Bayes Classifier Method Research Articles

Penerapan Data Mining Untuk Memprediksi Daya Serap Lulusan Siswa Menggunakan Algoritma Native Bayes

The importance of predicting the absorption of Vocational High School (SMK) graduates in the world of work, especially SMK Negeri 9 Muaro Jambi which is not yet known about the prediction of the world of work that accepts SMK graduates so that the purpose of this study is to analyze the prediction of the accuracy of the absorption of graduates of SMK Negeri 9 Muaro Jambi as material. a reference to see whether the graduates of SMK Negeri 9 Muaro Jambi have achieved the expected goals or not so that this analysis can be used as input for schools to improve the competence of SMK students. This implementation is assisted by using the Rapidminer and WEKA applications with 100 alumni work data input. The attributes used in this analysis process are Department, Waiting Time and Field of Work and Class of Work Field Accuracy. The process in this analysis is carried out with data that has been provided with the Naïve Bayes Classification Method to predict the absorption of graduates. The results of this study the highest accuracy value in the Rapidminer application is at 100% and WEKA is at 100%.

Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

This article use Systematic Literature Review (SLR) to classify sentiment analysis based on case studies, methods, social media, and platforms. The coding stage is divided into three stages, namely the open, selective and axial coding. The literature study on sentiment analysis is divided into two parts: identifying gaps based on case studies and data sources and identifying gaps based on the methods or algorithms used. The gap identification results based on case studies and data sources show that popular review topics are synonymous with entertainment, economic and political content. Therefore, the quantity of research with review topics related to the implementation of education, the dynamics of the bureaucracy, health facilities and services, and non-governmental organization’s activities need to be increased. Meanwhile, the most dominant platforms used as data sources are website and mobile-based applications. The results of the gap identification based on the method and algorithm show that the quantity of research with the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) method or algorithm is more dominant than the k-Nearest Neighbor (k-NN) and Lexicon-based. Thus, it is necessary to increase the number of other classification methods such as Particle Swarm Optimization, BM25, Decision Tree, K-Means, and Neural Networks.

The Classification of Taekwondo Kicks Via Machine Learning: A feature selection investigation

Martial art strike classification by machine learning has drawn more attention over the past decade. The unique signal from each technique makes it harder to be recognized. Thus, this paper proposed an SVM, Random Forest, k-NN, and Naïve Bayes classification method applied to the time-domain signal to classify the three type of taekwondo technique. Data collected from the total of five participant and statistical features such as mean, median, minimum, maximum, standard deviation, variance, skewness, kurtosis, and standard error mean were extracted from the signal. After that, the data will be trained using selected rank features and hold-out method with k-fold cross-validation applied to the training and testing data. Therefore, with ANOVA test as features selection and 60:40 ratio of a hold-out method, Random Forest classifier score the highest accuracy of 86.7%..

COMPATIBILITY OF SELECTION OF STUDENT DEPARTMENTS USING k-NEAREST NEIGHBOR AND NAÏVE BAYES CLASSIFIER IN INFORMATICS PRIVATE VOCATIONAL SCHOOL, SERANG CITY

Selection of majors by prospective students when registering at a school, especially a Vocational High School, is very vulnerable because prospective students usually choose a major not because of their individual wishes. And because of the increasing emergence of new schools in cities and districts in each province in Indonesia, especially in the province of Banten. Problems experienced by prospective students when choosing the wrong department or not because of their desire, so that it has an unsatisfactory value or value in each semester fluctuates, especially in their Productive Lessons or Competencies. To provide a solution, a departmental suitability system is needed that can provide recommendations for specialization or major suitability based on students' abilities through attributes that can later assist students in the suitability of majors. The process of classifying the suitability of majors in data mining uses the k-Nearest Neighbor and Naive Bayes Classifier methods by entering 16 (sixteen) criteria or attributes which can later provide an assessment of students through this test when determining the majors for themselves, and there is no interference from people. another when choosing a major later. Research that has been carried out successfully using the k-Nearest Neighbors method has a higher recall of 99%, 81% accuracy and 82% precision compared to the Naïve Bayes Classifier whose recall only yields 98% while the accuracy and precision is the same as the k- Nearest Neighbors.

