scholarly journals Web-based Application for Classification Using Naïve Bayes and K-means Clustering (Case Study: Tic-tac-toe Game)

2020 ◽  
Vol 5 (1) ◽  
pp. 8
Author(s):  
Indriyani Indriyani ◽  
M. Ihsan Alfani Putera
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Quality Data ◽  
Clustering Methods ◽  
Web Based ◽  
Numerical Attributes ◽  
Analysis Application ◽  
Categorical Attributes ◽  
Data Generalization

A database can consist of numerical and non-numerical attributes. However, several data processing algorithms, such as K-means clustering, can be used only in a dataset with numerical attributes. Data generalization by using Naïve Bayes and K-means clustering methods is usually employed WEKA (Waikato environment for knowledge analysis) application. Although the strength of WEKA lies in increasingly complete and sophisticated algorithms, the success of data mining still lies in the knowledge factor of the human implementer. The task of collecting high-quality data and knowledge of modeling and the use of appropriate algorithms is needed to guarantee the accuracy of the expected formulations. In this paper, we propose a simple web-based application that can be used like WEKA. The methodology used in this study includes several stages. The first stage is the preparation of data, which is the tic-tac-toe game dataset that is converted to CSV (comma-separated values) format. The next stage is the process of modifying data from non-numeric to numeric, specifically for clustering with the K-means algorithm. Afterward, the calculation of the distance between data is conducted and followed by data clustering. The final stage is the summary of these processes and results. From the experimental results, it was found that clustering can be done on categorical attributes that are transformed first into the numerical form using web-based applications.

IMPLEMENTASI TEOREMA NAÏVE BAYES PADA SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN REKOMENDASI TEMPAT WISATA TERBAIK DISULAWESI SELATAN

2019 ◽  
Vol 2 (1) ◽  
pp. 40-46
Author(s):  
Rikardo Chandra ◽  
Izmy alwiah Musdar ◽  
Junaedy .
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Programming Language ◽  
Support System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Based ◽  
Tourist Attractions ◽  
South Sulawesi

This study aims to design and build web-based decision support system applications used to recommend the best tourist attractions in South Sulawesi to tourists. The expected benefit of this research is to help the user get the best tourist recommendation information available in South Sulawesi based on the conditions in input factors. The theorem or method used in this study, namely the theorem Naïve Bayes. The design of the system isimplemented using PHP programming language and MYSQL database. Based on the results of the research, the authors have successfully built the application of decision support system to determine the recommendation of tourist attractions in South Sulawesi with 65% accuracy based on 20 tests conducted.

Indonesian Twitter Sentiment Analysis Application on The Covid l9 Vaccine Using Naive Bayes Classifier

2021 ◽  
Author(s):  
Adhitia Erfina ◽  
Moneyta Dholah Rosita Ndk ◽  
Rahmat Hidayat ◽  
Aris Subagja ◽  
Haerul Ramadhan ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Analysis Application

Twitter Users' Classification Based on Interest

2020 ◽  
Vol 10 (1) ◽  
pp. 1-12
Author(s):  
Noura A. AlSomaikhi ◽  
Zakarya A. Alzamil
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Knowledge Sharing ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Software System ◽  
Web Based ◽  
Twitter Users

Microblogging platforms, such as Twitter, have become a popular interaction media that are used widely for different daily purposes, such as communication and knowledge sharing. Understanding the behaviors and interests of these platforms' users become a challenge that can help in different areas such as recommendation and filtering. In this article, an approach is proposed for classifying Twitter users with respect to their interests based on their Arabic tweets. A Multinomial Naïve Bayes machine learning algorithm is used for such classification. The proposed approach has been developed as a web-based software system that is integrated with Twitter using Twitter API. An experimental study on Arabic tweets has been investigated on the proposed system as a case study.

scholarly journals The Development of Classification System of Student Final Assignment Using Naive Bayes Classifier Case Study: State Community Academy of Bojonegoro

2018 ◽  
Vol 7 (4.44) ◽  
pp. 228
Author(s):  
Pramana Yoga Saputra ◽  
Yoppy Yunhasnawa ◽  
Windy Fatmila ◽  
Faisal Rahutomo ◽  
Rosa Andrie Asmara ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Study Program ◽  
Management Study ◽  
Manual Calculation

