Naive Bayes Classifier Method Research Articles

Gömülü ve Sarıcı Öznitelik Seçim Yöntemleri Kullanılarak Akciğer Rahatsızlıklarının Tespiti

Son yıllarda biyomedikal sinyal işleme alanındaki gelişmelere rağmen, akciğer rahatsızlıklarının tespiti üzerine hızlı ve yüksek doğrulukta çalışan teşhis sistemlerine duyulan ihtiyaç artmaktadır. Yapılan çalışmada fiziki muayene ile 94 farklı kişiden, solunum döngülerinin otomatik olarak tespit edilmesiyle elde edilen 150 adet normal ve 444 adet normal olmayan akciğer sesleri veri tabanı olarak kullanılmıştır. Sınıflandırma işleminde öznitelik olarak frekans ve zaman bölgesinde 12 farklı yöntem uygulanmıştır. Tüm veriler %80 eğitim %20 test aşamasında kullanılacak şekilde ikiye bölünmüştür. Elde edilen öznitelikler gömülü ve sarıcı öznitelik seçim yöntemleri kullanılarak değerlendirilmiştir. Bu yöntemler; özyinelemeli öznitelik eliminasyonu, uyarlanabilir yapı öğrenimi ile öznitelik seçimi, bağımlılık kılavuzlu denetimsiz öznitelik seçimi, sıralı yerellik ile denetimsiz öznitelik seçimi, içbükey küçültme yoluyla öznitelik seçimi, en küçük mutlak büzülme ve seçim operatörü öznitelik seçim yöntemleri olarak isimlendirilmektedir. İncelenen bu öznitelikler doğrusal destek vektör makineleri, k en yakın komşuluk, karar ağaçları ve naive bayes yöntemleri ile sınıflandırılmıştır. Sonuç olarak öznitelik sayısının sınırlandırılmadığı durum için, özyinelemeli öznitelik eliminasyonu yönteminin k en yakın komşuluk sınıflandırma ile beraber kullanıldığı durum için %97,3 doğruluk değerindeki başarıma ulaşılmaktadır. Öznitelik sayısının üç ile sınırlandırıldığı durumda ise uyarlanabilir yapı öğrenimi ile öznitelik seçimi yönteminin karar ağaçları yöntemi ile beraber kullanılması ile %91,4 değerinde başarıma ulaşılmıştır.

Prediksi Kelancaran Pembayaran Angsuran Pada Koperasi Dengan Metode Naive Bayes Classifier

This study aims to predict the smoothness of installment payments in cooperatives, making it easier for staff to analyze credit lending. Lack of prudence in analyzing credit results in customers who are in arrears in paying installments, resulting in bad credit. To minimize errors that exist, it is necessary to evaluate the provision of loans to prospective debtors. By utilizing past member criteria data in the past that will be used to predict smooth payments using data mining. The data mining technique used is the Naive Bayes classifier method. The prediction process uses the naive Bayes method, namely by determining the probability or opportunity based on the previous member's data, and the results are used to help make a decision. The criteria used are member data: employment, income, house status, number of credits, and type of credit. Based on the naive Bayes method, the results obtained are 90.00% accuracy, 0.880% AUC, 83,33% recall, and 100% precision.

Analisis Sentiment Pelanggan Terhadap Penilaian Produk Pada Toko Online Shop Amreta Menggunakan Metode Naïve Bayes Classification

Sentiment analysis or opinion mining is an analysis that aims to see the sentiment of people or groups regarding certain entities. The sentiments expressed by society can be in positive, negative and neutral form. One media that can be given an opinion by the public is in the e-commerce application, namely the shopee application, shopee has a comment or assessment feature on the product that has been purchased. Toko which was used as a sample of researchersis an amreta online shop store , based on the results of the identification of the problem, it was found that the fact was that many comments did not match the stars given so it can be said that the rating cannot represent that the store's performance is good or not. Therefore, to increase the profit of shop work, the amreta still needs to evaluate the store. In conducting an evaluation, the store needs to classify positive, negative or neutral comments. Analysis of customer sentiment towards product assessments in amreta online shop stores using the naive bayes classification method. The use of test data in this study was obtained from the sentiment of amreta online shop consumers as much as 2014 data,then the data was processed through the data cleaning process resulting in net data of 1899 data. Furthermore, the data preprocessing process is divided into 3 stages, namely Tokenize Data, Transform case and Stopword removal. After that, the analysis of data for the automatic labeling stage using Text Vectorize from the process obtained data division into 3 data groups of 71% or 1343 positive data, 3% or 52 negative data and 26% or 504 neutral data. furthermore, it is processed using rapidminer tools while for operators in the form of algorithms using the Sentiment Naïve Bayes Classification model through automatic calculations. The results of the study can be concluded that the test data obtained have an accuracy level of 97.16% using the Naive Bayes Classification model.

