scholarly journals The Use of Naive Bayes for Broiler Digestive Tract Disease Detection

2019 ◽  
Vol 3 (01) ◽  
pp. 1-7
Author(s):  
Hindriyanto Dwi Purnomo
Keyword(s):  
Evaluation Method ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Gastrointestinal Diseases ◽  
Naïve Bayes ◽  
Common Disease ◽  
High Productivity ◽  
Bayes Algorithm ◽  
Tract Disease

Broiler chicken is a species of chicken that have high productivity. In order to get a good quality of chicken, good treatments of the breeding factors is needed, so the chicken will not easily infected by diseases. Gastrointestinal diseases are common disease that infects chickens. The mortality level caused by gastrointestinal diseases is considered high. This study is designed to address the problem by developing a system using the Naive Bayes algorithm. 60 chicken data samples were used, and the result shows that Naive Bayes might be used to detect gastrointestinal diseases among chickens with accuracy level of 93.3%. The number was confirmed by using confusion matrix evaluation method, and gave same level of accuracy compared to the expert judgments. 

scholarly journals Comparison of Cart and Naive Bayesian Algorithm Performance to Diagnose Diabetes Mellitus

2019 ◽  
Vol 2 (1) ◽  
pp. 9-16
Author(s):  
Irfan Santiko ◽  
Pungkas Subarkah
Keyword(s):  
Diabetes Mellitus ◽  
Evaluation Method ◽  
Naive Bayes ◽  
Adult Population ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Accuracy Result ◽  
Bayes Algorithm ◽  
Cart Algorithm ◽  
F Measure

Based on Indonesia's health profile in 2008, Diabetes Mellitus is the cause of the ranking of six for all ages in Indonesia with the proportion of deaths of 5.7% under stroke, TB, hypertension, injury and perinatal. This is reinforced by WHO (2003), Diabetes Mellitus disease reached 194 million people or 5.1 percent of the world's adult population and in 2025 is expected to increase to 333 million inhabitants. In particular, in Indonesia, people with Diabetes Mellitus are increasing. In 2000, Diabetes Mellitus sufferers have reached 8.4 million people and it is estimated that the prevalence of Diabetes Mellitus in 2030 in Indonesia reaches 21.3 million people.This allows researchers and practitioners to focus their attention on detecting/diagnosing diabetes mellitus and to prevent it because the disease can cause complications. The method used in this research was problem identification, data collection, pre-processing stage, classification method, validation and evaluation and conclusion. The algorithm used in this research was CART and Naïve Bayes using dataset taken from UCI Indian Pima database repository consisting of clinical data ofpatients who detected positive and negative diabetes mellitus. Validation and evaluation method used was 10-crossvalidation and confusion Matrix for the assessment of precision, recall and F-Measure. The result of calculation has been done, got the accuracy result on CART algorithm equaled to 76.9337% with precision 0.764%, recall 0.769%, and F-Measure 0.765%. Whilethe diabetes dataset was tested with the Naïve Bayes algorithm, got an accuracy of 73.7569% with precision 0.732%, recall 0.738%, and F-Measure 0.734%. From these results it can be concluded that to diagnose diabetes mellitus disease it is suggested to use CART algorithm.

scholarly journals Naïve Bayes Algorithm For Sentiment Analysis Windows Phone Store Application Reviews

SinkrOn ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 13 ◽  
Author(s):  
Normah Normah
Keyword(s):  
Sentiment Analysis ◽  
User Satisfaction ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Good Potential ◽  
Bayes Algorithm ◽  
And Performance ◽  
Windows Phone

Reading reviews helps consumers choose the applications, helping companies and developers monitor user satisfaction to improve quality of features and services, read overall and manually could spend the time and laborious, if read at a glance, information not conveyed perfectly. This study analyzes user sentiment Windows Phone Store applications by automatically classifying reviews into positive or negative opinion category. Naïve bayes has good potential because of its simplicity and performance as a model of classifying text on many domains. The model was evaluated using 10 Fold Cross Validation. Measurements were made with the Confusion Matrix and the ROC curve. The accuracy produced in this study is 84.50%, indicating that Naïve Bayes is a good model in classifying text especially in the case of sentiment analysis.

scholarly journals Prediction of Heart Disease Using Machine Learning Algorithms

2018 ◽  
Vol 7 (2.32) ◽  
pp. 363 ◽  
Author(s):  
N Rajesh ◽  
Maneesha T ◽  
Shaik Hafeez ◽  
Hari Krishna
Keyword(s):  
Risk Factors ◽  
Heart Disease ◽  
Decision Trees ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Common Disease ◽  
Bayes Algorithm ◽  
The One

