Data Mining Classification Techniques Research Articles

PERFORMANCE COMPARISON OF DECISION TREE AND LOGISTIC REGRESSION METHODS FOR CLASSIFICATION OF SNP GENETIC DATA

This research was conducted to compare the accuracy when decision tree and logistic regression methods are used on some data. Decision tree is one method of classification techniques in data mining. In the decision tree method, very large data samples will be represented as smaller rules, and logistic regression is a method that aims to determine the effect of an independent variable on other variables, namely dichotomous dependent variables. Both algorithms were written and analyzed using R software to see which method is better between the decision tree method and the logistic regression method applied to SNP (Single Nucleotide Polymorphism) genetic data, namely Asthma data. SNP Genetic Data was obtained from R software with the package name "SNPassoc" and the data name "asthma". Asthma data has 57 features, namely Country, Gender, Age, BMI, Smoke, Case control, and SNP (Single Nucleotide Polymorphism) genetic code. Comparative analysis was carried out based on the results of the accuracy values obtained in the two methods. Variations in the proportion of the test data used were 40%, 30%, 20% and 10% and were simulated 1000 times on the grounds of obtaining a better accuracy value. The results obtained show that the decision tree method obtains an accuracy value of 0.5793, 0.5777, 0.5745, 0.5526, respectively, while the logistic regression method is 0.7696, 0.7729, 0.7763, 0.7788, respectively and they are achieved at the proportion of test data of 40%, 30%, 20%, 10%. Thus it can be concluded that in this case the logistic regression method is better than the decision tree method in classifying Asthma data.

Crook node identification system in VANET using CRF based feature selection and data mining classification techniques

Intelligent Transportation Systems have become integral to daily life, with VANETs (vehicular ad-hoc networks) playing the pivotal role. VANETs, the subsets of MANETs, employ vehicles as nodes to establish intelligent transport systems. However, due to critical applications such as military use, these networks are susceptible to attacks. With features like high mobility, dynamic network topology, and coverage issues, security breaches are a concern. This necessitates a secure routing algorithm to mitigate attacks and ensure message delivery. In our study, we utilize the UNSW-NB15 intrusion detection dataset to develop training and testing models. Our proposed novel intrusion detection system employs a feature selection algorithm that prioritizes significant arriving traffic attributes. This algorithm enhances abnormal activity detection while minimizing associated features. To achieve this, we modify the Conditional Random Field algorithm with fuzzy-based rules, resulting in a more efficient selection of influential and contributing features for detecting attacks such as DoS, Worms, Fuzzers, and Shellcode. Through appropriate feature selection using the modified Conditional Random Field and Support Vector Machine classification system in our experiments, we demonstrate a notable increase in security by reducing the false positive rate. Additionally, our approach excels in detecting accuracy of Fuzzers (98.86%), DoS (98.80%), Worms (34.45%), and Shellcode (89.308%), ultimately enhancing network performance. These findings underscore the effectiveness of our proposed method in enhancing intrusion detection and overall network efficiency.

Prediction of Grade Point Average (GPA) for Students at Informatics and Computer Engineering Education – Universitas Negeri Jakarta during Online Learning Using Naive Bayes Algorithm

The transition of learning models from face-to-face to online learning has had several impacts on student learning, reflected in their academic achievements. This study aims to determine the performance of the algorithm model using data mining classification techniques in predicting the Semester Grade Point Average (GPA) of Informatics and Computer Engineering Education students, at Universitas Negeri Jakarta during online learning. The prediction employed the Naive Bayes algorithm and the dataset obtained by collecting questionnaires from 2020 and 2021 batches. The total data obtained is 155 records with 13 (thirteen) attributes in the form of 1 (one) ID attribute including NIM, 11 (eleven) regular attributes including gender, college entrance, smartphone facilities, network conditions, preferred online applications, interest in learning, learning attitudes, learning creativity, parental support, study groups, and other activities outside of lectures during online learning, and 1 (one) the label attribute namely the Semester Grade Point Average for students in 3rd and 5th semester. The evaluation of this research involved the confusion matrix and the ROC (Receiver Operating Characteristic) curve. Confusion matrix resulted in an accuracy of 75%, precision of 28.33%, and recall of 26.43%. The ROC curve resulted in an AUC value of 0.679, indicating the category of poor classification. This study also applied the SMOTE data balancing technique, leading to a confusion matrix evaluation with 88.46% accuracy, 57.43% precision, and 52.14% recall. Furthermore, the ROC curve resulted in an AUC value of 0.809 which is categorized as a Good classification.