SENTIMENT ANALYSIS FOR EXTRACTING STUDENT OPINION DATA ON HIGHER EDUCATION SERVICES USING THE NAIVE BAYES CLASSIFIER AND SUPPORT VECTOR MACHINE METHODS (CASE STUDY AKPRIND INSTITUTE OF SCIENCE AND TECHNOLOGY YOGYAKARTA)

Opinions are ideas, opinions, or the results of someone's subjective thoughts in explaining or addressing something. IST AKPRIND Yogyakarta provides comment and suggestion box facilities in the learning evaluation questionnaire. Opinions that have been collected can be used to determine the sentiment of the campus community. This sentiment information can be used in future campus development. The development of a system that can analyze sentiment automatically is designed by comparing the Naive Bayes Classifier (NBC) method and the support vector machine (SVM) optimized by selecting the Information Gain (IG) feature. Prior opinion data needs to be prepared before being analyzed. Preprocessing (text preprocessing) used includes: cleanning, text folding, normalization, stemming, stopword removal, convert negation, and tokenization. The results of this study show that the SVM method produces higher accuracy than NBC. The accuracy test shows the highest accuracy of SVM reaches 99.09% while NBC is 96.56%. The application of IG did not significantly affect the accuracy of the analysis. GI greatly influenced the analysis duration of the SVM method, which could shorten the time by 195.71%.

An Optimized VMD Method for Predicting Milling Cutter Wear Using Vibration Signal

Tool wear has a negative impact on machining quality and efficiency. As for the nonlinear and non-stationary characteristics of vibration signals and strong background noises during the milling process, an identification method of the milling cutter wear state based on the optimized Variational Mode Decomposition (VMD) was proposed, in which the objective function is to minimize the Envelope Entropy (Ep); the various modes of the vibration signal are decomposed using the self-adaptive optimization parameters with Differential Evolution (DE). According to the cross-correlation coefficient in the frequency domain between Intrinsic Mode Function (IMF) and the original signals, the informative IMF components were selected as the sensitive IMF components to superimpose the reconstruction signal and extract the eigenvalues. The mapping relationship between the eigenvalues and the milling cutter wear degree is established by the Naive Bayes classifier method. The experimental results under the various operation conditions indicate that the proposed optimized VMD method possesses an excellent generalization performance. Compared with Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD), it has better denoising capacity, and so can improve the identification accuracy of the milling cutter wear. Therefore, the processing quality and production efficiency are ensured effectively.

Menangani Ketaksaan dalam Transliterasi Mesin Jawi - Rumi menggunakan Pengelasan Naive Bayes Multinomial (NBM)

This paper discusses the problem of ambiguity in Jawi - Rumi machine transliteration for Jawi homograph words. Machine transliteration (MT) is the process of converting a script from source text to target text automatically. In the context of Malay MT for Jawi - Rumi, there are difficulties in obtaining high -accuracy transliteration of homographical Jawi words. Homographs are words that are the same spelling, but have different meanings and pronunciations. In the old Jawi spelling there were many homograph words, while it was successfully reduced when “Pedoman Ejaan Jawi yang Disempurnakan” (PEJYD) was first introduced by Dewan Bahasa dan Pustaka (DBP) in 1986. The main issue in the study of Malay Jawi - Rumi machine transliteration was word inaccuracy when the Jawi word is transliterated to Rumi. For example, the word “بيرو” can be transliterated to ‘biru’(blue) or ‘biro’(bureau), the word “بيليق” can be transliterated to ‘bilik’(room) or ‘belek’(turn around). This paper proposes that the Multinomial Naive Bayes (NBM) classification method be used for homograph unambiguity for TM Jawi - Rumi. Test results found that the accuracy of using this method can reach up to 67 percent.