Heart disease is the one of the most common disease. This disease is quite common now a days we used different attributes which can relate to this heart diseases well to find the better method to predict and we also used algorithms for prediction. Naive Bayes, algorithm is analyzed on dataset based on risk factors. We also used decision trees and combination of algorithms for the prediction of heart disease based on the above attributes. The results shown that when the dataset is small naive Bayes algorithm gives the accurate results and when the dataset is large decision trees gives the accurate results.  

scholarly journals SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN

2019 ◽  
Vol 15 (2) ◽  
pp. 247-254
Author(s):  
Heru Sukma Utama ◽  
Didi Rosiyadi ◽  
Dedi Aridarma ◽  
Bobby Suryo Prakoso
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Support Vector ◽  
Toll Road ◽  
Textual Data ◽  
Bayes Algorithm

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

scholarly journals Prediction of Solid Garbage Waste Generation in Smart Cities using Naive Bayes Algorithm

2019 ◽  
Vol 9 (2S) ◽  
pp. 53-56
Keyword(s):  
Naive Bayes ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Confusion Matrix ◽  
Daily Basis ◽  
Naïve Bayes ◽  
Human Beings ◽  
Waste Generation ◽  
Future Prediction ◽  
Bayes Algorithm

Smart cities which are becoming overcrowded today are making human beings life miserable and prone to more challenges on daily basis. Overcrowded is leading to vast generation of wastes contributing to air pollution and in turn is affecting health causing various diseases. Even though various measures are taken to recycle wastes, the rate at which it is being produced is becoming higher and higher. This paper deals with prediction of waste generation using Naïve Bayes machine learning algorithm(Classifier) based on the statistics of previous waste datasets. The datasets used for the future prediction are obtained from reliable sources. The implementation of the algorithm is done in Pyspark using Anaconda Jupyter. The performance of the classifier on the datasets is analyzed with confusion matrix and accuracy metric is used to rate the efficiency of the classifier. The accuracy obtained indicates that algorithm can be effectively used for real time prediction and it gives more accurate results for huge input datasets based on independence assumption.

scholarly journals Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease

2020 ◽  
Vol 4 (1) ◽  
pp. 76-85
Author(s):  
Dwi Yuni Utami ◽  
Elah Nurlelah ◽  
Noer Hikmah
Keyword(s):  
Genetic Algorithms ◽  
Liver Disease ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Attribute Selection ◽  
World Health ◽  
The Difference ◽  
Bayes Algorithm ◽  
Health Organization

Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%.Keywords: Liver Disease, Naïve Bayes, Genetic Agorithms, Bagging.

Analisa Topik Pendidikan Dalam Al-Quran dengan Pendekatan Text Mining

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Bustami Yusuf ◽  
Muhammad Zaeki ◽  
Hendri Ahmadian ◽  
Khairan Ar ◽  
Sri Wahyuni
Keyword(s):  
Evaluation Method ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Sources Of Knowledge ◽  
Scientific Disciplines ◽  
Study Results ◽  
The World ◽  
The Subject ◽  
Bayes Algorithm

Education is one of the sciences that makes humans much better by learning various scientific disciplines. Al-Quran is one of the sources of knowledge that is believed by Muslims around the world. Because technology has penetrated almost every domain of our lives , including the world of education. Thus, the authors make technology as tool  for researching educational topics in Al-Quran by implementing text exploration .The research was carried out by making some basic words that were related to the subject of education as the keywords in this study. The keywords are “Ajar”, “Bicara”, “Cipta”, “Dengar”, “Ingat” and “Lihat”. Then, the authors implemented the Naïve Bayes Classifier algorithm. To test and evaluate the results, the author used two methods, i.e. recall and precision. The study results are the keyword “cipta” by 3.05 %, “Ingat” 2.25 %, “Ajar” 1.96 %,“Lihat” 0.82 %, finally “Dengar” 0.62% and “Bicara” 0.34% with  total  weight of 3,516 words that  have been filtered. The overall percentage of the results is 9.04% of the total number of words 38,761 in the Al-Quran. For the Naïve Bayes algorithm evaluation method,  the recall and precision scores are 0.605 and 0.366, respectively.