DEVELOPMENT OF AN ENHANCED C4.5 DECISION TREE ALGORITHM USING A MEMOIZED MAPREDUCE MODEL

Classification technique in data mining concentrates on the prediction of categorical or discrete target variables which is designed to be handled by the classical C4.5 decision tree algorithm, an algorithm whose aim is to produce a tree which accurately predicts the target variable for a new unseen data. However its recursive nature poses a limitation when huge volume of dataset is involved; making computation more complex and resulting in an inefficient implementation of the algorithm in terms of computing time, memory utilization and data complexity. Meanwhile, several researches have been done to control these limitations. One of such improvements is the parallelizing of the algorithm using the MapReduce model. This involves dividing the large dataset into smaller units and sharing them on multiple computers for parallel processing, but the recursive nature of the algorithm makes the cost of computing large number of repeated calculations quite high, which is our concern in this work. . This research is aimed at reducing computation time further, by using a memoized MapReduce model, which involves the saving of the result of previous calculations in a cache; hence, when same calculations are encountered again, the cached result is returned, thus re-computation is avoided. The cached result is considered a reduced cost compared to the computational cost of re-computation.

Performance Improvement of K-Nearest Neighbor Algorithm in KIP Scholarship Recipient Selection

Abstract
 Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are indeed eligible for KIP scholarships, it is necessary to classify scholarship recipients with data mining classification techniques correctly. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.

Classification of Data Mining Techniques under the Environment of T-Bipolar Soft Rings

Data mining evaluation is very critical in the sense that it determines how well a classification model performs and how well it can generate accurate predictions on brand-new, unexplored data. It is especially important for classification tasks. There are several methods for evaluating classification models, and the choice of evaluation strategies depends on the particular situation, the available data, and the desired outcomes. The notion of a T-bipolar soft set (TBSS) is a valuable parameterization tool and is closer to the concept of bipolarity. Moreover, algebraic structures like groups, rings, and modules, etc., are basic tools that can be helpful not only in mathematics but also in other scientific areas due to their symmetric properties. In this article, based on the novelty of TBSS and the characteristics of rings, we have generalized these two notions to deliver and introduce the notion of T-bipolar soft rings (TBSRs). Additionally, the concepts of AND product, OR product, extended union, extended intersection, restricted union, and restricted intersection for two TBSRs is introduced, and the related results are conferred. To support these proposed notions, we have delivered examples related to these ideas. For the applicability of the developed approach, an algorithm is defined based on the delivered approach. An illustrative example regarding the classification of data mining techniques is developed to show the applications of the introduced work. We can see that there are four alternatives, and their score values are, respectively, given by −4, 42, 0, and −32. Based on these results, we can evaluate the best data mining technique. So, the defined algorithm makes it easy for us to classify the data mining techniques. Further asymmetric data are frequently employed for selecting the best alternative in decision-making problems because the information regarding alternatives is not necessarily always symmetric. Therefore, asymmetric information can be discussed using these proposed concepts.

Comparison Of K-Nearest Neighbor And CNN Classification Methods In Diabetic Data Sets

The number of diabetics worldwide is projected to increase by 204 million (48%), from 425 million in 2017 to 629 million in 2045. Indonesia ranks sixth out of ten countries with the most number of diabetics in the world or 10 million people. The majority of people with diabetes are between 20 and 64 years old, or 327 million people, compared to 123 million people between 65 and 99 years old. The incidence of diabetes increases by about 4.8% at the age of 55-64 years, and women (1.7%) suffer from diabetes more than men (1.4%). Therefore, the authors will create a program to determine the patient's diabetes. One approach is to use machine learning as a data mining classification technique. The author will do a classification comparison with the two methods, namely the KNN and CNN methods to provide the best results of the two methods for testing. So that the accuracy of the data from the diagnosis and photo images of the disease can be known to provide early treatment before the severity of the disease.