Prediksi Penggunaan Obat Peserta Jaminan Kesehatan Nasional Menggunakan Algoritma Naïve Bayes Classifier

Currently, most of the patients seeking treatment at the hospital use the National Health Insurance (JKN) organized by the Healthcare and Social Security Agency (BPJS Kesehatan). In some hospitals, the figure is above 80%. Considering the very high number of BPJS Kesehatan participant seeking treatment at the hospital, a good data management method is needed, especially regarding the management of drug. Drug supply needs to be analyzed from time to time so that it can help predict future needs. An adequate supply of drugs and as needed is one of the things that affect service to patients. The availability of sufficient stock is expected to accelerate service to patients so that they do not have to wait long. Patients who are served quickly are expected to be satisfied. The impact of this patient satisfaction will increase the number of patient visits to the hospital. To support this, it is necessary to create a system that can estimate drug needs. The system can predict drug demand by using drug sales data to JKN participant patients for five years. Drug data used as research samples and then processed using an algorithm is the Naive Bayes Classifier. The Naive Bayes Classifier method is a method used to predict future opportunities using the basis of previous experience. A distinctive feature of this method is that it uses a very strong assumption of the independence of each event. While software testing uses the User Acceptance Test (UAT) model. Based on testing using this method, the system can be well received by users with a score of 78.64% (good).

ANALYSIS OF EMPLOYMENT SENTIMENT IN THE INDONESIAN TELEMATICS FIELD USE MULTINOMIAL NAIVE BAYES AND VECTOR SPACE MODEL

Indonesia in 2030 experienced a demographic bonus in the sense that Indonesia would have far more labor supply than in previous years. Then there is a discourse that this 4.0 industrial revolution will replace a lot of work, especially low-skilled work or does not require special skills and rough jobs replaced by machinery and artificial intelligent (AI). To obtain the value of the percentage of positive, negative and neutral sentiments from the public regarding the impact of the industrial revolution 4 against labor and employment on online news media sites and social media Twitter, the authors conducted a study "analysis of employment sentiment in Indonesian telematics using multinomial naïve bayes. " The author uses the preprocessing stages including the case folding, tokenizing, stopword, and stemming. Then weighting with Term Frequency - Invers Document Frequency (TF-IDF). After that the classification stage was done using the multinomial Naive Bayes Classifier method and compare it with the Vector Space model classification. The evaluation used is the Confusion Matrix evaluation method. This study produced an evaluation value in the multinomial method of Naïve Bayes for news data to produce an accuracy of 81.75%, average precision 82.77%, and the average recall of 78.15%. Whereas with the Vector Space model method for news data produces an accuracy of 67.88%, average precision 65.59%, and the average recall of 70.56%. On Twitter data with the Multinomial Naïve Bayes method resulted in an accuracy of 88.80%, average precision 93.75%, and the average recall of 74.44%. On Twitter data with the Vector Space Model method resulted in 85.60% accuracy, average precision 76.44% and average recall of 86.07%.

Quality Classification of Palm Oil Varieties Using Naive Bayes Classifier

As one of the leading commodities of the Indonesian economy, the ever-increasing production of palm oil has created intense competition among palm oil (CPO) producers. This causes CPO producers to increase their palm oil production without compromising the quality of the palm oil produced. CPO producers are required to be able to objectively determine the quality of superior and precise oil palm varieties in order to produce high economic value palm oil. Therefore, a model is needed to determine the quality of oil palm from several existing varieties. The Naive Bayes Classifier method in this study was used to classify the quality of oil palm based on predetermined variables using a data set of 28 oil palm varieties. Method testing is done by using a confusion matrix and K-fold cross-validation scheme. This study shows a reasonably high accuracy value of 64.25% and a low error rate of 35.7%, indicating that the Naive Bayes Classifier can classify the quality of oil palm varieties quite well. 

IMPLEMENTASI AlGORITMA NAIVE BAYES UNTUK MEMPREDIKSI TINGKAT PENYEBARAN COVID

The development of Information Technology (IT) is now very rapid and has been used in various aspects of life both in the field of government, banking, socio- cultural, industrial, education, and even health. One type of disease that gets attention for the application of IT is corona virus or better known as Covid 19 because the spread is quite widespread throughout the country, especially in the territory of Indonesia. Corona virus disease development in Indonesia is growing, based on WHO data as of today on August 30, 2020 positive cases have reached 172,053 people, cases died 7,343 people and recovered 124,185 people and the number of cases is increasing every day. Based on these conditions, Central Aceh is in a state of alert against the threat of corona virus. then a form of prevention of the widespread spread of the virus can be done by breaking the chain of transmission by doing social distancing. In this study, a system will be designed to anticipate the Covid-19 pandemic by predicting the rate of spread of covid-19, especially in central Aceh districts using the Naive Bayes Classifier method. The accuracy level of this system is a positive case of 60%.

