using Brain Topographic Maps (Topomaps) to depict brain activity during MI tasks, facilitating the analysis of variability across subjects and sessions. Utilizing dataset 2a from the Brain-Computer Interface Competition (BCIC) IV, the study demonstrates the efficacy of this approach in identifying variability patterns. This research holds promise for improving BCI technology applications in various domains, and future work could explore refining signal processing techniques and validation on larger datasets. Topomap.

Coronary heart disease is a disease in which the occurrence of blockages in the blood vessels in the heart. Coronary heart disease is a fatal disease, it is better to get as much information about this disease as possible. Data Mining can classify whether a person has heart disease or not based on symptoms. Data mining builds a model that can predict whether a person has heart disease or not. How well a model performs classification can be determined from its accuracy value, but this accuracy value can still be improved. Increasing the accuracy value can be done by performing Feature Selection. The research object used in this research is a dataset about coronary heart disease obtained from the Kaggle website. The classification method used in this modeling is the Random Forest algorithm to classify whether a person has coronary heart disease or not. The Random Forest Algorithm is a classification algorithm consisting of Decision Trees for classifying. The Random Forest algorithm is used because it has been proven to produce good accuracy in several previous studies. The Feature Selection method used in this modeling is the Chi-Square hypothesis test to determine whether there is an effect of each independent variable on the dependent variable. This research compared the value of modeling accuracy without using Feature Selection with modeling using Feature Selection. The result of this study is that the model without Chi-Square Feature Selection produced an accuracy value of 96,05% and the model with Chi-Square Feature Selection produced an accuracy value of 97,33%.

Sudoku is a digital game that is included in the type of logic-based puzzle game where the goal is to fill in the puzzle with random numbers. Therefore, in this research it is proposed to use Artificial Intelligence which contains the Depth First Search Algorithm to track the number of possible solutions that lead to only one so that it becomes efficient. This game has different levels of difficulty such as easy, medium and difficult. The time and complexity of execution will vary depending on the difficulty so it is proposed to use Android Studio software. The experimental results prove that there is an increase in playing the Sudoku game quickly and accurately by applying the Depth First Search Algorithm method. This is proven by the ability to complete this game using the Depth First Search Algorithm using the Android Studio programming language. The average time at the easy level is 11:04 minutes, at the normal level is 10:52 minutes, at the hard level is 25:46 minutes, and at the extreme level is 38 minutes.

Emotions, being transient and variable, differ across locations, times, and individuals. Automatic emotion identification holds significant importance across various domains, such as education and business. In education, emotional analysis contributes to intelligent electronic learning environments, while in business, it aids in assessing customer satisfaction with products. This study advocates the application of Principal Component Analysis (PCA) to enhance the performance of text emotion classification using the Artificial Neural Network (ANN) method. PCA, a pattern identification method, reduces text dimensions, improving the classification process by determining word similarities. PCA offers the advantage of dimension reduction without compromising information integrity. The classification approach involves two stages: one after PCA dimension reduction and the other without PCA post TF-IDF stage. The study's conclusive findings, incorporating PCA in ANN classification, demonstrated a notable increase in recall for the happy class, reaching 0.92 compared to the pre-PCA score of 0.91. Furthermore, precision in the sadness class improved to 0.90, surpassing the pre-PCA precision of 0.80. This affirms the efficacy of integrating PCA in enhancing the accuracy and performance of emotion classification in text analysis.

One of the natural disasters that currently occurs frequently in Indonesia is earthquakes. An earthquake is a situation where the earth shakes due to volcanic activity, or collisions due to the movement of the earth's plates. Earthquake activity causes many problems. One of them is liquefaction. Liquefaction is an event that shows a loss of soil shear strength caused by an increase in pore water pressure. This occurs because the earthquake load occurred so quickly and briefly. Liquefaction is a description of the effects of an earthquake so that the soil layer loses its strength. Not many people know the process of earth movement which is the precursor to earthquakes. Especially people who have hearing limitations. For this reason, education is needed to be conveyed to the public. Educational techniques in virtual form will be a special attraction for people, especially deaf people. Augmented Reality (AR) technology is a technology where virtual objects and real objects are combined. In this research, an application was produced in the form of learning media that utilizes Augmented Reality (AR) technology with the Marker Based Tracking method. To make learning media more interactive, the technology was developed using the MDLC (Multimedia Development Life Cycle) method. The results of the tests carried out concluded that the application could be used as an interactive learning medium to increase knowledge about the occurrence of earthquakes and the effects of liquefaction for the community