Naive Bayes and Decision Tree Algorithms for BRI Life Sharia Insurance Product Classification

Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are eligible for KIP scholarships, it is necessary to classify scholarship recipients correctly using data mining classification techniques. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.

Risk Prediction of Thalassemia Using Data Mining Classifiers

Medical data mining is concerned with prediction knowledge, which is a useful method for extracting hidden patterns from given data for specific purposes. Thalassemia is one of the most common inherited blood hematological disorders, and this paper adopted data mining classification techniques to generate results with high performance and accuracy for risk prediction of thalassemia. The dataset for this purpose was collected from NIBD (National Institute of Blood Diseases), a well-known institute and hospital for blood diseases in Karachi, Pakistan. They provided 301 records of CBC test reports containing positive and negative statuses of diagnosis of thalassemia traits. There were many instances in the report, of which 6 were used for our research purpose, i.e. Gender, MCV, HGB, HCT, MCHC, and RDW. The dataset was divided into training and test data using the WEKA tool. Four algorithms of data mining classification, namely J48 Decision Tree, Naïve Bayesian Network, SMO algorithm, and Multilayer Perceptron Neural Network were adopted to train the model and classify the patient having traits of thalassemia from normal persons with the use of the WEKA tool. Results revealed that out of all four algorithms, Naïve Bayes provided results with the highest accuracy of 99%.

Improving The Performance of the K-Nearest Neighbor Algorithm in the Selection of KIP Scholarship Recipients

Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are indeed eligible for KIP scholarships, it is necessary to classify scholarship recipients with data mining classification techniques correctly. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.

IMPLEMENTASI DATA MINING DENGAN ALGORITMA NAIVE BAYES UNTUK KLASIFIKASI KELAYAKAN PENERIMA BANTUAN SEMBAKO

The percentage of the population receiving assistance was 5.19%, this figure increased compared to March 2022, while the line of aid recipients was recorded in 2022 of 783,730 people per capita per month. The number of poor households in Batu Aji District is Bukit Tempayang Village with 15,857 inhabitants, 300 poor households, 37,531 inhabitants in Buliang Village, 289 poor households, 28,693 The beneficiary of assistance is an inability to meet basic needs including food, clothing, education and housing. Thus it is necessary to carry out a strategy in dealing with the level of beneficiaries, namely by providing accurate and targeted data on beneficiaries. Naive Bayes is an algorithm that exists in data mining and is part of the data mining classification technique by using probability and statistical techniques to estimate or predict opportunities that will occur based on previous opportunities, namely there are two feasible and inappropriate classes. The results of the classification that will be carried out later will help in the processing of assistance to help make decisions regarding the classification of determining basic food recipients. And by testing calculations manually and using rapid minner software, you get an accuracy value of 80%.

Predictive Model of Psychoactive Drugs Consumption using Classification Machine Learning Algorithms

It is difficult to predict the effect of drugs on the individuals, as its results are unpredictable and most often dangerous. For a police purpose that concerned with the protection of individuals, the problem of predicting drug abusing is highly important. A dataset was used from open-source website UCI, that includes specific attributes about using up of eighteen different psychoactive drugs. Our study aimed to use data mining classification techniques, in order to classify the individual into two categories: user or non-user. Eighteen classification models were built using different classification algorithms such as Gaussian Naive Bais, Logistic Regression, k-nearest neighbors, Random Forest, and Decision Tree. The accurate classifier was chosen by studying the accuracy, recall, precision, and f1-score measures for each one, and it was evaluated by the Holdout method. The results were obtained optimally, and we got 18 models, where each one had different high accurate outputs, that classify an individual to user and non-user. The final model is a combination of 18 models for 18 critical psychoactive drugs: Alcohol, Amphet, Amyl, Benzos, Caff, Cannabis, Choc, Coke, Crack, Ecstasy, Heroin, Ketamine, Legalh, LSD, Meth, Mushrooms, Nicotine and VSA. This study in turn may give a chance for the decision makers to reduce the risk of these drugs consumption, in order to avoid healthcare issues and keep the community in safe.

