Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

Children’s healthcare is a relevant issue, especially the prevention of domestic accidents, since it has even been defined as a global health problem. Children’s activity classification generally uses sensors embedded in children’s clothing, which can lead to erroneous measurements for possible damage or mishandling. Having a non-invasive data source for a children’s activity classification model provides reliability to the monitoring system where it is applied. This work proposes the use of environmental sound as a data source for the generation of children’s activity classification models, implementing feature selection methods and classification techniques based on Bayesian networks, focused on the recognition of potentially triggering activities of domestic accidents, applicable in child monitoring systems. Two feature selection techniques were used: the Akaike criterion and genetic algorithms. Likewise, models were generated using three classifiers: naive Bayes, semi-naive Bayes and tree-augmented naive Bayes. The generated models, combining the methods of feature selection and the classifiers used, present accuracy of greater than 97% for most of them, with which we can conclude the efficiency of the proposal of the present work in the recognition of potentially detonating activities of domestic accidents.

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Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i3.3086 ◽

2021 ◽

Vol 5 (3) ◽

pp. 527-533

Author(s):

Yoga Religia ◽

Amali Amali

Keyword(s):

Feature Selection ◽

Customer Satisfaction ◽

Naive Bayes ◽

Naïve Bayes ◽

Point Of View ◽

Classification Model ◽

Passenger Satisfaction ◽

Airline Passenger ◽

Bayes Algorithm

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

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Opinion Mining on Culinary Food Customer Satisfaction Using Naïve Bayes Based-on Hybrid Feature Selection

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v15.i1.pp468-475 ◽

2019 ◽

Vol 15 (1) ◽

pp. 468 ◽

Cited By ~ 3

Author(s):

Oman Somantri ◽

Dyah Apriliani

Keyword(s):

Feature Selection ◽

Opinion Mining ◽

Naive Bayes ◽

Information Gain ◽

Naïve Bayes ◽

Classification Model ◽

Consumer Ratings ◽

Bayes Algorithm ◽

Restaurant Owners

<p>Conducting an assessment of consumer sentiments taken from social media in assessing a culinary food gives useful information for everyone who wants to get this information especially for migrants and tourists, in th other hand that information is very valuable for food stall and restaurant owners as information in improvinf food quality. Overcoming this problem, a sentiment analysis classification model using naïve bayes algorithm (NB) was applied to get this information. This problem occurs is the level of accuracy of classification of consumer ratings of culinary food is still not optimal because the weight of values in the data preprocessing process are not optimal. In this paper proposed a hybrid feature selection models to overcome the problems in the process of selecting the feature attributes that have not been optimal by using a combination of information gain (IG) and genetic algorithm (GA) algorithms. The result of this research showed that after the experiment and compared to using others algorithms produce the best of the level occuracy is 93%.</p>

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Komparasi Information Gain, Gain Ratio, CFs-Bestfirst dan CFs-PSO Search Terhadap Performa Deteksi Anomali

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v5i1.2258 ◽

2021 ◽

Vol 5 (1) ◽

pp. 332

Author(s):

Kurniabudi Kurniabudi ◽

Abdul Harris ◽

Albertus Edward Mintaria

Keyword(s):

Feature Selection ◽

Processing Time ◽

Naive Bayes ◽

Information Gain ◽

Large Data ◽

Naïve Bayes ◽

Classification Algorithm ◽

Test Results ◽

Gain Ratio ◽

Feature Selection Techniques

Large data dimensionality is one of the issues in anomaly detection. One approach used to overcome large data dimensions is feature selection. An effective feature selection technique will produce the most relevant features and can improve the classification algorithm to detect attacks. There have been many studies on feature selection techniques, each using different methods and strategies to find the best and relevant features. In this study, a comparison of Information Gain, Gain Ratio, CFs-BestFirst and CFs-PSO Search techniques was compared. The selection features of the four techniques were further validated by the Naive Bayes classification algorithm, k-NN and J48. This study uses the ISCX CICIDS-2017 dataset. Based on the test results the feature selection techniques affect the performance of the Naive Bayes algorithm, k-NN and J48. Increasingly relevant and important features can improve detection performance. The test results also show that the number of features influences the processing / computing time. CFs-BestFirst produces a smaller number of features compared to CFs-PSO Search, Information Gain and Gain Ratio so it requires lower processing time. In addition, k-NN requires a higher processing time than Naive Bayes and J48

