Prediction of User Loyalty Using the Naive Bayes Method in the "Goprint" Online Printing Marketplace

2020 ◽  
Vol 8 (3) ◽  
pp. 227
Author(s):  
Gede Widiastawan ◽  
I Gusti Agung Gede Arya Kadyanan
Keyword(s):  
Decision Making ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Test Results ◽  
Bayes Method ◽  
Testing Data ◽  
Bayes Algorithm ◽  
Naive Bayes Method

Goprint is an Online Printing Marketplace that connects printing services with users who want to print documents quickly without the need to queue. In the span of time from April 2019 to September 2019 it was found that the number of Goprint users reached 407 users, 24 partners, and 256 orders. From transactions that have been carried out by users, not a few orders are often canceled due to ineffective Goprint features or poor partner performance. This causes Goprint users to feel dissatisfied with the services provided by the Goprint application. The Naive Bayes algorithm is one of the algorithms used for classification or grouping of data, but can also be used for decision making. With this algorithm and the problems that occur, the authors make a system to predict the loyalty of Goprint users to anticipate users who stop leaving Goprint because they are not satisfied or loyal users. The data used as training data is 20 and testing data is 10. From the test results it is found that the value of precision is 80%, 100% recall, and 90% accuracy.

scholarly journals Application Of Naïve Bayes to Predict the Potential of Rain in Ternate City

2021 ◽  
Vol 328 ◽  
pp. 04011
Author(s):  
Alwin Ali ◽  
Amal Khairan ◽  
Firman Tempola ◽  
Achmad Fuad
Keyword(s):  
Test Data ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Method ◽  
Calculation Results ◽  
Bayes Algorithm ◽  
Correct Data ◽  
Rain Data ◽  
Naive Bayes Method

The amount of rainfall that occurs cannot be determined with certainty, but it can be predicted or estimated. In predicting the potential for rain, data mining techniques can be used by classifying data using the naive Bayes method. Naïve Bayes algorithm is a classification method using probability and statistical methods. The purpose of this study is how to implement the naive Bayes method to predict the potential for rain in Ternate City, and be able to calculate the accuracy of the Naive Bayes method from system created. The highest calculation results with new data with a total of 400 training data and 30 test data, obtained 30 correct data with 100% precision, 100% recall and 100% accuracy and the lowest calculation results with new data with a total of 500 training data and 50 test data, obtained 38 correct data and 12 incorrect data with a percentage of precision 61.29%, recall 100% and accuracy 76%.

scholarly journals A COMPARISON OF ACCURACY BETWEEN TWO METHODS: NAЇVE BAYES ALGORITHM AND DECISION TREE-J48 TO PREDICT THE STOCK PRICE OF PT ASTRA INTERNATIONAL Tbk USING DATA FROM INDONESIA STOCK EXCHANGE

2019 ◽  
Vol 7 (1) ◽  
pp. 1244-1258
Author(s):  
Joan Yuliana Hutapea ◽  
Yusran Timur Samuel ◽  
Heima Sitorus
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Stock Prices ◽  
Stock Price ◽  
Naive Bayes ◽  
Stock Exchange ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Testing Data ◽  
Bayes Algorithm

The ability to predict the stock prices is very important for market players, whether individual or organizational investors.  The market players needs to know how to predict, that will help them in their decision making process, whether to buy or to sell its shares, so that it can maximize profits and reduce potential losses due to mistakes in decision making.  In accordance to this, the authors conducted a study that aimed to analyze and to compare the accuracy of two (2) methods that is used to predict the stock prices, namely: the Naїve Bayes Method and the Decision Tree-J48 Method. The amount of data used in this study were 1,195 stock datas of PT Astra International Tbk, issued by the IDX, by the period of January 1, 2013 to November 30, 2017. This study uses 7 attributes, namely:  Previews, High, Low, Close, Volume, Value, and Frequency. By using the WEKA application the result shows that, the accuracy of the Naïve Bayes Method using 20% of testing data, is 92.0502%, the precision value is 0.920 and the value of recall is 0.961,  while the accuracy of the Decision Tree J-48 method, using 20% of testing data, is 98.7448%, with precision value of 0.989 and the value of recall of 0.997.   Through this results,  it can be concluded that the decision tree J-48 algorithm has a better accuracy results compared to the Naive Bayes algorithm in predicting the stock price of PT. Astra Internasional Tbk.

scholarly journals Application Design of Dengue Hemorrhagic Fever Patients Screening Using Naive Bayes Method

