The results of implementing Orange Data Mining for forecasting the value of the Gold Price are displayed on the Test and Score widget. RMSE and MAE values were obtained from each model from the test. The RMSE and MAE values for the K-Nearest Neighbor (K-NN) method are 0.007 and 0.006, respectively, while for the Support Vector Machine (SVM) method are 0.006 and 0.005. The RMSE and MAE values for the Linear Regression method are 0.004 and 0.003, respectively. Compared to the K-Nearest Neighbor and SVM methods, the Linear Regression method is the best at predicting changes in Gold prices based on the RMSE and MAE data mentioned above. For future research, this best practice method needs to be studied more deeply. It is recommended for future research to compare the Linear Regression method with alternative approaches using the Orange tool set or other related tools.

This research use to predict the impact of hybrid learning on Medan State Polytechnic students. This algorithm was chosen because it has excellent performance in classification compared to other algorithms. Statistical and probabilistic methods are used in the operation of this algorithm to make predictions about what will happen in the future. Technology mastery, level of teacher-student interaction, and mastery of teaching materials are the variables used in this study. The sample data used came from students of the Software Engineering Technology Study Program of Medan State Polytechnic. The prediction results carried out manually with naïve bayes, with training data of 100 (one hundred) students and test data of 1 (one) student, produced a result of 0.012, which indicates an increase in student academic results. The test results were proven using the phyton programming language. The first test results, with 20% test data, resulted in an increase in academic results by 86% around 13 students with an accuracy value of 80%, and the second test, with 40% test data, resulted in an increase in academic results by 92% around 29 students with an accuracy value of 88%.

The development of the world of transportation has increased rapidly, this is marked by the emergence of electric cars. The changes experienced by society are so fast to switch from fuel cars to electric cars. Electric cars have various advantages of being environmentally friendly, not producing pollutant gas emissions, being faster, cost-effective and more comfortable. In addition to the advantages provided there are also several risks from the transformation of oil-fueled (bbm) cars into electric cars including expensive car prices, charging infrastructure, shorter mileage, electric car batteries have a limited lifespan. This study aims to analyze the risk mitigation of transforming oil-fueled cars into electric cars. In this case study, the researcher uses two kinds of attributes, namely the price of the car and the emissions it produces. The method used is to calculate each gain information from the attribute used by looking at the highest gain value, it will be known how big the risk is for the transformation of oil-fueled cars (bbm) to electric cars. and what factors (attributes) most determine the risk. . It is hoped that this research can help electric car developers and entrepreneurs to continue to improve the performance of existing electric cars.

Motorcycles are a predominant mode of transportation in Indonesian society, comprising 84.5% of the total transportation vehicles in 2021 according to national BPS data. Despite providing convenience in mobility, motorcycles are susceptible to disturbances or damages that can hinder normal usage and potentially lead to accidents. Many motorcycle riders lack knowledge or awareness regarding potential issues with their motorcycles. This research aims to analyze the implementation of the certainty factor method in an expert system for identifying motorcycle malfunctions, with a focus on Giska Servis workshop. The certainty factor method serves as a reasoning tool to determine identification outcomes based on identified symptoms. The results of this study are expected to contribute to facilitating motorcycle riders in diagnosing symptoms of malfunctions in their vehicles. The certainty factor method offers a systematic and structured approach to identifying motorcycle issues. Through the implementation of this method, the research attempts to measure the success rate of the expert system in diagnosing malfunctions. Data from the identification results at Giska Servis workshop will be comprehensively analyzed to evaluate the accuracy and effectiveness of the certainty factor method in this context.By highlighting the success of this method, this research is expected to provide valuable insights for the development of expert systems for motorcycle issue identification. The findings of this study can serve as a guide for workshops and motorcycle users to enhance understanding and management of vehicle issues, thereby minimizing the potential for accidents and extending the lifespan of motorcycles.

YouTube is evidence of the development of leading digital technology in the media and entertainment sector. YouTuber is a term for those who create content or broadcast live on YouTube media. Not all YouTubers are able to express themselves directly in the content they create. For this reason, Virtual YouTubers were created, which are 2-dimensional or 3-dimensional virtual characters created using computer software to make it easier for YouTubers to interact with viewers. This research aims to analyze the live broadcasts of VTuber HoloH3ro, namely Kaela Kovalskia, Vestia Zeta, and Kobo Kanaeru, which contain schemes during the live streaming. With a large amount of data for each live stream time, a Naïve Bayes algorithm is needed through the use of range and frequency features, so accuracy results will be obtained which will be tested using the Confusion Matrix. The dataset used was taken from the kaggle.com site regarding the scheme live stream schedule conducted by HoloH3ro.

