Data Mining Classification Techniques Research Articles

Klasifikasi User Berdasarkan Trafik Http/Https Menggunakan Metode Naïve Bayes

<p>Along with the times and accompanied by advances in information and communication technology, it is undeniable that at this time all activities use information technology. One of the activities is to use the internet. The research will conduct a classification based on internet usage data obtained through questionnaires using data mining techniques. The attributes that will be used in doing the classification are Name, Age, Gender, Last Education. The method used is the Naïve Bayes method, which is one of the classification techniques in data mining. Based on the research conducted, it was concluded that based on internet user data used as training data, the Naïve Bayes method succeeded in classifying 32 data from 50 data tested. So the Naïve Bayes method succeeded in predicting the magnitude of the percentage of accuracy by 64%.</p><p> </p><p><strong>Keywords : </strong><em>Data Mining, Classification, Naïve Bayes</em></p>

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

A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field

The career opportunities in computer programming are vast and rapidly increasing. Skilled software engineers, programmers, and developers are vigorously in demand worldwide. The capability to forecast a student's future career can be helpful in a wide variety of pedagogical practices. Data mining is becoming a more robust tool for analysis and forecasting. Therefore, to forecast career placement probabilities in the programming field, data mining classification and forecast techniques are used in this study to facilitate prospective students to make sensible career decisions. To achieve this objective, passed-out graduates' data is utilized, which comprises features like graduates' educational attainments in pre-university grades, i.e. grades of matriculation and intermediate, programming courses taught in early semesters along with the Cumulative Grade Point Average (CGPA) with the internship experience, gender, and family demographic information. Various multi-way Classification Trees are generated, which could help students to choose a branch with high career placement probabilities. From historical data, the Classification Trees have determined whether the branch is 'Good', 'Satisfactory', or 'Poor' based on the given information. The experimental findings indicate that all the features significantly influence the career placement probabilities in the programming field.

Comparison of Breast Cancer Classification Using the Decision Tree ID3 Algorithm and K-Nearest Neighbors Algorithm

One of the main causes of death is cancer. The most common cancer in women is breast cancer. Breast cancer (Carcinoma mammae) is defined as a malignant neoplasm originating from the parenchyma. Breast cancer ranks first in terms of the highest number of cancers in Indonesia and is one of the first contributors to cancer deaths. Globocan data in 2020, the number of new cases of breast cancer reached 68,858 cases (16.6%) of the total 396,914 new cases of cancer in Indonesia. Meanwhile, the number of deaths reached more than 22 thousand cases (Romkom, 2022). This death rate is increasing due to lack of information about the early symptoms and dangers of breast cancer itself. From this lack of information, a system is needed that can provide information about breast cancer such as early diagnosis. Classification data mining techniques can be used to predict which patients will develop breast cancer and which do not with several parameters. In this study, a comparison of the classification of breast cancer using the Decision Tree ID3 algorithm and the K-Nearest Neighbors algorithm will be carried out. Attribute data used consists of Menopause, Tumor-Size, Node-Caps, Deg-Malig, Breast, Breast-Squad and Irradiant. The main objective of this study is to improve classification performance in breast cancer diagnosis by applying feature selection to several classification algorithms. The Decision Tree ID3 algorithm has an accuracy rate of 93.333% and the K-Nearest Neighbors algorithm has an accuracy rate of 76.6667%.

The Influence of Village Demographics on the Prediction of Community Participation in Elections Using the Naïve Bayes Algorithm (Case Study: Pacitan City)

Voters' demographic factors have an influence in regional head elections or regional elections. The study will classify based on general election data obtained from villages in sub-districts in Pacitan Regency using data mining techniques. The variables that will be used in classifying villages are TPS, DPT, Attendance and Golput. The method that will be used is the Naïve Bayes method which is one of the classification techniques in data mining. Based on the research conducted, it is concluded that the information system created can classify villages into 2 types, namely low and high by Naïve Bayes method.

Software Effort Estimation using Machine Learning Techniques

Success of the software development companies is mostly dependent on the best effort prediction. If the predictedeffort is somewhat correct, then the company can find relief from the great tension of hurrying up the employees to getthe job done within targeted time. There are many estimation methods, techniques and tools that are available. But itis very difficult to select the best one for a particular project. Each method has its own advantages and disadvantages.And also the effort estimation depends on various parameters. It is the responsibility of the project manager to select thebest tool for his project. Based on the historical data, the project manager can find effort value of the new project afterapplying some statistical methods and data mining techniques on that data. The main aim of this work is to reveal howmuch accurate are data mining-classification techniques on software project effort prediction datasets when we performanalogy based effort estimation.

