scholarly journals OPTIMASI METODE NAIVE BAYES DENGAN FEATURE SELECTION INFORMATION GAIN UNTUK PREDIKSI KETERLAMBATAN PEMBAYARAN SPP SEKOLAH

2019 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Muqorobin Muqorobin ◽  
Kusrini Kusrini ◽  
Emha Taufiq Luthfi
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Information Gain ◽  
Confusion Matrix ◽  
Education Institution ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Education Development ◽  
Main Requirement ◽  
Naive Bayes Method

The cost of education is one component of input that is very important in implementing education. Because costs are the main requirement in an effort to achieve educational goals. SMK Al-Islam Surakarta is a private education institution that requires students to pay school fees in the form of Education Development Donations. Educational Development Donation is a routine school fee that is conducted every month. Based on last year's TU report, many students were late in paying Education Development Donations, around 60%. This is a big problem. The purpose of this study is that researchers will build a predictive system using the Naïve Bayes method. Because the method can classify the class right or late, in the payment of school fees. Data processing was taken from the dapodik data of schools in 2017/2018 with the test dataset taking 30 records. To find out the level of accuracy, this research was conducted with the Naive Bayes Method and the Information Gain Method for feature selection. Accuracy testing is done by the Confusion Matrix method. The results showed that the highest accuracy was obtained by combining the Naive Bayes Method with the Information Gain Method obtained by 90% accuracy. 

scholarly journals Estimation System For Late Payment Of School Tuition Fees

2020 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Muqorobin Muqorobin ◽  
Kusrini Kusrini ◽  
Siti Rokhmah ◽  
Isnawati Muslihah
Keyword(s):  
Naive Bayes ◽  
Information Gain ◽  
Educational Institution ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Tuition Fees ◽  
Bayes Method ◽  
Estimation System ◽  
Naive Bayes Method

The Surakarta Al-Islam Vocational School is a private educational institution that requires all students to pay school tuition fees. Education is an obligation for all Indonesian citizens. The cost of education is one of the most important input components in implementing education. Because cost is the main requirement in achieving educational goals. SPP School is a routine school fee that is carried out every month. Based on last year's School Admin report, many students were late in paying school tuition fees, around 60%. This is a very big problem because the income of school funds comes from school tuition. The purpose of this research is that the researcher will build a prediction system using the best classification method, which is to compare the accuracy level of the Naïve Bayes method with the K-K-Nearest Neighbor method. Because both methods can make class classifications right or late, in paying school fees. processing using dapodic data for 2017/2018 as many as 236 data. In improving accuracy, the researcher also applies feature selection with Information Gain, which is useful for selecting optimal parameters. System testing is carried out using the Confusion Matrix method. The final results of this study indicate that the Naïve Bayes Method + Information Gain Method produces the highest accuracy, namely 95% compared to the Naïve Bayes method alone, namely 85% and the K-NN method, namely 81%.

scholarly journals An Anti-Spam System using Naive Bayes Method using Feature Selection Methods

2017 ◽  
Vol 165 (4) ◽  
pp. 1-5 ◽  
Author(s):  
Masoome Esmaeili ◽  
Arezoo Arjomandzadeh ◽  
Reza Shams ◽  
Morteza Zahedi
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Selection Methods ◽  
Bayes Method ◽  
Naive Bayes Method

scholarly journals Social Assistance Decision Support System Using the Naive Bayes Method

2021 ◽  
Vol 1 (1) ◽  
pp. 85
Author(s):  
Moh. Syaiful Anam
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Social Assistance ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Naive Bayes Method

Covid-19 telah menjadi pandemi yang menyebar hampir ke seluruh penjuru dunia. Karena proses penularannya yang begitu cepat Dalam masa pandemi covid -19, pandemi ini menyebar ke seluruh sendi kehidupan dan salah satu yang paling menjadi perhatian adalah dibidang sosial ekonomi. Banyak terdapat bantuan Sosial (Bansos) yang disalurkan baik oleh pemerintah ataupun pihak swasta lain. Penelitian ini bertujuan untuk membuat sistem pendukung keputusan bantuan sosial menggunakan metode Naive Bayes, selanjutnya melakukan Analisa menggunakan tabel Confusion Matrix.  Dalam menyelesaikan masalah dengan menggunakan metode Naive Bayes dari hasil pembahasan yang dilakukan dapat ditarik kesimpulan Naive Bayes dan aturan yang dihasilkan memiliki tingkat akurasi tinggi (good) yaitu sebesar 73% dan Sementara nilai Precision sebesar 92% dan Recall sebesar 86%. Sehingga metode Naive Bayes dapat diterapkan dalam menentukan prediksi yang lebih banyak dan potensial aturan yang dihasilkan untuk membantu menentukan pemberian bantuan sosial.