In the learning process at the inclusive junior high school SMP Muhammadiyah 5 in the city of Cimahi, conventional methods are still employed, utilizing limited book resources, and students remain passive, merely listening to what the teacher imparts. Augmented Reality is a technology that combines the real world with digital or virtual elements, creating an engaging and interactive user experience. The purpose of this research is to develop a learning media using Augmented Reality technology that is engaging and interactive in the field of Science, thus facilitating both teachers and students in comprehending the material. The stages in this research employ the Multimedia Life Cycle method, which includes concept, design, data collection, creation, testing, and distribution. The research has successfully developed an Android-based application that has undergone black box testing. Survey results also indicate a 90% satisfaction rate, demonstrating that the application is easy to use, interactive, and engaging.

Information technology nowadays is developing very rapidly, especially in the tourism sector. The use of technology in the tourism sector has made Traveloka's services popular because they can be accessed easily, quickly and reliably. The Traveloka service provides a service where users can order plane tickets from various airlines that collaborate with Traveloka with different price variants. This service makes it easier for people to enjoy their holidays and find out plane ticket prices easily. This research used data collection through questionnaires and obtained 30 respondents. Then a validity and reliability test is carried out to measure the quality level of a statement, after which a maturity level calculation will be carried out to analyze whether Traveloka's services are good enough to carry out service operations in the community. This research uses the ITIL V3 framework to analyze Traveloka services, especially in the Service Operation domain, which consists of 5 subdomains, namely the maturity level in the Event Management subdomain gets a value of 4.41, the maturity level in the Incident Management subdomain gets a value of 4.22, then for the Problem subdomain Management got a maturity level of 4.17, then for the Request Fulfillment subdomain the maturity level got a value of 4.21 and finally for the Access Management subdomain it got a maturity level of 4.30. So it can be seen that based on the results of the maturity level calculation it can be stated that all subdomains are at the Managed Level.

Smoking is an activity that can have a bad impact on health, both for yourself and for others. This is because there are various kinds of ingredients that are dangerous to health, there are 4000 types of chemicals found in cigarettes, starting from nicotine and other substances that can cause cancer in humans. The dataset obtained from BPS (Central Statistics Agency) can be utilized by using K-Means Clustering to determine the grouping of smokers aged more than 15 years in each region. The grouping of smokers aged over 15 years can be seen through 3 clusters, Cluster 1 is the highest level of smoking in 10 provinces out of 34 provinces studied, Cluster 2 is the medium level with 15 provinces out of 34 provinces studied, and Cluster 3 is the lowest level. with 9 Provinces.

In the context of regional development and decision making related to determining the best village, the use of a Decision Support System (DSS) with the application of the Entropy and Additive Ratio Assessment (ARAS) algorithms is a very important approach. The main objective of this research is to propose and implement a method that utilizes the Entropy algorithm to evaluate criteria weights and ARAS to rank villages based on predetermined criteria. This approach begins the process by identifying relevant criteria to determine the best village in an area. Next, the Entropy algorithm is used to measure the level of importance or relative weight of each predetermined criterion. This step helps in assessing how informative each criterion is in the decision-making process regarding determining the best Village.
 After determining the criteria weights using Entropy, the approach continues with the application of the ARAS method. ARAS is used to rank villages based on normalized values ​​from previously determined criteria. The data normalization process is carried out to ensure the validity of comparisons between villages. The final result of this approach is a ranking of villages indicating the best villages based on the criteria considered. This method was tested in a case study using a dataset involving a number of relevant criteria for assessing village development potential. Experimental results show that the use of the Entropy and ARAS algorithms in the Decision Support System provides an effective and informative framework for decision makers in determining the best Village. In conclusion, this approach provides a solid foundation to support a more effective and precise decision-making process in regional development based on clearly defined criteria.

This research focuses on issues within the agricultural sector, specifically in cocoa farming. The associated problem is the lack of agricultural extension workers, which results in farmers' limited understanding of cocoa plant diseases and their management. As a consequence, disease identification in cocoa plants has traditionally relied on mere estimates, often proving inaccurate and inefficient in taking appropriate actions. The primary objective of this research is to design and develop an Android-based application utilizing the Certainty Factor method for diagnosing diseases in cocoa plants. The application is envisioned to function as an intelligent consultant for providing disease diagnoses in cocoa plants. The research employs the Research and Development methodology, gathering data from both primary and secondary sources through observations and interviews. The application is tested using both whitebox and blackbox methodologies. Whitebox testing results demonstrate that the system can operate effectively, as indicated by calculations of V(G) and Cyclomatic Complexity (CC). Blackbox testing, conducted with sample tests, also indicates that the system functions as expected and efficiently. In conclusion, the system developed in this research can significantly assist users, particularly farmers, in diagnosing diseases in cocoa plants. The expert system for diagnosing cocoa plant diseases can be relied upon and operates effectively.