IMPLEMENTASI METODE NAÏVE BAYES CLASSIFIER UNTUK KLASIFIKASI STATUS GIZI STUNTING PADA BALITA

Stunting describes a state of chronic malnutrition during growth and development since early life. This situation is represented by the height z-score for age (TB/U), which is less than minus 2 standard deviations (SD), based on WHO growth standards.Data from the Semarang City Health Office stated that the results of monitoring nutritional status based on indicators of body length for age (PB/U) or height for age (TB/U) the incidence of stunting in the city of Semarang was 20.37%. This research will make it easier to determine information regarding the classification of stunting nutritional status in toddlers. Stunting data will be processed and used as information regarding normal or not stunting nutritional status in toddlers. With this information, it can make it easier to collect data on toddlers who experience stunting nutritional status, besides that it can also be used to hold counseling to increase stunting nutritional levels and prevent stunting in toddlers by using the Naive Bayes Classifier. The accuracy result of the Naive Bayes Classifier method in classifying stunting nutritional status is 88%

Measure the effectiveness of information systems with the naïve bayes classifier method

<span id="docs-internal-guid-a31a7709-7fff-65df-d360-f52175b11d47"><span>Technological advances at this time are developing very fast, information systems became the frontline in technological advancements, the need for information systems to support jobs is increasingly high. However, its implementation for users does not have a significant impact, so that it needs to be reviewed and re-evaluated in the use of the information system built. The naive bayes classifier method can provide "effective" and "ineffective" conclusions and is used as material for evaluation and improvement. The purpose of this study is to contribute to measuring the effectiveness of the information system, to solve problems with the naïve bayes classifier method approach which has advantages in the process of classifying data and predicting data. From the test results three times, training has been conducted using 100 data, accuracy value of 84.82% and error 15.18%.</span></span>

IMPLEMENTATION OF TEXT MINING FOR CLASSIFYING COMMUNITY QUESTIONS VIA WHATSAPP WITH THE NAÏVE BAYES CLASSIFIER METHOD

Text mining is the process of exploring knowledge based on specific patterns of textual data retrieval. There was an increase in the amount of text data from community questions on the Whatsapp information service at Surabaya Immigration Office which can be processed into detailed and complete information. Text data entered through Whatsapp question has also not been classified specifically, structured and also has not been published. This study aims to explain the characteristics of incoming messages provided by the public through the Whatsapp information service and to explain the process of classifying community questions according to the field of immigration public services namely WNI and WNA. The authors used the classification method with the Naïve Bayes Classifier (NBC). Obtained the value of classification accuracy with algorithms and methods using the Naïve Bayes Classifier on the training data equal to 93.5% and testing data equal to 95% that included in the excellent scale. Therefore, Naïve Bayes Classifier method is very well applied for classifying public questions and SIPESAN system.

Classification of Student Aspiration Using Naïve Bayes Classifier

Students aspiration are various demands from the student that packed in creative idea to propose changing process of a thing. Mostly, aspiration delivered in complaints and expectation. Aspiration is used for evaluating the laxity and early detection in university quality system for the better. This activity took place in Dian Nuswantoro University, and Student Representative Council (SRC) is the unit to manage the students aspiration. Aspiration is obtained through predetermined mechanism such as manual questionnaire distribution and or using google form. The provided questionnaire requires student to fill the content according to the provided aspiration categories. However, the problem is sometimes the student choose the wrong category according to the content. Therefore it is needed to create an application that can classified the students aspiration automatically. Document text classification become the best way to determine the category based on the content of the students aspiration. Naïve bayes classifier method is used because it is capable to produce high accuracy. With 1000 data training document of each category, "facilities and infrastructure" (facilities), "lecturers" (attitudes, teaching methods, material delivered), "staffing and the academic system"(attitudes, ways of working, providing information), and "suggestions and feedback". This experiment achieved 90.20% accuracy. It can be said that this method is worth to implement in this research.

Sentiment Analysis on Indonesia Twitter Data Using Naïve Bayes and K-Means Method

This study focuses on the analysis of sentiments on Indonesian twitter data. Twitter data on Indonesian simultaneous pilkada used to get its sentiments using Naïve Bayes Classifier method as a method of classification and K-means method to get Label on the data train process. Combining the two methods is expected to get high accuracy results. The results obtained from the research shows a pretty good accuracy of 74.5%.

Naive bayes methods for rainfall prediction classification in Banyuwangi

Banyuwangi is the largest district in East Java with an area of 5,782.50 km2. It has a long coastline of about 175.8 km which stretches along the southern eastern boundary of Banyuwangi Regency, and there are 10 islands. The BMKG estimates that the dry season in the Banyuwangi area is due to the appearance of the beach having hot weather and rarely rains. Banyuwangi also predicts that the dry season is due to the slight influence of cloud growth. Rainfall is a factor of the rainy season which has a big influence on life such as aviation, plantations and agriculture. Agriculture and plantations in Banyuwangi are mostly located in remote areas. Remote areas are likely to lack weather and climate data information. climate elements of a region cannot be ignored, especially rainfall. Based on data from BMKG (Meteorology, Climatology and Geophysics), the weather data used needs to be classified. Rainfall classification can be categorized into three, namely, light, normal and heavy. There are quite a lot of classification methods, there are several new methods that are quite good such as Naive Bayes (NB). Naive Bayes Classifier (NBC) is an algorithm in data mining techniques that is used to determine the probability of a member of a group. Large and irrelevant datasets can be solved using the Naive Bayes Classifier (NBC) method. The rainfall data used is known first, observed then identified to form a training dataset. Determining the accuracy of rainfall with the Naive Bayes Classifier (NBC) can use several parameters that have a physical relationship between the atmosphere and rainfall. The parameters used to determine rainfall are humidity, rainfall and precipitation. From this study, from 49 data testing, 47 data were predicted correctly with an accuracy of 96%.