2017 ◽  
Vol 34 ◽  
pp. 20-28
Author(s):  
Cindy Astelia Ramadhan Suparman ◽  
Endah Purwanti ◽  
Prihartini Widiyanti
Keyword(s):  
Dengue Hemorrhagic Fever ◽  
Naive Bayes ◽  
Public Health Problem ◽  
Hemorrhagic Fever ◽  
Naïve Bayes ◽  
Training Data ◽  
Design System ◽  
Design Calculation ◽  
Bayes Method ◽  
Naive Bayes Method

Dengue Hemorrhagic Fever is a disease which is endemic in most districts / cities still becomes a public health problem in Indonesia. The awareness of people to the dengue viral infection and its symptoms are needed to decrease the fatality of this disease. The community need to be known the symptoms thereby they could intervened and prevent from falling in to worse condition. This study was conducted to design system which could diagnose the onset of the disease with 3 levels of possibilities namely Grade 1 Dengue Hemorrhagic Fever, Grade 2 Dengue Hemorrhagic Fever, and Non Dengue Hemorrhagic Fever. The system is build based on patient medical records of Dr. Wahidin Sudiro Husodo General Hospital, Mojokerto, East Java using the Naive Bayes method. The method of this study including several steps such as collecting data, preprocessing data, designing database, interface design, calculation and processing data, classification and analyzing data and evaluating application. Determining the results of the application diagnose requires posterior calculation which searches the highest values in three degrees as the results of the initial diagnose. The application as a device for an early diagnosis of dengue hemorrhagic fever has a high accuracy value of 97% out of the 30 tested data. The homogenization of the training data and the test data by sex and age can be considered in future research.

scholarly journals Optimasi Model Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 32
Author(s):  
Hartatik Hartatik
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Prediction Models ◽  
Naive Bayes ◽  
Program Planning ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Student Graduation ◽  
Bayes Algorithm ◽  
Naive Bayes Method

<p>Abstrak :</p><p>Prediksi tentang status kelulusan mahasiswa menjadi persoalan tersendiri di perguruan tinggi. Perguruan tinggi utamanya di era Big Data sangatlah penting untuk melakukan prediksi perilaku akademik mahasiswa aktif sehingga dapat di ketahui kemungkinan mahasiswa bisa studi secara tepat waktu serta dapat diketahui langkah preventive dalam membuat prpgram perencanaan. Salah satu cara yang digunakan adalah teknik data mining yaitu menggunakan Algoritma <em>naive bayes</em>. Algoritma <em>Naive bayes</em> merupakan salah satu metode yang digunakan untuk memprediksi kelulusan mahasiswa.  Peneliti  dalam hal ini menerapkan  metode  <em>Naive bayes</em> menggunakan parameter Indeks prestasi kumulatif( IPK) dan membandingkan dengan menggunakan prediksi <em>naive bayes methods</em> berdasarkan parameter IPK dan sosial parameter yaitu jenis kelamin dan status tinggal. Dalam penelitian ini menggunakan parameter akademis  dan dilakukan optimasi menggunakan parameter sosial yang melekat pada mahasiswa. Berdasarkan hasil evaluasi untuk mendapatkan akurasi, hasil dari penelitian ini mendapatkan nilai akurasi untuk metode <em>Naive bayes</em>  sebesar 75% dan akurasi untuk model prediksi dengan parameter sosial  sebesar 85% dengan selisih akurasi 10%.</p><p>__________________________</p><p>Abstract : </p><p><em>Predictions about a student's graduation status are a problem in college. Major tertiary institutions in the era of Big Data are very important to predict the behavior of active students so that they can find out the possibility of students in a timely manner and can determine preventive steps in making program planning. One method used is data mining techniques using the Naive bayes Algorithm. The Naive bayes algorithm is one of the methods used to predict student graduation. Researchers in this case applied the Naive bayes method using the cumulative achievement index (GPA) parameter and compared using the prediction of the Naive bayes method based on the GPA parameters and social parameters, namely gender and status. This study uses academic parameters and is carried out optimally using social parameters inherent in students. Based on the results of the evaluation to get an accuracy value, the results of this study get an accurate value for the Naive bayes method of 75% and accurate for prediction models with social parameters of 85% with a difference of 10%.</em></p>

scholarly journals Development of Android-based Rabbit Disease Expert System

2018 ◽  
Vol 7 (4.44) ◽  
pp. 82
Author(s):  
Dyah Ayu Irawati ◽  
Yan Watequlis Syaifudin ◽  
Fabiola Ester Tomasila ◽  
Awan Setiawan ◽  
Erfan Rohadi
Keyword(s):  
Naive Bayes ◽  
Diagnostic System ◽  
Naïve Bayes ◽  
Certainty Factor ◽  
Bayes Method ◽  
Trust Value ◽  
User Input ◽  
Sample Data ◽  
Testing Data ◽  
Naive Bayes Method

