Search

The primary purpose of this research is to use the Support Vector Machine (SVM) classifier and the Naive Bayes (NB) classifier to make arrhythmia predictions from the MIT-BIH database. With an alpha of 0.05, 95% confidence interval (CI), 80% power, and an enrollment ratio of 1, the proposed research employed SVM and NB machine learning algorithms to predict arrhythmia using the MIT-BIH dataset of 65 normal and 65 abnormal ECG signals downloaded from IEEE dataport in.xlsx format. We used the data mining programme WEKA 3.8.5 to determine how to categorise people with and without arrhythmia. IBM SPSS version 21 was used for the statistical analysis. When comparing SVM and NB classifiers, a statistically significant difference (p0.010) was found. Using WEKA's 10-fold cross-validation for training and testing, the SVM classifier outperformed the NB classifier with an 88.50% accuracy rate in classification (80.39 percent). [ABSTRACT FROM AUTHOR]

The primary goal of this research is to use J48 and K-Nearest Neighbor (KNN) classifiers to predict arrhythmia using the MIT-BIH database. With an alpha of 0.05, 95% confidence interval (CI), 80% power, and an enrollment ratio of 1, the proposed study used the J48 and KNN machine learning algorithms to predict arrhythmia using data from the MIT-BIH dataset consisting of healthy (n=65) and arrhythmia (n=65) ECG signals collected from IEEE dataport in.XLSX format. WEKA 3.8.5, a data mining tool, was used to distinguish between those with arrhythmia and those without. IBM SPSS version 21 was used for the statistical analysis. There was no discernible difference (p=0.025) between the J48 and KNN classifiers. Using WEKA's 10-fold cross validation to train, test, and verify the classifiers, we find that the J48 classifier is more accurate at classifying data (89.80 percent) than the KNN classifier (87.64 percent). [ABSTRACT FROM AUTHOR]

It is possible to collect online data from huge databases by web mining. Anemia can be identified by low hemoglobin levels or a shortage of red blood cells. The most frequent cause is undernutrition, which affects young children, pregnant women, and women of reproductive age most frequently. Without treatment or attention, it may result in heart failure or an enlarged heart. In this study, we collect information regarding anemia in Gujarat from the internet and organize it according to several criteria. The dataset is created by using Python soup to scrape the web. We can further categorize the dataset we extract to reach a conclusion. The identification of anemia-related information provided by this research also helps to save lives. Data mining technologies forecast future trends, support organisations, and display facts in a way that is simple for people to grasp. For a variety of stakeholders, including policymakers, programme planners, health service providers, academicians, research scholars, and regular citizens, the data can be processed, analysed, and described by a specially trained algorithm, enabling them to make decisions based on the available evidence. [ABSTRACT FROM AUTHOR]

The C4.5 algorithm still has weaknesses in predicting or classifying data if the amount is large. It is necessary to improve the performance of the C4.5 algorithm with the selected split attribute using application of the average gain value to perform the classification. The C4.5 algorithm is one of the Decision Tree methods in the classification process using entropy. The result of the classification obtained from the analysis can be a classification of 8 student data from 100 student data that is tested to produce information on Sanguine, Choleric, Melancholy, and Phlegmatic. From the result of the Decision Tree classification algorithm C4.5 has an accuracy rate of 86.36% with an application error rate or error of 13.64%. [ABSTRACT FROM AUTHOR]

Students graduating on time are an essential indicator for a higher education institution in supporting campus accreditation. Several factors cause students to graduate on time, namely the origin of the previous student's school and student interest. This study aims to predict student graduation based on the head of the last student's school and student interest so that higher education institutions can get the basis for decisions that will be taken in the future. The method used in analyzing student data and supporting criteria for predicting student graduation is the C4.5 algorithm. Then for the decision tree classifier, this research uses data mining. [ABSTRACT FROM AUTHOR]

In a company, employees are the main movers of the company, and the role of an employee is very important to help run business processes in the company. Kumala Galindo Lestari Company recruits employees who are deemed to meet the qualifications required by the company. However, there are several obstacles in the employee recruitment process, where the file selection process and data collection of prospective new employees before the test are to determine a decision because the process is still manual and employee recruitment must be right on target because if it is wrong in the hiring decision it will cause performance that is not by management. company. The concept of data mining will make it easier to overcome these problems, so the classification method can find models that distinguish concepts or data classes to be able to estimate the class of an object. Therefore, the Naive Bayes algorithm can predict future opportunities based on previous experience. In this study, researchers took 41 data on prospective employees, using 4 criteria: Graduate, Salary Requests, Work Experience, and Classification. The results of this research are expected to help Kumala Gasindo Lestari Company in determining the appropriate and effective recruitment of employees. [ABSTRACT FROM AUTHOR]