Predicting the Probability of Diabetes using Data Mining Techniques

In recent years, diabetes has become a common disease, and the number of people suffering from diabetes is huge, and the prevalence and mortality rate are on the rise. Therefore, the prevention of diabetes is an important task. Data mining will play a significant role in the prevention of diabetes by predicting and analyzing the risk of diabetes in various persons with various features. We chose the Pima Indian diabetes dataset for this study, and based on a supervised learning machine learning algorithm, which is one of the simplest, most commonly used and most effective algorithms in data mining classification techniques, namely the KNN algorithm, we used data mining to make a predictive research analysis of diabetes based on the combination of disease characteristics and to predict the probability of diabetes occurrence.

Implementation of Particle Swarm Optimization Feature Selection on Naïve Bayes for Thoracic Surgery Classification

Thoracic surgery is among the operations that are most often performed on patients with lung cancer. Naive Bayes is one of the data mining classification techniques that may be used to handle thoracic surgery data. Therefore, the goal of this study is to assess the precision of all research models using Naive Bayes with and without Particle Swarm Optimization. This study's methodology includes the dataset used, the Naive Bayes algorithm theory, the particle swarm optimization algorithm, test validation using split validation, and performance assessment using the confusion matrix and AUC evaluation approaches. In this inquiry, secondary data are retrieved via the UCI Repository website. Thoracic surgery weight optimization accuracy is increased using particle swarm optimization. The test results of the Naive Bayes technique utilizing the thoracic surgery dataset showed the highest accuracy of 81.91% at a ratio of 80:20 and an AUC value of 0.620. The highest accuracy score is 93.62% with an AUC value of 0.773 at a ratio of 90:10, with three characteristics, namely PRE6, PRE14, and PRE17, having zero weight. This accuracy score was achieved when Particle Swarm Optimization was used to refine feature selection for attribute weighting. As a consequence, Naïve Bayes accuracy in thoracic surgery has increased as a result of attribute weighting on feature selection utilizing Particle Swarm Optimization. In turn, this research contributes to increasing the precision and efficiency with which thoracic surgical data are processed, which benefits lung cancer diagnosis in both speed and accuracy.

Classification of Public Sentiment on Fuel Price Increases Using CNN

The government's policy of changing fuel prices is carried out every year. The public gave responses to this policy categorized as positive, negative, or neutral sentiments. The community's response was conveyed through tweets on the Twitter application. Based on the public's response to the policy, sentiment classification can be done using data mining classification techniques. Some research has been carried out on classification techniques using deep learning and machine learning methods. In general, deep learning methods get better results, and this research will be approached using the CNN method. The system stages start from crawling data, labeling, and preprocessing, which consists of cleaning, case folding, tokenization, normalization, removing stopwords and stemming, classification using CNN, and evaluation using 10-Cross Validation. The dataset used is 17.270. The results show that the developed classification system is relatively high, with the highest accuracy of 87%, 93% recall, 93% precision, and 90% F1 score. An in-depth analysis of the classification results and an understanding of sentiment toward rising fuel prices can also provide valuable insights.

