IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI PEMESANAN DRIVER GO-JEK ONLINE DENGAN MENGGUNAKAN METODE NAIVE BAYES (STUDI KASUS: PT. GO-JEK INDONESIA)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Delisman Laia ◽  
Efori Buulolo ◽  
Matias Julyus Fika Sirait
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Transportation Industry ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Taxi Service ◽  
Bayes Algorithm ◽  
Using Data

PT. Go-Jek Indonesia is a service company. Go-jek online is a technology-based motorcycle taxi service that leads the transportation industry revolution. Predictions on ordering go-jek drivers using data mining algorithms are used to solve problems faced by the company PT. Go-Jek Indonesia to predict the level of ordering of online go-to drivers. In determining the crowded and lonely time. The proposed method is Naive Bayes. Naive Bayes algorithm aims to classify data in certain classes. The purpose of this study is to look at the prediction patterns of each of the attributes contained in the data set by using the naive algorithm and testing the training data on testing data to see whether the data pattern is good or not. what will be predicted is to collect the data of the previous driver ordering, which is based on the day, time for one month. The Naive Bayes algorithm is used to predict the ordering of online go-to-go drivers that will be experienced every day by seeing each order such as morning, afternoon and evening. The results of this study are to make it easier for the company to analyze the data of each go-jek driver booking in taking policies to ensure that both drivers and consumers or customers.Keywords: Go-jek Driver, Data Mining, Naive Bayes

scholarly journals Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection

2020 ◽  
Vol 1 (3) ◽  
pp. 123-134
Author(s):  
Budiman Budiman ◽  
Reni Nursyanti ◽  
R Yadi Rakhman Alamsyah ◽  
Imannudin Akbar
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Study Program ◽  
Data Set ◽  
Lower Accuracy ◽  
Accuracy Result ◽  
Bayes Algorithm

Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.

scholarly journals The Comparison of Data Mining Methods Using C4.5 Algorithm and Naive Bayes in Predicting Heart Disease

Tech-E ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 44
Author(s):  
Rino Rino
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Set ◽  
A Value ◽  
C4.5 Algorithm ◽  
Calculation Results ◽  
Mining Methods ◽  
Bayes Algorithm

Heart disease is a condition of the presence of fatty deposits in the coronary arteries in the heart which changes the role and shape of the arteries so that blood flow to the heart is obstructed. Data mining methods can predict this disease, some of the methods are C4.5 Algorithm and Naive Bayes which are often used in research.The data set in this research was obtained from the uci machine learning repository site, where the dataset has 3546 records and 13 attributes.The accuracy value of the Naïve Bayes algorithm has a high value of 81.40% compared to the C4.5 algorithm which only has an accuracy value of 79.07%. Based on the calculation results, it can be concluded that the Naïve Bayes Algorithm is a very good clarification because it has a value between 0.709 - 1.00.From conclusion above, the Naïve Bayes algorithm has a higher accuracy value than the C4.5 algorithm so the researchers decided to use the Naïve Bayes algorithm in predicting heart disease.

scholarly journals Application of the Naïve Bayes Algorithm for Student Graduation Analysis

2018 ◽  
Vol 7 (4.15) ◽  
pp. 421
Author(s):  
Erick Akhmad Fahmi Alfa’izy ◽  
Khairil Anam ◽  
Naidah Naing ◽  
Rosanita Tritias Utami ◽  
Nur Anim Jauhariyah ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
College System ◽  
Student Graduation ◽  
Bayes Algorithm ◽  
Using Data ◽  
Analysis System ◽  
Law Student

Design an analysis system to find out graduation by comparing previous data and existing data to overcome errors in a college system. By taking data records that are already available to be processed using the naïve Bayes algorithm. This research was conducted at Universitas Maarif Hasyim Latif. In this case, the object of research is to analyze the data of students with naïve Bayes algorithms to find out their graduation. For sampling the data taken is the previous Faculty of Law Student data to be used as training data, to retrieve the entire data using data records that are already available in the Directorate of Information Systems. That the naïve Bayes algorithm can be used in the classification of data in the form of a string or textual. This is based on researchers' trials in taking examples of calculations that have been done before. To compare the results of the classification of graduation analysis using the naïve Bayes algorithm testing is done with a sample of data in the form of training data compared to data testing. From the calculations that have been made, the accuracy is 77.78%. 