Many rabbit keepers or breeders are panics when their rabbit has an illness. This paper proposed an expert diagnostic system application for Android-based rabbit disease using the Naïve Bayes method to determine the illness and Certainty Factor for the trust value of the condition by combining the rate of the trust of users and experts due to diagnose the diseases of the rabbit.The testing was using 65 data learning and 160 data learning to test the naïve Bayes method. Furthermore, the certainty factor is using CF user 1 and its variation.The results obtained for 65 data learning is 53%, while 160 data learning is 73%. With the naïve Bayes method, it can be concluded that the more data learning, the better and more accurate the system. The results of conformity with the testing data obtained from the variative CF user value, namely 53% accordingly, 13% inappropriate, 33% near. The effect of compliance with the sample data collected from the CF value of user 1 is 53% appropriate, 7% inappropriate, 40% is near. With the certainty factor method, it can be concluded that differences in user input values affect the overall CF value. 

scholarly journals Perbandingan Naïve bayes dan Certainty factor pada Sistem Pakar Untuk Mendiagnosa Dini Penyakit Glaukoma

2020 ◽  
Vol 5 (3) ◽  
pp. 291
Author(s):  
Hanif Rahman Burhani ◽  
Iskandar Fitri ◽  
Andrianingsih Andrianingsih
Keyword(s):  
Expert System ◽  
Naive Bayes ◽  
Comparison Method ◽  
Naïve Bayes ◽  
Training Data ◽  
Certainty Factor ◽  
Bayes Method ◽  
Lack Of Information ◽  
Statistics And Probability ◽  
Naive Bayes Method

Glaucoma is an eye disease that causes the second largest blindness after cataracts, this disease can cause decreased vision and can even be fatal, namely permanent blindness if it is not realized and treated immediately. Lack of information and education to the public to always maintain eye health is the basis for the purpose of making this expert system which aims to provide early diagnosis to people who are indicated to have glaucoma based on the symptoms or characteristics previously felt. The Naïve bayes method is a method that uses statistics and probability in predicting a person's chance of suffering from glaucoma based on the symptoms previously felt. It is made based on a website with PHP as the programming language and uses MySQL for the database. As for the comparison method used is the Certainty factor, which is a method that functions to determine a certainty value based on the calculation of the predetermined CF value by applying manual calculations. In the Naïve bayes method, the application can group symptom data and types of disease and can diagnose based on previous training data, while for the Certainty factor method based on the calculation of the value of the expert and the CF value that has been inputted by the user, it can produce a percentage of the diagnosis of the disease glaucoma in 96%.Keywords:Certainty factor, Expert System, Glaucoma, MySQL, Naïve bayes, PHP.

scholarly journals Penerapan Metode Naive Bayes dalam Memprediksi Kepuasan Mahasiswa Terhadap Cara Pengajaran Dosen

2019 ◽  
Vol 1 ◽  
pp. 287
Author(s):  
Desi Ratna Sari ◽  
Dedy Hartama ◽  
Irfan Sudahri Damanik ◽  
Anjar Wanto
Keyword(s):  
Higher Education ◽  
Student Satisfaction ◽  
Teaching Methods ◽  
Naive Bayes ◽  
Teaching Method ◽  
Higher Education Institutions ◽  
Naïve Bayes ◽  
Training Data ◽  
Testing Data ◽  
Bayes Algorithm

This research aims to classify in determining student satisfaction with teaching methods at STIKOM Tunas Bangsa. Data obtained from the results of the 2015 and 2016 semester student questionnaires were odd, with a sample of 80 students. Attributes used are 4, namely communication (C1), Building learning atmosphere (C2), Assessment of students (C3) and delivery of material (C4). The method used in this study is the Naïve Bayes Algorithm and is processed using RapidMiner studio 5.3 software to determine student satisfaction with teaching methods. Training data used 100 data while testing data used in manual calculations as much as 5 data. From the results of data testing the five data expressed satisfaction with the way teaching lecturers at STIKOM Tunas Bangsa. While the training data that is processed with RapidMiner has an accuracy of 92.00%. With this analysis, it is expected to be able to help higher education institutions to evaluate the performance of lecturers, especially in evaluating one of the three triharma colleges, namely the teaching method of lecturers.

scholarly journals Penerapan Algoritma Naïve Bayes Dalam Penentuan Kelayakan Penerima Kartu Jakarta Pintar Plus

2020 ◽  
Vol 6 (1) ◽  
pp. 15-20
Author(s):  
Endah Widya Ningsih ◽  
Hardiyan Hardiyan
Keyword(s):  
Smart Card ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Method ◽  
Using Data ◽  
Naive Bayes Method