Proactive Ransomware Detection Using Extremely Fast Decision Tree (EFDT) Algorithm: A Case Study

Several malware variants have attacked systems and data over time. Ransomware is among the most harmful malware since it causes huge losses. In order to get a ransom, ransomware is software that locks the victim’s machine or encrypts his personal information. Numerous research has been conducted to stop and quickly recognize ransomware attacks. For proactive forecasting, artificial intelligence (AI) techniques are used. Traditional machine learning/deep learning (ML/DL) techniques, however, take a lot of time and decrease the accuracy and latency performance of network monitoring. In this study, we utilized the Hoeffding trees classifier as one of the stream data mining classification techniques to detect and prevent ransomware attacks. Three Hoeffding trees classifier algorithms are selected to be applied to the Resilient Information Systems Security (RISS) research group dataset. After configuration, Massive Online Analysis (MOA) software is utilized as a testing framework. The results of Hoeffding tree classifier algorithms are then assessed to choose the enhanced model with the highest accuracy and latency performance. In conclusion, the 99.41% classification accuracy was the highest result achieved by the EFDT algorithm in 66 ms.

The Study of Neural Network Algorithm, Random Forest for Classification of Student Graduation

The academic performance is one of aspect which has remained the bechmark of the success in learning activities at an university. The indicator of academic performance in the university is the students able to complete their studies on time. Unfortunately, the problem regarding academic performance was associated with the completion time of student studies in Faculty of Economics, University of Garut. In this research explore the model that able to classify the graduation of student through the data mining classification technique by comparing the Neural Network Algorithm dan Random Forest. The classification conducted by evaluating the academic performance based on Semester Performance Index (IPS) first years in the beginning and use the demographics of students as attributes that will be used in the dataset. Based on the results of several model tests from the data train, totaling 1467 data records and 25 attributes. It shows that the 14th Random Forest test model produces a Recall Performance value of 72.70%, 74.70% for Accuracy Performance, 72.80% for Precision Performance and 74.70% for F-measure Performance.

Sentiment Analysis of Visitor Reviews on Star Hotels in Manado City

Sentiment analysis is a technique of extracting the text data to analyze the opinions and evaluate to obtain the information. Sentiment analysis is performed by internet users on social media or online applications or websites to provide assessments or personal opinions. Tourism in North Sulawesi has grown by 600% in the past four years, and the rise of tourism has sent tourists flocking to the city of Manado. These travelers need a hotel that satisfies their desires, so they need to read about the hotel in the reviews on the hotel reservation service website. This takes a lot of time. To overcome existing problems, sentiment analysis applications were developed to make it easier for potential hotel users to find previous user responses. Additionally, data mining classification techniques are used to help hotel managers determine the satisfaction of previous hotel users using a Naive Bayes algorithm. The five tests performed gave the best result, 76.20% accuracy, with an average of 70.55%. While the average precision is 70.57% and 99.85% for the recall.

Comparison of Adaboost Application to C4.5 and C5.0 Algorithms in Student Graduation Classification

Students become a benchmark used to assess quality and evaluate college learning plans. Therefore, students who graduate not on time can have an effect on accreditation assessment. The characteristics of students who graduate on time or not on time in determining student graduation can be analyzed using classification techniques in data mining, namely the C4.5 and C5.0 algorithms. The purpose of this study is to compare the application of the Adaboost Algorithm to the C4.5 and C5.0 Algorithms in the classification of student graduation. The data used is the graduation data of students of the Statistics Study Program at Tanjungpura University Period I of the 2017/2018 Academic Year to Period II of the 2022/2023 Academic Year. The analysis begins by calculating the entropy, gain and gain ratio values. After that, each data was given the same initial weight and iterated 100 times. Based on the classification results using the C5.0 Algorithm, the attribute that has the highest gain ratio value is school accreditation, meaning that the school accreditation attribute has the most influence in the classification of student graduation. The application of the Adaboost Algorithm to the C5.0 Algorithm is better than the C4.5 Algorithm in classifying the graduation of students of the Untan Statistics Study Program. The Adaboost algorithm was able to increase the accuracy of the C5.0 Algorithm by 12.14%. While in the C4.5 Algorithm, the Adaboost Algorithm increases accuracy by 10.71%.

Comparing the reliability of classical statistics and data mining techniques in unit energy prediction in circular stone cutting

Comparing the reliability of classical statistics and data mining techniques in unit energy prediction in circular stone cutting