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

PENERAPAN DATA MINING MENGGUNAKAN METODE TEKNIK CLASSIFICATION UNTUK MELIHAT POTENSI KEPATUHAN WAJIB PAJAK BUMI DAN BANGUNAN

2019 ◽  
Vol 20 (2) ◽  
pp. 157-168
Author(s):  
Qoriani Widayati
Keyword(s):  
Data Mining ◽  
Regional Development ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Regional Government ◽  
Data Mining Techniques ◽  
Know How ◽  
Bayes Algorithm ◽  
Using Data

The goverment implements development in Indonesia, requires substantial funds. The entry of cash from the Land and Building Tax is the most important part for the development of a region, with the results that have been obtained by the regional government can increase regional development with various infrastructures that help the community to carry out various activities and make the area more advanced. One type of tax is the Land and Building Tax (PBB). With the increasing number of taxpayers and data paying contributions directly into the treasury of state finances, the UPT BPPD of SU II Subdistrict of Palembang city did not know how many obedient and non-compliant taxpayers. In this study using data mining techniques, namely classification by applying the Naive Bayes algorithm and getting from the number of taxpayers as many as 1,647 taxpayers with an accuracy of 99.33% which has the potential to not be on time in 16 ulu villages at 0,437 and sub-district households with data of 0.229.

scholarly journals Prediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh)

2019 ◽  
Vol 3 (2) ◽  
pp. 59
Author(s):  
Munawir Munawir ◽  
Taufiq Iqbal
Keyword(s):  
Data Mining ◽  
Research Method ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithm ◽  
Standard Data ◽  
Industry Standard ◽  
Bayes Algorithm ◽  
Using Data ◽  
Banda Aceh

The e-questionnaire application that researchers built using CodeIgniter and React-Js This study aims to data mining by using rapidminer tools to collect student data from the Feeder application page from the class of 2010-2014 which is assumed that the student class has been declared graduated in 2018. The data was collected from 5 (five) Private Universities in the City Banda Aceh. then by observing the graduation level using data mining can bring a considerable contribution to educational institutions, in an effort to improve curriculum competency in Higher Education, it is expected that the results of data mining can make reference to curriculum standards as a form of graduate competency improvement. The research method uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) which is used as a standard data mining process as well as a research method with stages starting from Business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results showed that the data mining algorithm for graduation prediction based on the selected pass accuracy attribute revealed that the prediction level was uniform with the algorithm used, Naïve Bayes, prediction accuracy was 84%. The data attributes that were found to have significantly influenced the classification process were the GPA and Study Length. The results obtained that students who graduated by 60% are students who are educated in ASM Nusantara and AMIK Indonesia, while in Banda Aceh STIES and Serambi University Mecca the prediction of graduation is 52%. Another thing is different from STIA Iskandar Thani where the prediction of graduating is only 48% and not passing on time is 52%. The results of this prediction can reveal and become a recommendation for prospective students or academics to increase the quantity of graduates and increase student confidence in tertiary institutions.Keywords:Prediction, Student Graduation, Naive Bayes Algorithm. 

scholarly journals IMPLEMENTASI KLASIFIKASI NAIVE BAYES DALAM MEMPREDIKSI LAMA STUDI MAHASISWA (STUDI KASUS : UNIVERSITAS DHYANA PURA)

2021 ◽  
Vol 4 (2) ◽  
pp. 202-209
Author(s):  
Kelvin Hennry Loudry Malelak ◽  
I Made Dwi Ardiada ◽  
Gerson Feoh
Keyword(s):  
Data Mining ◽  
Undergraduate Students ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Accuracy Rate ◽  
Testing Data ◽  
Study Programs ◽  
Bayes Algorithm ◽  
Academic Year

Under normal conditions, undergraduate or undergraduate students from a university can complete their studies for 4 years or 8 semesters. In fact, many students complete their study period of more than 4 years. Is known that in fact in the 2015/2016 academic year there were 744 people who were accepted as students. Of the 744 people who were accepted, 405 people had completed a study period of about 4 years and the remaining 39 people completed their studies for 5 years and 300 of them did not continue their studies. Based on the problem on, so This study implements a classification that can help Dhyana Pura University in predicting the length of study for students who are currently studying in various study programs at Dhyana Pura University. The author's method serves in the classification to predict long student study period is the Naive Bayes algorithm. By using the Java-based Rapid Miner tool to classify graduation data. Then the implementation of data mining which is divided into 968 training data and 193 data testing data with naive Bayes has succeeded in obtaining an accuracy rate of 100% which also has very good parameters.

scholarly journals Application of Data Mining with Classification Methods for Promotion of New Student Admissions at Muhammadiyah University of Sidoarjo Using Web-Based Naïve Bayes Algorithm

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Vianti Widyasari ◽  
Arief Senja Fitrani
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Classification Method ◽  
New School ◽  
School Year ◽  
New Students ◽  
Bayes Algorithm ◽  
New Student

The University of Muhammadiyah Sidoarjo (UMSIDA) is one of Indonesia's superior and innovative private colleges in developing IPTEKS based on Islamic values for community welfare. UMSIDA that has stood long enough with the number of students received in each year is quite a lot. Each new school year opening, this private college regularly organizes new student admissions (PMB) activities. Admission for new students (PMB) at UMSIDA can be done at pmb.umsida.ac.id. Therefore, research aims to create data mining applications classification method with the algorithm Naïve Bayes. This research uses the classification method used to Megukur accuracy level. To predict the promotion of new students receiving Muhammadiyah Sidoarjo University (UMSIDA) can be done using the Naïve Bayes algorithm with 7 predefined variables. Offline and online predictor of the dataset of 2601 data is divided into 2 as many as 70% of 2000 Training data and as much as 30% from 601 of Testing data.

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