scholarly journals Implementasi Algoritma C5.0 Untuk Menganalisa Gejala Prioritas Pada Anak Yang Mengalami Bullying

Repositor ◽  
2020 ◽  
Vol 2 (8) ◽  
Author(s):  
Nabillah Annisa Rahmayanti ◽  
Yufis Azhar ◽  
Gita Indah Marthasari
Keyword(s):  
Feature Selection ◽  
Cross Validation ◽  
Evaluation Method ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Training Data ◽  
Victims Of Bullying ◽  
Fold Cross Validation ◽  
Selection Of

AbstrakBullying sering terjadi pada anak-anak khususnya remaja dan meresahkan para orang tua. Maraknya kasus bullying di negeri ini bahkan sampai menyebabkan korban jiwa. Hal ini dapat dicegah dengan cara mengetahui gejala-gejala seorang anak yang mengalami bullying. Kondisi seorang anak yang tidak dapat mengungkapkan keluh kesahnya, tentu membuat orang tua dan juga guru di sekolah sukar dalam mengerti apa yang sedang menimpanya. Hal tersebut bisa saja dikarenakan anak sedang mengalami tindakan bullying oleh teman-temannya. Oleh karena itu peneliti memiliki tujuan untuk menghasilkan fitur yang telah terseleksi dengan menggunakan algoritma C5.0. Sehingga dengan menggunakan fitur yang telah terseleksi dapat meringankan pekerjaan dalam mengisi kuisioner dan juga mempersingkat waktu dalam menentukan seorang anak apakah terkena bullying atau tidak berdasarkan gejala yang ada di setiap pertanyaan pada kuisioner. Untuk menunjang data dalam penelitian ini, peneliti menggunakan kuisioner untuk mendapatkan jawaban dari pertanyaan yang berisi tentang gejala anak yang menjadi korban bullying. Jawaban dari responden akan diolah menjadi kumpulan data yang nantinya akan dibagi menjadi data latih dan data uji untuk selanjutnya diteliti dengan menggunakan Algoritma C5.0. Metode evaluasi yang digunakan pada penelitian ini yaitu 10 fold cross validation dan untuk menilai akurasi menggunakan confusion matrix. Penelitian ini juga melaukan perbandingan dengan beberapa algoritma klasifikasi lainnya yaitu Naive Bayes dan KNN yang bertujuan untuk melhat seberapa akurat algoritma C5.0 dalam melakukan seleksi fitur. Hasil pengujian menunjukkan bahwa algoritma C5.0 mampu melakukan seleksi fitur dan juga memiliki tingkat akurasi yang lebih baik jika dibandingkan dengan algoritma Naive Bayes dan KNN dengan hasil akurasi sebelum menggunakan seleksi fitur sebesar 92,77% dan setelah menggunakan seleksi fitur sebesar 93,33%. Abstract Bullying often occurs in children, especially teenagers and unsettles parents. The rise of cases of bullying in this country even caused casualties. This can be prevented by knowing the symptoms of a child who has bullying. The condition of a child who cannot express his complaints, certainly makes parents and teachers at school difficult to understand what is happening to them. This could be because the child is experiencing bullying by his friends. Therefore, researchers have a goal to produce selected features using the C5.0 algorithm. So using the selected features can ease the work in filling out questionnaires and also shorten the time in determining whether a child is exposed to bullying or not based on the symptoms in each question in the questionnaire. To support the data in this study, the researcher used a questionnaire to get answers to questions that contained the symptoms of children who were victims of bullying. The answer from the respondent will be processed into a data collection which will later be divided into training data and test data for further research using the C5.0 Algorithm. The evaluation method used in this study is 10 fold cross validation and to assess accuracy using confusion matrix. This study also carried out a comparison with several other classification algorithms, namely Naive Bayes and KNN which aimed to see how accurate the C5.0 algorithm was in feature selection. The test results show that the C5.0 algorithm is capable of feature selection and also has a better accuracy compared to the Naive Bayes and KNN algorithms with accuracy results before using feature selection of 92.77% and after using feature selection of 93.33%

scholarly journals Sentiment Analysis Using Naive Bayes Algorithm with Feature Selection Particle Swarm Optimization (PSO) and Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Abi Rafdi ◽  
Herman Mawengkang Herman ◽  
Syahril Efendi
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Bayes Algorithm