The study aims to implement Data Mining with Apriori Algorithm and Association Methods (shop cart analysis) to analyze the sales pattern of Kaffa Beauty Shop stores as a case study. Sales information obtained from stores is used to find out the repeated buying habits of cosmetic products. This analysis provides store owners with valuable information to make more useful decisions about product inventory management, marketing strategies, and other aspects of their business. The Apriori Algorithm implementation follows steps including data preprocessing, subsetting, frequent dataset search, and strong association rules (strong Association Rules). The results of the analysis show that there are important purchasing patterns among some cosmetic products that can be the basis of a more effective sales strategy. The study helps understand how data mining and Apriori Algorithms can be applied in business contexts such as Kaffa Beauty Shop stores. Therefore, the results of this analysis are expected to contribute greatly to improving business efficiency and optimizing marketing strategies for store owners and stakeholders. The research is also expected to show the enormous potential of data analysis to support optimal business decision making.

Spam email is a problem that disturbs and harms the recipient. Machine learning is widely used in overcoming email spam because of its ability to classify emails into spam or non-spam. In this research, the Naïve Bayes algorithm is initiated with the Chi-Squared selection feature to classify spam emails. So that the implementation is able to increase accuracy for better performance in classification. The feature selection method is used to direct the model's attention to features that are related to the target variable. In this study, the chi squared feature uses a value of K = 2500, with an accuracy of 98.83% which shows an increase in model performance compared to previous research. So that the Naïve Bayes model with the Chi-Squared selection feature is proven to provide better performance. 

Utilization of computer vision can be applied in various aspects of daily life, reducing dependence on human labor. One of its implementations is in industry, such as in the production process of motorized vehicles, to sort or classify parts or goods. The computer vision process involves many stages, such as image capture, image processing, image analysis, image recognition, and decision-making. In the automotive industry, computer vision has been used in autonomous or driverless electric vehicles, as well as in creating intelligent transportation systems. To detect objects in real-time, one of the options that can be used is to use the YOLO algorithm, which can detect objects in one stage with predictions of bounding boxes and class probabilities simultaneously. However, although YOLO has good performance, the architecture has some drawbacks, such as complexity and complicated hyperparameter congurations. To remedy this, the Adam optimization algorithm was introduced, which combines the momentum and RMSprop algorithms to adjust the learning rate adaptively and provide faster convergence in model training. This is evidenced by an increase in the value of mAP on Yolov5. These results prove that the Yolov5 method with Adam`s optimization is better than the Yolov5 method without optimization.

Education is one of the most important things for people to have. Many people are competing for education to increase their abilities. Technology plays a big role in developing access to education to make it easier with many online applications and online classes education becomes easier. However, there are still many unresolved problems in this field of education, namely the emergence of the phenomenon of incompatibility between educators and students so that student interest decreases dramatically because of this. And also, the lack of learning materials taught that are not school subjects such as programming. Therefore, the author and team designed an application where this application can find students a learning mentor outside of school so that they can increase their knowledge. The application also provides a matching feature based on the student's personality so that the student can find a suitable tutor.

Abstract – Person re-identification is a mechanized procedure of video investigation which has been widely studied in contemporary years. Research problems that are often raised in the field of a person's re-identification research are characteristic representations that are easily affected by closure (abhorrent to other objects). Furthermore, after extracting local features by means of a boundary box, the background image still contains and does not focus on the human body parts. This study comes up with a method combination of CNN, SVM classification, and semantic segmentation. CMC (Cumulative Matching Characteristics) and mAP (mean Average Precision) are measurements of assessment that will be utilized to measure the operation of re-identification. The ResNet + SVM + SSP-ReID technique performed best in the Market dataset, with a CMC increase of 3-10% (rank-1 through rank-20). The Market and CUHK03 (D) datasets both showed improvements of 1-4.1% in mAP.  Keywords Person re-identification; Feature extraction; CNN; SVM; Semantic segmentation;