One of the things that is highly expected when collecting data is to produce complete data. In research, incomplete data will affect the results obtained. This is due to the non-maximum process carried out in the research. A dataset is a collection of data information that has been stored for a long time and becomes a large pile of data. Not infrequently, the dataset used in the research data presented is not complete. The missing value problem can be solved using data mining techniques. Data mining is the process of extracting information from a collection of data already stored in the data warehouse. Classification is the process of finding a common identity among different entities and classifying them into appropriate classes. Classification of large and complex data if performed manually would be difficult and take a long time. The K-Nearest Neighbor Imputation algorithm is a system that uses a supervised learning algorithm and aims to find new data patterns by connecting existing data patterns with new data. The conclusion that the authors drew is that the application of data mining to search for lost data using the K-Nearset Neighbor Imputation (KNNI) method is a process for generating new knowledge in the form of a comparison between the factors that influence the search for the missing data. The results of data mining using the K-Nearest Neighbor Imputation (KNNI) method are an arrangement of sequences of activities that support each other in the process. [ABSTRACT FROM AUTHOR]

Data are a collection of information in the form of facts. Information is stored in data from various origins. Data processing is an important step that is currently carried out. Data processing is commonly performed using data mining. However, data processing usually face barriers that keep it from fully running well because the data stored in the dataset sometimes are not in a normal form. One of the problems encountered in random data is that there is a considerable distance between data, which sets an impediment to data processing. This problem can be solved using normalization. Normalization is also generally referred to as simplification. Some algorithms such as the min-max normalization and Z-score algorithms can be used for normalization. The results of the testing on the use of the min-max normalization and Z-score algorithms for normalization revealed that the former had better performance than the latter. This was judged from the magnitude of the increase in accuracy obtained from the use of both algorithms, in which case min-max normalization gained an increase of 0.41%, while Z-score normalization did an increase of 0.14%. [ABSTRACT FROM AUTHOR]

Medical research has evolved considerably at this time. In the past decade, a growing number of researchers have been conducting numerous medical research works. Existing medical research generally uses medical datasets. The medical research that is currently being carried out is related to the use of computers and datasets. Data processing on datasets usually uses data mining techniques, one of which is classification. The results from a classification process are generally dependent on the model formed. Based on this, it is necessary to perform a comparison of classification algorithms in data mining. The purpose of this comparison is to find out which algorithm has the best performance. The data processing carried out on medical datasets faces various obstacles, including the problem of the distance between scattered values. Normalization is a process of simplifying the data contained in a dataset. Based on the test results from the comparison performed, it was found that the K-NN algorithm has better performance than the Naïve Bayes algorithm. The K-NN algorithm obtained a 95.30% accuracy before normalization and 95.44% after normalization. Meanwhile, the Naïve Bayes algorithm obtained an accuracy of 86.28% before normalization and 95.44% after normalization. It can be summarized that normalization was better with the use of the Naïve Bayes algorithm, with an increase of accuracy level of 6.21%. On the other hand, the K-NN algorithm increased only by 0.14%. [ABSTRACT FROM AUTHOR]

Over the past few decades, there has been a tendency to increase the number of ongoing studies of varying complexity and focus, which is directly related to significant progress in various fields of science. As a result, the growth of ongoing research is directly proportional to the growth of scientific papers presented to scientists in the form of scientific articles published in various journals or scientific and educational publications. However, such many scientific papers entails the formation of the problem of finding and selecting materials that are potentially useful for research conducted by young scientists or scientists working in new areas of science. As a solution to this problem, this work is proposed for consideration, devoted to the implementation of its own system designed for natural language processing of scientific publications, to group input data materials into main categories, by extracting attributes from texts using a matrix of terms, reducing the dimension by the Correlation-based approach feature selection and clustering by the k-means algorithm. The result of this solution will be a structured data cascade, consisting of the main topics containing research papers belonging to the input data array. [ABSTRACT FROM AUTHOR]

Weak gravitational lensing is an important tool to estimate the masses of galaxy clusters, as it allows us to directly access their projected surface mass density along the line of sight in a manner largely independent of their dynamical state. Moreover, we can extract information on the projected shape of the cluster mass distribution. In this work, we generate mock catalogs of lensed background galaxies to measure the individual lensing properties of galaxy clusters from the simulation project The Three Hundred. By repeating the analysis for different projections of the same cluster, we find that the use of shear multipoles provides constraints on the ellipticity of the cluster projected mass density but does not have a significant impact on the cluster mass reconstruction compared to the standard approach. [ABSTRACT FROM AUTHOR]

This study aims to formulate a product bundling strategy for IndiHome products using the Apriori Algorithm, one of the association rule algorithms in data mining techniques. This study used 62,504 sales transaction data for IndiHome products at Telkom Jakarta Timur in 2021 to be processed through data mining techniques with the association method. The association method generates association rules based on the relationship between item. The evaluation of the results of this algorithm is based on the value of support, confidence level, and lift ratio. The results of this study reveal that the Apriori algorithm can form association rules that are applied in sales strategies to optimize products. Based on the result, this study finds that the association rules formed can be implemented into a product bundling strategy in terms of product mix strategy and sales channel strategy. Furthermore, this product bundle also shows the effectiveness of each channel location, so this application can be used to justify a marketing strategy that should be implemented. In addition, the sales force channel is one of the most effective and strategic selections that can offer attractive product package programs at competitive prices that can increase customer purchasing power. Thus, the company can increase product sales, especially achieve daily and monthly targets and optimize potential returns. [ABSTRACT FROM AUTHOR]