Determination of near-fault impulsive signals with multivariate na\xefve Bayes method

Near-fault ground motions may contain impulse behavior on velocity records. To calculate the probability of occurrence of the impulsive signals, a large dataset is collected from various national data providers and strong motion databases. The dataset has a large number of parameters which carry information on the earthquake physics, ruptured faults, ground motion parameters, distance between the station and several parts of the ruptured fault. Relation between the parameters and impulsive signals is calculated. It is found that fault type, moment magnitude, distance and azimuth between a site of interest and the surface projection of the ruptured fault are correlated with the impulsiveness of the signals. Separate models are created for strike-slip faults and non-strike-slip faults by using multivariate naïve Bayes classifier method. Naïve Bayes classifier allows us to have the probability of observing impulsive signals. The models have comparable accuracy rates, and they are more consistent on different fault types with respect to previous studies.

Optimization the Naive Bayes Classifier Method to diagnose diabetes Mellitus

World Health Organization (WHO) states that Diabetes Mellitus is the world's top deadly disease. several studies in the health sector including diabetes mellitus have been carried out to detect diseases early. In this study optimization of naive bayes classifier using particle swarm optimization was applied to the data of patients with 2 classes namely positive diabetes mellitus and negative diabetes mellitus and data on patients with 3 classes, those who tested positive for diabetes mellitus type 1, diabetes mellitus type 2 and negative diabetes mellitus. After testing, the algorithm of Naive Bayes Classifier and Naive Bayes Classifier based on Particle Swarm Optimization, the results obtained are the Naive Bayes Classifier method for 2 classes and 3 classes each producing an accuracy value of 78.88% and 68.50%. but after adding Particle Swarm Optimization the value of accuracy increased respectively to 82.58% and 71, 29%. The classification results for 2 classes have an accuracy value higher than 3 classes with a difference of 11.29%

Klasifikasi Data Antroprometri Individu Menggunakan Algoritma Naïve Bayes Classifier

With the development of advances in computer technology today, most companies and organizations need a decision support system based on information systems, where the information is generally stored in the form of documents / text that is not structured. In this regard, a system for text management that is integrated with the decision support system is needed. One of them is the use of text data classification for anthropometric case studies of several samples. Anthropometry is a measurement of a person's body dimensions. The object of research is gender, first name, and height of a person. The research aims to determine the ratio of the number and height probability level of the number of men and women based on the input into an application using the Naïve Bayes Classifier method. The implementation design uses the Python programming language. The results showed that the height classification data frequency of women was more than the height classification data for men. And the number of height probability of a woman's body is greater than the number of height probability of a man's body.

Classification of Anemia with Digital Images of Nails and Palms using the Naive Bayes Method

Purpose: Early detection of anemia based on nails and palms images by applying the Naive Bayes method, as well as to measure the level of accuracy in detecting anemia.Design/methodology/approach: Using the Naive Bayes method. System development uses the waterfall method.Findings/result: Based on the results of the tests that have been carried out, the resulting accuracy is 87.5% with varying light intensities and is 92.3% by using a light intensity of 5362 Lux.Originality/value/state of the art: The difference between this study and previous research is in the image pre-processing method and classification method. In this study, the images of the nails and palms were converted to the YCbCr color space to be segmented and color features extracted. Then the color features will be classified using the Naive Bayes classification method. The output of this system is the result of the input image classification, whether normal or anemic.

Classification of Anemia with Digital Images of Nails and Palms using the Naive Bayes Method

Purpose : Early detection of anemia based on nails and palms images by applying the Naive Bayes method, as well as to measure the level of accuracy in detecting anemia. Design/methodology/approach : Using the Naive Bayes method. System development uses the waterfall method. Findings/result : Based on the results of the tests that have been carried out, the resulting accuracy is 87.5% with varying light intensities and is 92.3% by using a light intensity of 5362 Lux. Originality/value/state of the art : The difference between this study and previous research is in the image pre-processing method and classification method. In this study, the images of the nails and palms were converted to the YCbCr color space to be segmented and color features extracted. Then the color features will be classified using the Naive Bayes classification method. The output of this system is the result of the input image classification, whether normal or anemic.