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Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement

Knowledge Engineering and Data Science ◽

10.17977/um018v2i22019p58-71 ◽

2019 ◽

Vol 2 (2) ◽

pp. 58 ◽

Cited By ~ 1

Author(s):

Utomo Pujianto ◽

Asa Luki Setiawan ◽

Harits Ar Rosyid ◽

Ali M. Mohammad Salah

Keyword(s):

Feature Selection ◽

Decision Tree ◽

Naive Bayes ◽

Feature Selection Method ◽

Selection Method ◽

Naïve Bayes ◽

The Body ◽

Classification Model ◽

Diabetic Patients ◽

Patient Readmissions

Diabetes is a metabolic disorder disease in which the pancreas does not produce enough insulin or the body cannot use insulin produced effectively. The HbA1c examination, which measures the average glucose level of patients during the last 2-3 months, has become an important step to determine the condition of diabetic patients. Knowledge of the patient's condition can help medical staff to predict the possibility of patient readmissions, namely the occurrence of a patient requiring hospitalization services back at the hospital. The ability to predict patient readmissions will ultimately help the hospital to calculate and manage the quality of patient care. This study compares the performance of the Naïve Bayes method and C4.5 Decision Tree in predicting readmissions of diabetic patients, especially patients who have undergone HbA1c examination. As part of this study we also compare the performance of the classification model from a number of scenarios involving a combination of preprocessing methods, namely Synthetic Minority Over-Sampling Technique (SMOTE) and Wrapper feature selection method, with both classification techniques. The scenario of C4.5 method combined with SMOTE and feature selection method produces the best performance in classifying readmissions of diabetic patients with an accuracy value of 82.74 %, precision value of 87.1 %, and recall value of 82.7 %.

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Sentimen Analisis Komentar Toxic pada Grup Facebook Game Online Menggunakan Klasifikasi Naïve Bayes

Jurnal Informatika Universitas Pamulang ◽

10.32493/informatika.v5i3.6571 ◽

2020 ◽

Vol 5 (3) ◽

pp. 356

Author(s):

Renaldy Permana Sidiq ◽

Budi Arif Dermawan ◽

Yuyun Umaidah

Keyword(s):

Social Media ◽

Feature Selection ◽

Naive Bayes ◽

Information Gain ◽

Text Processing ◽

Confusion Matrix ◽

Naïve Bayes ◽

Classification Model ◽

Testing Data ◽

F Measure

Toxic comments are comments made by social media users that contain expressions of hatred, condescension, threatening, and insulting. Social media users who are on average still teenagers with a nature that still cannot be controlled completely becomes a matter of great concern when they comment, their comments can be studied as text processing. Sentiment analysis can be used as a solution to identifying toxic comments by dividing them into two classifications. Where the data used amounted to 1,500 taken from social media Facebook in the private group Arena of Valor community. The dataset is divided into 2 classes: toxic and non-toxic. This research uses Naive Bayes with TF-IDF transformation and Information Gain feature selection and use distribution ratio 80:20. It will be compared the results of the evaluation where Naive Bayes without transformation, using TF-IDF transformation, and TF-IDF using Information Gain feature selection. The results of the comparison of evaluations from confusion matrix that have been carried out obtained the best classification model is to use the ratio of training and testing data 80:20 with TF-IDF transformation resulting in an accuracy of 75%, precision of 63%, recall of 67%, and F-measure of 64%.

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Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i3.1942 ◽

2020 ◽

Vol 4 (3) ◽

pp. 504-512

Author(s):

Faried Zamachsari ◽

Gabriel Vangeran Saragih ◽

Susafa'ati ◽

Windu Gata

Keyword(s):

Social Media ◽

Support Vector Machine ◽

Feature Selection ◽

Public Opinion ◽

Naive Bayes ◽

Naïve Bayes ◽

Capital City ◽

Support Vector ◽

National Capital ◽

Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

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