There is a need to produce more crop plants to meet the future global demand. However, the climate change has affected the global crop yield. Consequently, finding an alternative approach to improve crop yield becomes essential. The development of sequencing techniques, as well as information technologies, have enabled us to perform genome data mining. Using genome data mining approach, it is possible to identify or discover a protein which has a particular characteristic. This study aims to identify a protein, which could potentially improve crop yield, using genome data mining approach. D1 protein was used as the target, as this protein is highly involved in photosynthesis. Then, protein sequences of various crop plants were collected from biological database. After conducting data trimming and filtering, sequence analysis was performed. The analysis was used to construct phylogenetic tree and create a 3D protein model. Sequence analysis displayed variation in amino acid sequence in D1 protein. Protein modelling located the variations, which scattered within D1 protein. Furthermore, we highlighted the amino acid residues that are the targets for genetic engineering. The research findings may provide a reference to improve crop production through genome mining approach. [ABSTRACT FROM AUTHOR]

Recently, researchers and clinicians have been searching for new technologies to slow down or stop COVID-19 pandemic. The utility of Data Mining (DM) algorithms to suggests new opportunities to combat the spread of the new Coronavirus. This paper suggests a comparative study on data mining approaches to predict COVID19. We used common classification algorithms like the Support Vector Machines, Random Forest, Logistic Regression, K-Nearest Neighbor and Artificial Neural Network with Python simulation to compare it in metrics accuracy, recall, precision and AUC; results showed that Random Forest model had a 98.43% accuracy – which is a higher accuracy than many other previous studies known COVID-19 data mining algorithms. [ABSTRACT FROM AUTHOR]

This article presents the results of solving various classification problems by the algorithms proposed in this study. Computational experiments were carried out in such a way that their results were comparable with already known solutions. This made it possible to assess the strengths and weaknesses of the minimax approach to data analysis and determine the ways for further research. Of the traditional methods of cluster analysis, linguistic data analysis was chosen, since the method of nuclear clustering has "grown" and is developing in this approach. The two main problems of data processing - accuracy and speed - with large dimensions of processed arrays can be solved by developing accurate and fast algorithms and using new info communication technologies on which these algorithms are implemented. The present work is devoted to solving these two problems. A nuclear clustering method is proposed within the framework of structural or data mining (Data Mining) and the possibilities of its operation in a distributed environment are evaluated. Indeed, the emergence of modern technologies for structural data analysis Data Mining is associated with a new round in the development of data processing tools and methods. The goal of this approach is to uncover hidden rules an patterns in datasets, since the human brain cannot identify more than two or three relationships, even in small samples. Mathematical statistics often turns out to be effective only for testing hypotheses already obtained by Data Mining, and the formulation of hypotheses is performed automatically in the course of a computational experiment. Today this technology is widely used in business (banking, insurance, trade), telecommunications. Foreign experience shows that the use of Data Mining can give an economic effect (10-70 times) as high as in comparison with the initial costs. [ABSTRACT FROM AUTHOR]

Data analytic approach has been widely used in combining with the traditional method in order to improve the accuracy of forecasting, especially in tourism industry. Currently, the common used approach is known as data mining technology, which can dramatically accelerate the recovery process of the tourism industry after the outbreak of COVID-19. The purpose of this paper is to review the implementation of data mining technology in tourism industry based on different case studies that were collected through selected digital databases. Based on the literature reviews, there is no doubt that data mining technology has the potential to be extremely important in tourism research. This technology can help the industrial practitioners make wise decision in future since it provides an efficient approach for identifying and comprehending customer behavior's pattern. It is hoped that this study can contribute as a reference for the future studies and are able to help destinations and tourism businesses maintain continuous supplies of tourism products and services in order to fulfill the customer's demand. [ABSTRACT FROM AUTHOR]

The Propositional Satisfiability (SAT) is an optimization problem that give solution in many fields such as planning, verification, and security. The SAT solver and Neural Network are the two greatest achievement in computer science to made significant contributions in the variety of real-world issues. In the Hopfield Neural Network (HNN), non-systematic and systematic SAT used as a theoretical input. This study introduces the systematic logical structure Horn 3-SAT (Horn 3-SAT) in HNN by simply add the number of iterations. The Horn 3-SAT gives solutions in larger and more difficult in the training phase. The Horn3SAT formulas are Boolean expressions with the feature that each sentence can only include one positive variable. They are expressed in conjunctive normal form. The logical framework when all clauses are met, Horn 3-SAT calculates the minimal cost function and saves the truth values of the atoms in neurons. Horn 3-SAT's functionality and scalability in HNN are evaluated using performance metrics. A novel logical system for data mining that combines a dynamic of literals and clauses is often proposed by the Horn 3-SAT formulation. The global minimum of the energy function indicates stable configuration. Modification in the logical structure can increase the quality of the solution by escape from local minima in the searching process of the global optimum. [ABSTRACT FROM AUTHOR]