scholarly journals Mobile-based Activity Monitoring System for the Self-quarantine Patient

2021 ◽  
Vol 4 (1) ◽  
pp. 56-62
Author(s):  
Annisaa Sri Indrawanti ◽  
Eka Prakarsa Mandyartha
Keyword(s):  
Monitoring System ◽  
Activity Recognition ◽  
Naive Bayes ◽  
Performance Testing ◽  
Naïve Bayes ◽  
Activity Monitoring ◽  
The Self ◽  
Classification Method ◽  
The Impact ◽  
Position Monitoring

Nowadays, not all the patient can be hospitalized because of the COVID-19 pandemics. So, the self-quarantine for the patient with the various diseases will be the given solution by the hospital. It would make the hospital needs a system that can monitor the activity and the position of the patient from a distance. Nowadays, mobile phone is equipped by the sensor that can detect the user movement. Not only the user’s position, but also the user’s activity. In this paper, it will be developed an activity and position monitoring system for the self-quarantine patient that can be used in their home. The mobile activity monitoring can be achieved by activity recognition using classification method. For the needs of performance testing, we evaluate some classification method for activity recognition to compare the among classification method for the activity recognition. Some tested classification methods are Naïve Bayes, KNN, KStar and TreeJ48. Furthermore, we tested the impact of sliding windows per N samples taken to the accuracy of the activity recognition. We choose the best N sample that could give the best accuracy for activity recognition. The system not only monitor the patient’s activity, but also the patient’s position. The position monitoring can be achieved using Google Maps API. The result is Naive bayes has the accuracy of 81.25%, KNN has the accuracy of 78.125%, KStar has the accuracy of 78.125% and TreeJ48 has the accuracy of 75%. The N sample that could give the best accuracy is 6 with the accuracy of 90.15%.

IMPRECISE CLASSIFICATION WITH CREDAL DECISION TREES

2012 ◽  
Vol 20 (05) ◽  
pp. 763-787 ◽  
Author(s):  
JOAQUÍN ABELLÁN ◽  
ANDRÉS R. MASEGOSA
Keyword(s):  
Decision Trees ◽  
Naive Bayes ◽  
Cardinal Number ◽  
Naïve Bayes ◽  
Classification Method ◽  
Cost Matrix ◽  
Imprecise Dirichlet Model ◽  
Dirichlet Model

In this paper, we present the following contributions: (i) an adaptation of a precise classifier to work on imprecise classification for cost-sensitive problems; (ii) a new measure to check the performance of an imprecise classifier. The imprecise classifier is based on a method to build simple decision trees that we have modified for imprecise classification. It uses the Imprecise Dirichlet Model (IDM) to represent information, with the upper entropy as a tool for splitting. Our new measure to compare imprecise classifiers takes errors into account. Thus far, this has not been considered by other measures for classifiers of this type. This measure penalizes wrong predictions using a cost matrix of the errors, given by an expert; and it quantifies the success of an imprecise classifier based on the cardinal number of the set of non-dominated states returned. To compare the performance of our imprecise classification method and the new measure, we have used a second imprecise classifier known as Naive Credal Classifier (NCC) which is a variation of the classic Naive Bayes using the IDM; and a known measure for imprecise classification.

Research on Filtration System of Network Negative Information on the Basis of Naive Bayes

2011 ◽  
Vol 271-273 ◽  
pp. 911-916
Author(s):  
Xi Ai Yan ◽  
Jin Min Yang
Keyword(s):  
Pattern Classification ◽  
Naive Bayes ◽  
Threshold Value ◽  
Negative Information ◽  
Naïve Bayes ◽  
Classification Method ◽  
Filtration System ◽  
Strong Robustness

The difficulty of filtrating network negative information lies in how to classify information correctly. As one of the classification method with the advantage of strong robustness and good understandability in the field of pattern classification, Naïve Bayes has been used widely. A method for filtrating network negative information on the basis of Naïve Bayes, improvement proposals aiming at the disadvantages of Naïve Bayes and amelioration of erroneous judgment of negative information by setting threshold value k have been put forward in this article. The experiment shows that by adjusting threshold value k can the integrity of the system can be optimum and can favorable application effects be achieved.