A Review on Prediction of Diabetes using Machine Learning and Data mining Classification Techniques

A Review on Prediction of Diabetes using Machine Learning and Data mining Classification Techniques

A review on prediction of diabetes using machine learning and data mining classification techniques

Machine learning (ML) and data mining (DM) techniques have grown in popularity among researchers and scientists in various fields. The healthcare industry could not be an exception to it. Diabetes or diabetes mellitus, a gaggle of metabolic disorder, can be caused due to age, obesity, lack of exercise, hereditary diabetes, living style, bad diet, hypertension, etc. and for that, the entire body system can be affected harmfully and be susceptible to dangerous diseases like heart disease, kidney disease, stroke, eye problem, nerve damage, etc. For this, we tried to go for a systematic review on diabetes by applying ML and DM classification algorithms for prediction and diagnosis. Concerning the sort of knowledge, medical datasets as well as Pima Indian Diabetes Datasets (PIDDs) provided by the UCI-ML Repository were mainly used. This survey may be useful for further investigation in predictions and resulting valuable knowledge on diabetes.

Analyzing soil fertility using data mining techniques

Data mining classification techniques play a significant role in agriculture. To increase yield production, many parameters are responsible like weather, fertilizers, pesticides, and soil. Soil is an important key aspect of agriculture, as an increase in yield can only be attained by ensuring that the soil provides a balanced and adequate supply of nutrients. The main aim of the work is to predict soil fertility using data mining classification techniques. Soil fertility is predicted using data mining classification techniques such as J48, Random Forest, Decision Table, PART, and Naive Bayes. These classifier algorithms are used to extract soil fertility knowledge from soil data. A comparison of different classifier algorithms concerning prediction accuracy revealed that the Random Forest is the best classifier to classify the soil fertility dataset. The Random Forest classifier algorithm can produce more consistent results of soil data for the Anand District of Gujarat State and the Kappa Statistics in the prediction were improved.

Penerapan Algoritma Decision Tree Untuk Penentuan Pola Penerima Beasiswa KIP Kuliah

The Indonesian Smart College Card (KIP Lecture) is a government program that has been implemented from 2020 until now. KIP Lectures are distributed by the Ministry of Education, Culture, Research and Technology through universities in each region. Where each university gets a different quota - based on the level of progress of the college. The provision of quotas for each university based on the accreditation at each university raises its own problems for these universities. The problem faced is that the number of new prospective students who register to take the KIP Lecture program exceeds the quota set for each university. The provision of KIP Lecture assistance to the wrong person will lead to misuse of assistance and also inappropriate targets. The acceptance of the selection process for new prospective students can be seen from the previous process that has been carried out. Data mining is a technique used to solve problems in large data processing. Decision Tree is an algorithm that is included in the classification technique in data mining. The process in the decision tree aims to group or classify data against their respective classes. The results of the Decision Tree algorithm are in the form of decision trees and rules, the results obtained are in the form of rules that can be used for future decision-making processes

Comparative Analysis of Data Mining Techniques to Predict Cardiovascular Disease

Cardiovascular disease is the leading cause of death. In recent days, most people are living with cardiovascular disease because of their unhealthy lifestyle and the most alarming issue is the majority of them do not get any symptoms in the early stage. This is why this disease is becoming more deadly. However, medical science has a large amount of data regarding cardiovascular disease, so this data can be used to apply data mining techniques to predict cardiovascular disease at the early stage to reduce its deadly effect. Here, five data mining classification techniques, such as: Naïve Bayes, K-Nearest Neighbors, Support Vector Machine, Random Forest and Decision Tree were implemented in the WEKA tool to get the best accuracy rate and a dataset of 12 attributes with more than 300 instances was used to apply all the data mining techniques to get the best accuracy rate. After doing this research people who are at the early stage of cardiovascular disease or probably going to be a victim can be identified more accurately.

Predicting Malnutrition Status of Under-Five Children in Dhamar Governorate, Yemen Using Data Mining Techniques

Malnutrition is characterised by the insufficient intake of certain nutrients and the inability of the body to absorb or use these nutrients. This health problem keep going to be a real challenge among children under five years of age in developing countries, including Yemen, despite good aids provided. So, malnutrition is a health problem that significantly participates to child mortality rate in Yemen. The overall prevalence of malnutrition among children in Dhamar Governorate has significantly higher rates compared to other Yemeni governorates.
 In this paper, an intelligent predictive system using data mining classification techniques such as J48 decision tree, Bagging and Multi-Layer Perceptron Neural Network (MLPNN) for predicting malnutrition status of under-five children in Dhamar Governorate is proposed.
 The main objective of the present paper is to study these classification techniques to predict the 2018-2019 Dhamar Governorate, Yemen Demographic and Health Survey (DGYDHS) dataset and find an efficient technique for prediction. This dataset is imbalanced, so Synthetic Minority Over-sampling TEchnique (SMOTE) is utilised to balance the dataset.
 The obtained results were evaluated by the famous performance metrics like Accuracy, TP (True Positive)-rate, FP (False Positive)-rate, Precision, F-Measure, Receiver Operating Characteristics (ROC) graph and execution time. The obtained results revealed that the three classifiers with all attributes have higher predictive accuracy and are generally comparable in predicting malnutrition cases.