Predicting the Quality of Pineapple Using the Naive Bayes Classifier Method

The growing demand for pineapple-based snacks offers very promising business prospects for fruit growers in Indonesia. This opportunity has been taken advantage of by many local farmers who grow this uniquely shaped fruit. To simplify the process of analyzing the quality of pineapples worth selling, machine learning assistance is needed. The factors that will be used for the calculations and analyzes that were previously performed manually by humans, should be performed with machine learning more quickly, efficiently, effectively and accurately. The author proposes the Naïve Bayes Classifier model as a classification method to demonstrate that the model has good accuracy, good precision, good recall and is good at classifying data. In this proposed model, the Naïve Bayes Classifier is a prediction technique based on simple probabilistic criteria and on the application of Bayes’ theorem with the assumption of strong independence. Therefore, this model is capable of producing high classification accuracy with low complexity with classification accuracy up to 75%, so this model can be efficient as an analytical tool.

Klasifikasi Penderita ISPA Menggunakan Metode Naive Bayes Classifier

Information related to the classification of ARI disease suffered by the community in a public health is essential. This is because the public health is one of the community health centers that is a reference for treatment for the community. Public health must identify the right type of ARI disease so that treatment for ARI sufferers can be given optimally. This study classified the data of patients with ARI in a public health based on the determining factors, namely the disease suffered, age, and period of stay. Classification is carried out using the Naive Bayes Classifier method with the Confusion Matrix testing method. The results of applying the Naive Bayes Classifier method to patient data resulted in three types of ARI, namely mild, moderate and severe. The highest number of ARI patients is severe ARI. The results of the Confusion Matrix test that have been carried out prove that this method has an accuracy of 93.33% so it is suitable for use to classify ARI diseases.

Sentiment Analysis of Public on The COVID-19 (Corona Virus Disease 2019) Vaccination Moderna and Sinovac Vaccine Using Naïve Bayes

One of the government's attempts to break the COVID-19 ( Corona Virus Disease 2019 ) chain was vaccination. Achieving the herd imunity is the main goal of vaccinating to control the COVID-19 pandemic. The Sinovac Vaccine and the Moderna Vaccine are examples of a type of inactivation vaccine. Indonesia's role in expressing and responding to the controversy often leads to public services used as materials to analyze who produced the data in support of the decision to compare the Sinovac type vaccine with the Moderna type vaccine. The purpose of the study is to see how people respond to vaccination with the various kinds of vaccines such as those taken in the study, the Moderna Vaccine type and the Sinovac Vaccine and the perceived value of sentiment analysis on the two vaccines. The method used in the study using the Naive Bayes Classification Method. The results from the study indicate that the Twitter users’sThe results from the study indicate that the Twitter users' twitter Sentiments Analysis with a positive testing of the Sinovac is larger than the Moderna Vaccine, which is 367 for the Sinovac Vaccine and 144 for the Moderna Vaccine.

Klasifikasi Penduduk Dengan Tingkat Ekonomi Rendah Menggunakan Metode Klasifikasi Naive Bayes

The effort to reduce poverty, it is hoped that the economic growth of a region can be spread evenly. To equalize the economic level of its citizens, local governments provide many opportunities for their citizens to get help or subsidies and even help open businesses so that the welfare of their citizens can be improved. The availability of accurate and sustainable citizens' economic status data is one of the important instruments for evaluating government policies in alleviating poverty by distributing targeted aid. Therefore, this study will conduct a classification based on data from citizens with low economic levels obtained from Pasar Babakan Sub-district, Bogor. The data used in this study is the data of underprivileged residents who are in Pasar Babakan Sub-district Bogor using data mining techniques. The training data are taken from 214 citizens that has a category of underprivileged citizens with attributes used in the classification of income per day, occupation and number of dependents. The test results using the Naive Bayes Classifier method produce 100% accuracy including the excellent category with 100% precision and 100% recall.

KLASIFIKASI DIABETES PADA WANITA MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER

The report from Riskesdas shows that there is a 2x increase in diabetes every year in Indonesia. This is due to an increase in factors such as human population, age, obesity, irregular eating patterns and lack of physical activity. The increase in a factor that causes diabetes in Indonesia must be prevented. The first step in preventing diabetes is to detect the risk factors for diabetes that may occur. Influencing factors include behavioral factors and sociodemographic factors The increase in diabetes in a country is due to late identified factors. The number of factors that are collected in order to detect whether a person has diabetes or not requires a fairly large data processing system. The data used in this study are diabetes data obtained from the Pima Indian Diabetes Database with attributes of pregnant, glucose, diastolic, triceps, insulin, BMI, history of diabetes, age and 300 data output. The Naive Bayes Classifier method can be used to classify diabetes in women based on pregnant, glucose, diastolic, triceps, insulin, BMI, history of diabetes, age and output. The accuracy result of the Naive Bayes Classifier method in classifying diabetes in women is 84% ​​of 300 data which is divided into 2, namely 275 data as training data and 25 data as test data.