Implementation of Naïve Bayes Classification Method for Predicting Purchase

2018 ◽  
Author(s):  
Fitriana Harahap ◽  
Ahir Yugo Nugroho Harahap ◽  
Evri Ekadiansyah ◽  
Rita Novita Sari ◽  
Robiatul Adawiyah ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Method ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

scholarly journals Klasifikasi Berita Olahraga Menggunakan Metode Naïve Bayes dengan Enhanced Confix Stripping Stemmer

2018 ◽  
Vol 5 (3) ◽  
pp. 269
Author(s):  
Yoga Dwitya Pramudita ◽  
Sigit Susanto Putro ◽  
Nurul Makhmud
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Method ◽  
Easy Access ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
News Stories ◽  
Long Time ◽  
Sports News

<p>Dokumen berita olahraga dalam bentuk web kini memiliki jumlah yang besar dalam kurun waktu singkat. Untuk kemudahan akses dokumen perlu melakukan pengelompokan dokumen berita kedalam beberapa kategori. Hal tersebut bertujuan agar berita olahraga tersusun sesuai dengan kategori yang ditentukan. Berita dapat dikelompokkan secara manual oleh manusia, akan tetapi hal tersebut membutuhkan waktu yang lama untuk melakukan kategorisasi. Metode klasifikasi diusulkan dalam penelitian ini untuk melakukan pengkategorian secara otomatis dokumen berita. Tujuan dilakukannya klasifikasi adalah untuk mempercepat dan mempermudah dalam pemberian kategori, sehingga dapat meningkatkan efisiensi waktu. Pada penelitian ini menggunakan metode klasifikasi Naïve Bayes Classifier. Sebelum dilakukan klasifikasi ada proses preprocessing dengan menggunakan Enhanced Confix Striping Stemmer.  Hal ini bertujuan untuk mengembalikan ke bentuk kata dasar, sehingga data berkurang dan proses komputasi menjadi lebih efisien. Pengujian dilakukan menggunakan 18 berita olahraga yang dipilih secara acak oleh user atau tester, dari 18 berita yang diujikan terdapat 14 berita yang bernilai benar atau relevan dengan analisis yang dilakukan use atau tester pada berita uji. Dari penelitian ini dapat disimpulkan bahwa Aplikasi Klasifikasi Berita Olahraga menggunakan Metode Naïve Bayes dengan Enhanced Confix Striping Stemmer mampu mengklasifikasi berita olahraga sesuai dengan kategori masing-masing, seperti Sepak Bola, Basket, Raket, Formula 1, Moto GP dan olahraga lainnya dengan keakuratan sebesar 77%.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Judul2"> </p><p>Web-based sports news currently has a considerable amount of documents. News documents need to be grouped into multiple categories for easy access. The goal is that sports news is structured according to the specified category. News can be grouped manually by humans, but it takes a long time to categorize if it involves large documents. Classification method is proposed in this research to categorize automatically news document. The purpose of doing the classification is to accelerate and simplify the granting of categories, thereby increasing the efficiency of time. In this research using the Naïve Bayes Classifier classification method. Prior to classification there is a preprocessing process using Enhanced Confix Striping Stemmer. It aims to return to the basic word form, so the data is reduced and the computing process becomes more efficient. From the test using 18 sports news randomly selected by the user or tester, there are 14 news stories that are true or relevant to the analysis by the user or the tester on the test news. This study concludes that the Sports News Classification Application using the Naïve Bayes Method with Enhanced Confix Striping Stemmer is able to classify sports news according to their respective categories, such as Football, Basket, Racquet, Formula 1, Moto GP and other sports with accuracy of 77%.</p>

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