APPLICATION OF CLASSIFICATION ALGORITHM FOR SALES PREDICTION

Increasing sales results is a desired target for all companies both at home and abroad. The company has a wide variety of products to offer. This paper (to fulfill a Business Intelligence course assignment) is the result of an experiment from data (keaggle) about consumer demand for products during the 2013-2015 period, then based on this data we try to predict to classify product sales, in order to make it easier for companies to classification for sales predictions. To find out the sales of the best-selling products, data mining classification techniques are used, namely XGBoost, Decision Tree, Random Forest, Linear Regression, and Nave Bayes. Based on the test results of the five classification techniques, the XGBoost model is the best with the data training value producing an RMSE value of 0.68% and data testing of 0.79%. This method is also better than the results of previous studies.

Analisis Penerapan Normalisasi Data Dengan Menggunakan Z-Score Pada Kinerja Algoritma K-NN

The large volume of information in the data causes a lot of data to be stored in the dataset. The dataset consists of various attributes and attribute values which contain information stored in the dataset. Data mining is a process that can be used to search for information on datasets. However, the problems encountered in the dataset are often found to have abnormal data such as the range of values that are too far and different between dataset attributes. The value range that is too far causes the results of the information obtained to be not optimal, in data mining itself the process or results are good based on the quality of the data stored in the dataset. Data normalization is a preprocessing stage, where data normalization is scaled back to the range of values in the attribute. Z-Score Normalization is a statistical technique that can be used in data mining to preprocess data by performing data transformations. Z-Score Normalization can be combined with data mining classification techniques, where the role of Z-Score Normalization is to normalize data which is useful for improving the performance of data mining classification algorithms, especially the K-NN algorithm in this study. The results of the study show that Z-Score Normalization is useful for improving performance than the K-NN algorithm. This can be seen from the increase in the accuracy value obtained from the K-NN process before normalizing the dataset and after normalizing the dataset. The accuracy values respectively before normalizing the dataset were 95.13%, 95.83%, 96.11%, 95.77% and 95.81% after normalizing the dataset there was an increase in the accuracy value, namely 97.87%, 98, 57%, 98.77%, 97.23% and 98.11%.

Comparative Analysis of Indonesian Text Mining News Online Classification Using the K-Nearest Neighbor and Random Forest Algorithm

The rapid development of internet technology today makes many news media grow pretty rapidly. Newspaper companies have utilized internet technology to spread the latest news online through online mass media. Hundreds of thousands of stories are written and published daily on online-based Indonesian news portals, making it difficult for readers to find the news topics they want to read. In making it easier for readers to find the news they are looking for, news needs to be classified according to its respective categories, such as education, current news, finance, and sports. So to classify categories, a text classification method is needed or often called Text Mining. Text mining is a data mining classification technique for processing text using a computer to produce helpful text analysis. In this study, a comparison of 2 methods for developing texts was carried out to get accuracy above 80%.

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques.

Breast cancer must be addressed by a multidisciplinary team aiming at the patient's comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.

Machine Learning Architecture for Heart Disease Detection: A Case Study in Iraq

In recent years, the amount of data has been increased dramatically, driven by many real-world fields such as marketing, learning, social media, multimedia, medicine…etc. Because of that, data mining algorithms have extensively used on these data to serve as one of the newest data modeling and analytical tools, by which, a knowledge-rich environment can be generated and decision-making can be improved. Data mining tools can be employed for reducing these tests and predicting future trends by valuable information-driven decisions. There are two categories of data mining algorithms: descriptive and predictive. The rules of clustering, association, summarization, and sequence discovery will be associated with descriptive type. On the other hand, predictive type will compromise classification, regression and time series analysis rules. In this paper, a study have been presented for helping specialists and physicians in Iraq to investigate heart problems via (Weka 3.8.3) software focusing on four data mining classification techniques (1BK, J48, Naïve Bayes and REPTREE). The predictive precision tests, the ROC curve, and the AUC value are calculated using a compiled dataset that have been obtained from the hospital of Ibn al-Bitar and the hospital of Baghdad medical city. The performance of the J48 technique (94.5%) indicates optimum performance based on SMO no performance factor of Baghdad medical city.