Naive Bayes Algorithm Implementation Based on Particle Swarm Optimization in Analyzing the Defect Product

In the era of progressively more competitive industrial competition, especially in the manufacturing world, it is always required to develop the quality or quality of products and productivity. Each company is compete to win market share. One of the strategies carried out by the company is improving the quality of products and the production process conducted by the company. In the industrial world, product quality and productivity are the keys for success of the production process. Therefore, the purpose of this study is to analyze data for defective products at PT Mane Indonesia with the Particle Swarm Optimization (PSO) and Naïve Bayes Classifier method. The accuracy results using the Naïve Bayes algorithm get a value of 84.38% and an AUC value of 0.953. The results of the PSO-based Naïve Bayes algorithm get a value of 88.62% and AUC value of 0.945. Based on the research which has been performed by using Naïve Bayes based on PSO, it developed a contribution rate of 5,02% in predicting the defected products.

Analysis of Cantaloupe Fruit Maturity Based on Fruit Skin Color Using Naive Bayes Classifier

The traditional sorting of fruit maturity can be done by seeing the color of the fruit’s skin. Manual sorting will take a long time and the results are subjective. This paper presents the results of maturing cantaloupe fruit based on the color of the fruit skin using a digital image of the fruit skin. The research objective is to classify the maturity of cantaloupe fruit using the Naive Bayes Classifier method and compare the results with similar studies using the Learning Vector Quantization (LVQ) Artificial Neural Network method. This study used the image of a raw and mature cantaloupe rind of 15 images each. A total of 16 images are grouped into training data for the training process and 14 other images are grouped into test data for the testing process. The results showed that the accuracy of training and testing using the Naive Bayes Classifier method was 68.75% and 57.14%, respectively. The accuracy of the training and testing of the Naive Bayes Classifier method turns out to be lower compared to the LVQ Artificial Neural Network method.

PENERAPAN NAÏVE BAYES CLASSIFIER UNTUK PENDUKUNG KEPUTUSAN PENERIMA BEASISWA

Scholarships are the provision of assistance in the form of financial assistance provided to individuals with the aim of being used for the sustainability of the education achieved. The problem that occurs in this research is that the process of determining which is still carried out conventionally the student section must check one by one the scholarship application files submitted by students because each data will be compared one by one according to predetermined criteria, which results in the student section becoming difficult in the decision so that It takes a long time, therefore we need a decision support system that can help schools make decisions about scholarship recipients.The Naive Bayes Classifier method is a method that can be used in decision making to get better results on a classification problem. The purpose of this study is to build a scholarship recipient decision support system using the Naïve Bayes Classifier method. In this study, a problem analysis was carried out using PIECES analysis and for the system development method using.The result of this research is that applying the naïve Bayes method to the scholarship recipient's decision support system can assist the school in determining the scholarship recipient more quickly and accurately. The scholarship recipient's decision support system was built using the Java programming language and MySQL database. Keyword: Decision Support Systems, Naïve Bayes Classifier, Waterfall, Blackbox Testing, PIECES

Naïve Bayes Classifier and Particle Swarm Optimization Feature Selection Method for Classifying Intrusion Detection System Dataset

The security of a network might be threatened by an intrusion aim to steal classified data or to find weaknesses on the network. In general, network main security systems use a firewall to control and monitor both incoming and outgoing network traffic. Intrusion Detection System can be used to strengthen network security. Several data mining methods have been used to solve Intrusion Detection System (IDS) problem on a network. On this paper we will use Naïve Bayes Classifier along with Particle Swarm Optimization (PSO) as the feature selection method specifically on one of the benchmark dataset on IDS problem, KDD CUP’99. The dataset consists of more than 40 features with more than 400 thousands records. To solve IDS problem on the dataset, it needs a quite expensive cost either on time computation or memory usage hence the use of PSO as the feature selection method. The best classification result was reached when we use 38 features where the accuracy is 99.12%. Particle Swarm Optimization method has several parameters that may affect the classification performance. For future improvement, it is possible to use a parameter optimization method to ensure the best classifier performance.

Eggshell crack detection based on the transient impact analysis and cross-correlation method

To explore the transient impact process for cracked eggshell detection, an equivalent mechanical model was built based on a self-designed automatic excitation device. Through analysis of power spectrum from dynamic force signal, it was found that the impact speed affects only the impaction energy. In contrast, the material of the impact head and the weight of the excitation rod determine the energy and the cut-off frequency of the impaction. When the weight of impact tup is less than 6.62 g, the cut-off frequency of excitation impact can cover the egg's inherent frequency. Then, an optimized experiment system was designed to acquire the response acoustic signals. The cross-correlation analysis and Bayes classification methods were carried out to detect the cracked eggshell. In the conducted experiments, a crack detection level of 97% and a false rejection level of 1% were achieved. From the findings, it can be concluded that the proposed method will assist in optimizing the impact device and simplifying the classification algorithm for an online detection system.