In determining interest, students are faced with the choice of specialization in determining the final field of interest. Specialization in the Information Management Study Program of State Community Academy of Bojonegoro is divided into five specializations. The choice of specialization groups is an important part. This is because the accuracy in choosing specialization groups is part of the initial plan of students to determine the final assignment project. Thus, the field of specialization taken will be in accordance with the interests and abilities of the students and will have an impact on the process. In this work, we propose a system that can provide information about the classification of student final assignments. We use Naive Bayes Classifier (NBC) algorithm to do the classification. In this work, we used datasets, that obtained from the State Community Academy of Bojonegoro Informatics Management Study Program. Based on the accuracy testing of the classification results, the system gives higher result, than test manual calculation of 83.33%.  

scholarly journals Analisis Perbandingan Algoritma Decision Tree, kNN, dan Naive Bayes untuk Prediksi Kesuksesan Start-up

2021 ◽  
Vol 6 (3) ◽  
pp. 178-188
Author(s):  
Adhitya Prayoga Permana ◽  
Kurniyatul Ainiyah ◽  
Khadijah Fahmi Hayati Holle
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Test Results ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Start Up ◽  
The Many ◽  
Start Ups

Start-ups have a very important role in economic growth, the existence of a start-up can open up many new jobs. However, not all start-ups that are developing can become successful start-ups. This is because start-ups have a high failure rate, data shows that 75% of start-ups fail in their development. Therefore, it is important to classify the successful and failed start-ups, so that later it can be used to see the factors that most influence start-up success, and can also predict the success of a start-up. Among the many classifications in data mining, the Decision Tree, kNN, and Naïve Bayes algorithms are the algorithms that the authors chose to classify the 923 start-up data records that were previously obtained. The test results using cross-validation and T-test show that the Decision Tree Algorithm is the most appropriate algorithm for classifying in this case study. This is evidenced by the accuracy value obtained from the Decision Tree algorithm, which is greater than other algorithms, which is 79.29%, while the kNN algorithm has an accuracy value of 66.69%, and Naive Bayes is 64.21%.

scholarly journals PENERAPAN EQUAL-WIDTH INTERVAL DISCRETIZATION DALAM METODE NAIVE BAYES UNTUK MENINGKATKAN AKURASI PREDIKSI PEMILIHAN JURUSAN SISWA (STUDI KASUS: MAS PAB 2 HELVETIA,MEDAN)

2018 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Alfa Saleh ◽  
Fina Nasari
Keyword(s):  
Naive Bayes ◽  
Academic Skills ◽  
Naïve Bayes ◽  
Academic Ability ◽  
Accuracy Rate ◽  
Important Thing ◽  
Student Data

The student majors is very important thing in developing students' academic skills and talents, it is required by the students are expected to hone the academic ability according to the field that is mastered and this is done so that each student can learn more in the Subjects that match the concentration that has been determined for each - students based on some predefined criteria. In this research has been tested by the method of Naive Bayes which aims to classify the students department based on the criteria that support. Where it is currently conducted with a case study on Madrasah Aliyah PAB 6 Helvetia students and obtained results from 100 student data with 90% accuracy rate. However, in order to improve the accuracy of the results of calcification, the researcher, the method used by using Unsupervised Discretization techniques that will transform numerical / continuous criteria into a categorical criterion. The result of the discretization on 100 data have been tested, it is proved that the results of the techniques used Discontented Disputes on the method of Naive Bayes rose from 90% to 93%. Jurusan siswa merupakan hal yang sangat penting dalam mengembangkan keterampilan dan bakat akademik siswa, hal ini dianggap perlu sebab siswa diharapkan mampu mengasah kemampuan akademis sesuai bidang yang dikuasai dan hal ini dilakukan agar setiap siswa dapat belajar lebih dalam pada mata pelajaran yang sesuai dengan konsentrasi yang telah ditentukan untuk masing-masing siswa berdasar beberapa kriteria yang telah ditetapkan. Pada penelitian ini telah dilakukan pengujian dengan metode Naive Bayes yang bertujuan untuk mengkasifikasikan jurusan siswa bedasarkan kriteria yang menunjang. pada penelitian ini dilakukan dengan studi kasus pada siswa Madrasah Aliyah Swasta PAB  6 Helvetia dan didapatkan hasil pengujian dari 100 data siswa dengan tingkat keakuratan 90%. Namun, untuk meningkatkan akurasi hasil kalsifikasi penentuan jurusan siswa ini, peneliti mengembangkan metode yang digunakan sebelumnya dengan menerapkan teknik Unsupervised Discretization yang akan mentransformasikan kriteria numerik/kontinyu menjadi kriteria kategorikal. Hasil dari diskritasi pada 100 data siswa yang diuji, terbukti bahwa hasil klasifikasi penerapan teknik Unsupervised Discretization pada metode Naive Bayes naik dari 90% menjadi 93%.

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