This study analyzes Sentiment to see opinions, points of view, judgments, attitudes, and emotions towards creatures and aspects expressed through texts. One of Social Media is like Twitter is one of the most widely used means of communication as a research topic. The main problem with sentiment analysis is voting and using the best feature options for maximum results. Either, the most widely known classification method is Naive Bayes. However, Naive Bayes is very sensitive to significant features. That way, in this test, a comparison of feature selection is carried out using Particle Swarm Optimization and Genetic Algorithm to improve the accuracy performance of the Naive Bayes algorithm. Analyses are performed by comparing before and after testing using feature selection. Validation uses a cross-validation technique, while the confusion matrix ??is appealed to measure accuracy. The results showed the highest increase for Naïve Bayes algorithm accuracy when using the feature selection of the Particle Swarm Optimization Algorithm from 60.26% to 77.50%, while the genetic algorithm from 60.26% to 70.71%. Therefore, the choice of the best characteristics is Particle Swarm Optimization which is superior with an increase in accuracy of 17.24%.

scholarly journals ANALISIS PEMANTAUAN LAN MENGGUNAKAN METODE QoS DAN PENGKLASIFIKASIAN STATUS JARINGAN INTERNET MENGGUNAKAN ALGORITMA NAIVE BAYES

2018 ◽  
Vol 4 (2) ◽  
Author(s):  
Sachin Sabloak ◽  
Jasuandi Wijaya ◽  
Abdul Rahman ◽  
Molavi Arman
Keyword(s):  
Quality Of Service ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Internet ◽  
Data Set ◽  
Computer Laboratory ◽  
The Status ◽  
Bayes Algorithm ◽  
Internet Network

[Id]Pentingnya jaringan komputer pada kehidupan sekarang, perlu adanya kestabilan jaringan komputer yang digunakan. Pemantauan kualitas jaringan internet didalam sebuah jaringan LAN dilakukan network administrator untuk mendapatkan nilai dari data yang didapat, penelitian ini menerapkan algoritma Naive Bayes menggunakan dataset TIPHON dengan parameter yang terdapat dalam metode QoS yaitu delay, packetloss dan jitter untuk memonitor kualitas jaringan internet. Metode QoS akan menghasilkan nilai dari setiap parameter yang dibutuhkan untuk pemantauan jaringan, guna mendapatkan kesimpulan mengenai status jaringan internet digunakan Algoritma Naive Bayes. Metode Quality of Service (QoS) merupakan sebuah metode yang digunakan dalam mendefinisikan kemampuan suatu jaringan yang ?digunakan untuk pengukuran tentang kualitas ?jaringan. Penggunaan algoritma Naive Bayes diperlukan karena algoritma tersebut digunakan dalam pengklasifikasian yang menggunakan probabilitas dan statistik serta mampu mengambil keputusan dengan menggunakan dataset yang telah disediakan. Tujuan penelitian ini dilakukan untuk mengetahui status jaringan internet di lab komputer STMIK Global Informatika MDP serta mengetahui tingkat akurasi dari algoritma Naive Bayes untuk mengklasifikasikan status jaringan internet. Pengujian penelitian dilakukan di lab komputer STMIK Global Informatika MDP. Hasil pengujian dalam penelitian ini menunjukkan bahwa akurasi Naive Bayes yang didapatkan sebesar 87,78% dan status jaringan internet di lab komputer STMIK Global Informatika MDP masuk ke dalam kategori memuaskan dengan nilai dominan yaitu sebesar 47,78%.Kata Kunci: Naive Bayes, network administrator, Quality of Service (QoS), status jaringan internet.[En]Since computer network is very important nowadays, it needs the stability of the network used. Monitoring the quality of the internet network in LAN is conducted by an administrator to get the value of the data obtained. This research applied Naive Bayes algorithm using TIPHON data set with parameters in QoS method; delay, packetloss and jitter, to monitor the quality of the internet network. QoS method will gain value in every parameter needed for network monitoring. To get a conclusion about the status of the internet network, Naive Bayes algorithm was used. Quality of Service (QoS) method is a method used to define the ability of a network to measure its quality. Naive Bayes algorithm is needed since the algorithm is used in classifying using probability and statistic as well as making decision using dataset provided. This research is conducted to see the status of the internet network in STMIK Global Informatika MDP computer laboratory and to know the level of accuracy of Naive Bayes algorithm to classify the status of the network. The research was conducted in STMIK Global Informatika MDP computer laboratory. The result of the research showed that the accuracy of Naive Bayes was 87,78% and the status of the internet network STMIK Global Informatika MDP was in the category of satisfactory with dominant value 47,78%.

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