Determination of the Children Classification with Special Needs in Extraordinary High School in Banjarmasin Using the Naive Bayes

Children with special needs with different physical characteristics are very easy to distinguish. However, in children with special needs with different psychological characteristics, there are difficulties in determining the classification of the child. Children with mental disorders are children who have deviations in the ability to think critically, logically in responding to the world around them. This study determined the classification of children with special needs, the decision support system can analyze using the naive bayes classification (NBC) method. The result showed that Naïve Bayes Classifier algorithm can be used as the basis for the decision-making process by the Special High School Special Education Foundation (SMALB YPLB) Banjarmasin. In this study, it is hoped that using an application based on the Naive Bayesian Classification method can be used as an alternative calculation that is more effective than calculations using the manual method, with the achievement of conformity of the results reaching 80%.

Analisis Sentimen Mengenai Vaksin Sinovac di Media Sosial Twitter Menggunakan Metode Naïve bayes Classification

The Sinovac vaccine is an example of a type of inactivated vaccine. The government bought Sinovac, Novavax, AstraZeneca, and Pfizer vaccines. This vaccine is used to treat the Covid-19 pandemic. This vaccine is used to treat the Covid-19 pandemic. The role of the Indonesian people in expressing and stating the pros and cons often involves public services that are easily accessible by many people, namely social media, one of which is Twitter. This can be used as material to analyze who produces data in support of decisions. The technique that can be used is sentiment analysis. The method used in this study is the Naïve bayes Classification. The purpose of this study was to determine the value of sentiment analysis on the Sinovac vaccine using the Naive Bayes Classification method on Twitter social media using Indonesian. The result of this research is the final probability value based on the condition 0.000002765 for positive and 0.000000359 for negative. A response with a positive comment has a greater probability of a response with a negative comment.

Perancangan Sistem Klasifikasi Wilayah Layanan Samsat Keliling Yang Berpotensi Menggunakan Metode Naïve Bayes Classification

This research builds a classification system for the potential service area of Samsat around Mataram. SAMSAT Mobile is a type of service at the joint office of SAMSAT Mataram which is a Technical Implementation Unit (UPT). samling can make it easier for people to make payments without the need to go to the Mataram SAMSAT office, but in the past year 4 service units owned by SAMSAT Mataram have provided services at the same place and there are several mobile SAMSATs that do not meet the target the place still has potential or there are other places that are more potential than the places that have been occupied by means of classification. Therefore, it is necessary for the application of the Potential Mobile SAMSAT Service Area Classification System Design Using the Naive bayes Classification Method. Naive Bayes is a classification method that is known to be simple but has high accuracy and to build this system requires python programming language with the Django framework. The system created can be used to identify areas that still have the potential to provide Mobile SAMSAT services

Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device

The need for monitoring of children today is very important, especially for children under five, whose physical and verbal abilities are still inadequate to be able to communicate effectively with their parents or caregivers about the conditions they are experiencing. Previous studies related to child safety that were studied generally focused on responses to events that could potentially harm children.This study aims to design a prototype child monitoring system consisting of a wearable device that is used on a child's wrist equipped with sensors and connected to a server via a wireless network. Monitoring software that runs on the server will collect all data parameters from wearable devices with built-in audio signal, temperature, and heart rate sensor then with machine learning algorithm implemented in software will allow the system to predict if stress conditions happen on children and then system can give warnings to child-caregiver through monitoring applications or SMS messages. Using the Decision Tree and Naive Bayes classification methods the system can effectively predict stress conditions in children with an accuracy of 82.8 percent using audio, temperature, and heart rate parameters. This shows that the system has the capability to contribute to increasing child safety in the supervision environment.

Visualisasi dan Analisa Data Penyebaran Covid-19 dengan Metode Klasifikasi Naïve Bayes

The covid-19 virus became a pandemic in 2020. The spread of covid cases has hit the whole world, reaching 63 million cases in 190 countries as of November 2020. Information regarding the spread of covid is necessary for the general public. This research will produce a system that can provide information on the geographic distribution of covid cases. The data on the distribution of covid cases in this study were also used to analyze the classification using the Naive Bayes Classifier method. The Naive Bayes Classifier method works by using probability calculations so that this research can be used to classify the covid status in an area. The results of this study have succeeded in providing information on the status of the covid pandemic based on data on covid cases that have occurred around the world. Covid case data becomes training data for the analysis of the Naive Bayes classifier method so that it can determine the status of the Covid pandemic based on test data provided by system users. This research has succeeded in helping users to know the status of the Covid pandemic in an area well because it has reliable training data.Keywords:System, Covid, Naïve Bayes Classifier.