The eligibility of Jakarta Smart Card Plus recipients is still not on target due to subjective receipts. Schools has an important role in making decisions about the eligibility of Jakarta Smart Plus Card recipients. Therefore, the authors make this research using data that already exists or is called training data. The author uses the Naïve Bayes method with 7 independent attributes to knowing eligibility. The author also uses the another application  Rapidmined 5.3 to test the accuracy of the Naïve Bayes method. The result of this research the accuracy of determining the eligibility of Jakarta Smart Plus Card recipients are good with 98.88% with an error of presentation 2.22%.  So it can be concluded that the naive bayes method can help detrermine the eligibility of jakarta smart plus card recipients. Keywords: Jakarta Smart Card, Naïve Bayes, eligibility

scholarly journals Prediksi Angka Kelahiran Bayi Pada Desa Tridaya Sakti Dengan Menggunakan Algoritma Naive Bayes

2020 ◽  
Vol 1 (2) ◽  
pp. 77-88
Author(s):  
Nur Isnaini Parihah ◽  
Sari Hartini ◽  
Juarni Siregar
Keyword(s):  
Data Mining ◽  
Population Growth ◽  
Naive Bayes ◽  
Large Population ◽  
Naïve Bayes ◽  
Training Data ◽  
Birth Rates ◽  
Testing Data ◽  
Bayes Algorithm ◽  
Infant Birth

The birth rate is something that can affect the increase in population growth. Large population is a burden for development. According to Malthus's Theory which states that a large population growth is not the welfare that is obtained but rather poverty will be encountered if the population is not well controlled. The number of baby births in Tridaya Sakti Village is increasing every year. Therefore Data Mining using the Naive Bayes algorithm can help in the calculation of predicting infant birth rates in Tridaya Sakti Village. Data Mining in predicting the number of infant birth rates aims to determine the number of infant birth rates for the coming year using the Naive Bayes algorithm. By looking at the prediction patterns of each variable and testing training data on testing data. It is hoped that the Naive Bayes algorithm can solve the problem in Tridaya Sakti Village in handling and overcoming the calculation of infant birth rates and can help the Tridaya Sakti Village in regulating population growth in the coming years. The results obtained from the data that have been taken and calculated by Data Mining using the Naive Bayes algorithm produce an information that can be used as a reference to find out the number of births. Performance and time in data processing are more effective and efficient as well as more accurate and accurate predictions of the number of baby births.   Keywords: Naive Bayes, Birth of a Baby, Prediction   Abstrak   Angka kelahiran merupakan suatu hal yang dapat mempengaruhi peningkatan pertumbuhan penduduk. Jumlah penduduk yang besar merupakan beban bagi pembangunan. Menurut Teori Malthus yang menyatakan bahwa pertumbuhan jumlah penduduk yang besar bukanlah kesejahteraan yang didapat tapi justru kemelaratan akan ditemui bilamana jumlah penduduk tidak dikendalikan dengan baik. Jumlah angka kelahiran bayi di Desa Tridaya Sakti setiap tahunnya semakin bertambah. Maka dari itu Data Mining dengan menggunakan algoritman Naive Bayes dapat membantu dalam perhitungan memprediksi angka kelahiran bayi di Desa Tridaya Sakti. Data Mining dalam memprediksi jumlah angka kelahiran bayi bertujuan untuk mengetahui jumlah angka kelahiran bayi tahun yang akan mendatang mengunakan algoritma Naive Bayes. Dengan melihat pola prediksi dari setiap variabel dan melakukan pengujian data training terhadap data testing. Diharapkan algoritma Naive Bayes ini dapat menyelesaikan permasalahan di Desa Tridaya Sakti dalam menangani dan mengatasi perhitungan angka kelahiran bayi dan dapat membantu pihak Desa Tridaya Sakti dalam mengatur pertumbuhan jumlah penduduk tahun yang akan mendatang. Hasil yang diperoleh dari data yang sudah diambil dan dihitung dengan Data Mining mengunakan algoritam Naive Bayes menghasilkan sebuah informasi yang dapat digunakan sebagai acuan untuk mengetahui jumlah angka kelahiran bayi. Kinerja dan waktu dalam proses pengolahan data lebih efektif dan efesien serta dari prediksi jumlah kelahiran bayi lebih tepat dan akurat. Kata Kunci: Naive Bayes, Kelahiran Bayi, Prediks  

scholarly journals Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

2021 ◽  
Vol 5 (2) ◽  
pp. 640
Author(s):  
Mulkan Azhari ◽  
Zakaria Situmorang ◽  
Rika Rosnelly
Keyword(s):  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Svm Algorithm ◽  
Testing Data ◽  
C4.5 Algorithm ◽  
Bayes Algorithm ◽  
Using Data

In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest algorithm accuracy is in SVM algorithm and the smallest is in random forest algorithm

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