scholarly journals Kombinasi Metode Correlated Naive Bayes dan Metode Seleksi Fitur Wrapper untuk Klasifikasi Data Kesehatan

2020 ◽  
Vol 11 (2) ◽  
pp. 50-55
Author(s):  
Hairani Hairani ◽  
Muhammad Innuddin
Keyword(s):  
Feature Selection ◽  
Performance Test ◽  
Naive Bayes ◽  
Health Data ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Heart Attacks ◽  
Bayes Methods ◽  
Naive Bayes Method

Most features of health data that have many irrelevant features can reduce the performance of classification method. One health data that has many attributes is the Pima Indian Diabetes dataset and Thyroid. Diabetes is a deadly disease caused by the increasing of blood sugar because of the body's inability to produce enough insulin and its complications can lead to heart attacks and strokes. The purpose of this research is to do a combination of Correlated Naïve Bayes method and Wrapper-based feature selection to classification of health data. The stages of this research consist of several stages, namely; (1) the collection of Pima Indian Diabetes and Thyroid dataset from UCI Machine Learning Repository, (2) pre-processing data such as transformation, Scaling, and Wrapper-based feature selection, (3) classification using the Correlated Naive Bayes and Naive Bayes methods, and (4) performance test based on its accuracy using the 10-fold cross validation method. Based on the results, the combination of Correlated Naive Bayes method and Wrapper-based feature selection get the best accuracy for both datasets used. For Pima Indian Diabetes dataset, the accuracy is 71,4% and the Thyroid dataset accuracy is 79,38%. Thus, the combination of Correlated Naïve Bayes method and Wrapper-based feature selection result in better accuracy without feature selection with an increase of 4,1% for Pima Indian Diabetes dataset and 0,48% for the Thyroid dataset.

scholarly journals Analisis Sentimen Pada Media Sosial Twitter Menggunakan Naive Bayes Classifier Dengan Ekstrasi Fitur N-Gram

2018 ◽  
Vol 2 (2) ◽  
pp. 200
Author(s):  
Agung Nugroho
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Bayes Method ◽  
N Gram ◽  
Naive Bayes Method

Social media is currently an online media that is widely accessed in the world. Microblogging services such as Twitter allow users to write about various things they experience or write reviews of a product, service, public figures and so on. This can be used to take opinion or sentiment towards an entity that is being discussed on social media such as Twitter. This study utilizes these data to determine public opinion or sentiment regarding public perceptions of the issue of rising electricity tariffs. Opinion taking is based on three classes namely positive, negative and neutral. Users often use non-standard word abbreviations or spelling, this can complicate the process and accuracy of classification results. In this study the authors apply text-preprocessing in handling these problems. For feature extraction, n-gram and classification methods are used using the Naive Bayes classifier. From the results of the research that has been done, the most negative sentiments are formed in response to the issue of the increase in basic electricity tariffs. In addition, from the results of testing with the method of cross validation and confusion matrix it is known that the accuracy of the naïve Bayes method reaches 89.67% before applying n-gram, and the accuracy rate increases 2.33% after applying n-gram characters to 92.00%. It is proven that the application of the n-gram extraction feature can increase the accuracy of the naïve Bayes method.

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

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%.

scholarly journals Feature Selection using Particle Swarm Optimization Algorithm in Student Graduation Classification with Naive Bayes Method

2020 ◽  
Vol 4 (3) ◽  
pp. 469-475
Author(s):  
Evi Purnamasari ◽  
Dian Palupi Rini ◽  
Sukemi
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Swarm Optimization ◽  
Student Graduation ◽  
Naive Bayes Method

The study of the classification of student graduation at a university aims to help the university understand the academic development of students and to be able to find solutions in improving the development of student graduation in a timely manner. The Naive Bayes method is a statistical classification method used to predict a student's graduation in this study. The classification accuracy can be improved by selecting the appropriate features. Particle Swarm Optimization is an evolutionary optimization method that can be used in feature selection to produce a better level of accuracy. The testing  results of the alumni data using the Naive Bayes method that optimized with the Particle Swarm Optimization algorithm in selecting appropriate features, producing an accuracy value of 86%, 6% higher than the classification without feature selection using the Naive Bayes method.

scholarly journals PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA)

2020 ◽  
Vol 3 (1) ◽  
pp. 22-34
Author(s):  
Komang Aditya Pratama ◽  
Gede Aditra Pradnyana ◽  
I Ketut Resika Arthana
Keyword(s):  
Naive Bayes ◽  
Intelligent System ◽  
Naïve Bayes ◽  
The State ◽  
State University ◽  
Bayes Method ◽  
New Students ◽  
Computer Based ◽  
Entrance Test ◽  
Naive Bayes Method

Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using “Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)”. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%.

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