scholarly journals Data Mining Predicts the Need for Immunization Vaccines Using the Naive Bayes Method

2021 ◽  
Vol 2 (3) ◽  
pp. 93-101
Author(s):  
Arri Widyanto
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Naive Bayes Method

scholarly journals Application of Data Mining to Predict Procurement of Office Writing in Al-Ikhwan Middle School using Naïve Bayes Method

2020 ◽  
Vol 4 (2) ◽  
pp. 39
Author(s):  
Aida Sopia ◽  
Muhammad Syahrizal
Keyword(s):  
Data Mining ◽  
Middle School ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Educational Institutions ◽  
Bayes Method ◽  
Expiry Date ◽  
Long Time ◽  
Naive Bayes Method ◽  
Analyze Data

Office stationery (ATK) is now a necessity for almost everyone, especially for corporate instances or educational institutions. The need for office stationery is often an unexpected need to buy, this is what makes some educational institutions overwhelmed in doing their work, when they find out their office stationery is out of stock, so it is not uncommon to make work in company or institutional instances education is not completed on time, one of the ways to be more efficient is by implementing data mining to predict the purchase of office stationery (ATK) at the company or educational institution's intents, especially at the AL IKHWAN Middle School in Tanjung Morawa. The Naive Bayes method is used to analyze data in pattern recognition and predict purchase of office stationery (ATK) at AL IKHWAN Middle School in Tanjung Morawa. The data needed is the data for the purchase of office stationery (ATK) last month as test data, calculated from the date of the first purchase until the expiry date of office stationery (ATK) at AL IKHWAN Middle School in Tanjung Morawa. The results of this study are to be able to predict whether the office stationery (ATK) at AL IKHWAN Middle School in Tanjung Morawa can be bought back, or it can still be used for a long time, and if more than four types of stationery at the AL IKHWAN Middle School in Tanjung Morawa the lack of writing instruments from the two then the purchase of new stationery is feasible to do, from the amount of data occurring out of stationery

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 Comparison of Classification Data Mining C4.5 and Naïve Bayes Algorithms of EDM Dataset

TEM Journal ◽  
2021 ◽  
pp. 1738-1744
Author(s):  
Joseph Teguh Santoso ◽  
Ni Luh Wiwik Sri Rahayu Ginantra ◽  
Muhammad Arifin ◽  
R Riinawati ◽  
Dadang Sudrajat ◽  
...  
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Educational Data Mining ◽  
Naïve Bayes ◽  
T Test ◽  
Bayes Method ◽  
Average Accuracy ◽  
Difference Test ◽  
Naive Bayes Method ◽  
Better Than

The purpose of this research is to choose the best method by comparing two classification methods of data mining C4.5 and Naïve Bayes on Educational Data Mining, in which the data used is student graduation data consisting of 79 records. Both methods are tested for validation with 10-ford X Validation and perform a T-Test difference test to produce a table that contains the best method ranking. Different results were obtained for each method. Based on the results of these two methods, it is very influential on the dataset and the value of the area under curve in the Naïve Bayes method is better than the C4.5 method in various datasets. Comparison of the method with the 10-Ford X Validation test and the T-Test difference test is that the Naïve Bayes method is better than C4.5 with an average accuracy value of 73.41% and an under-curve area of 0.664.

scholarly journals IMPLEMENTASI METODE C4.5 DAN NAIVE BAYES BERBASIS ADABOOST UNTUK MEMPREDIKSI KELAYAKAN PEMBERIAN KREDIT

2017 ◽  
Vol 1 (2) ◽  
pp. 65
Author(s):  
Putu Gede Surya Cipta Nugraha ◽  
Gede Rasben Dantes ◽  
Kadek Yota Ernanda Aryanto
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Implementation Process ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Accuracy Level ◽  
Precision Level ◽  
The Future ◽  
Mining Methods ◽  
Naive Bayes Method

At PT. BPR XYZ credit problems is a very vital issue, where if many debtors are delinquent in payment it will increase the NPL value of the bank itself. Increasing the NPL value above 5% indicates that the bank is not healthy. From the above problems, then in this study aims to perform the implementation process of data mining methods to determine the accuracy level of prediction of creditworthiness at PT. BPR XYZ, so that the future of credit problems can be overcome. Data mining methods used in the prediction process are C4.5 and Naïve Bayes methods, where both methods are implemented and the accuracy level comparison process is used to see which method is more accurate in predicting creditworthiness. Both methods are also embedded AdaBoost method with the aim of increasing the accuracy in the process of prediction of creditworthiness feasibility. The result obtained from the comparison of method accuracy level, stated that the better accuracy is C4.5 method that is 90.00% with the precision level of 86.67%. As for the accuracy of Naïve Bayes method that is equal to 70.00% with the precision level of 79.71%. Then with the addition of AdaBoost method in predicting creditworthiness proved to increase the higher accuracy value of 91.54% in method C4.5 and by 78.13% in Naïve Bayes method. From the description above, with the implementation of AdaBoost method on the method of C4.5 and Naïve Bayes can improve the accuracy of the prediction of creditworthiness of PT. BPR XYZ. In addition, the implementation of the AdaBoost-based C4.5 method can be a recommendation for PT. BPR XYZ in conducting predictive process of credit worthiness in the future.

scholarly journals KLASIFIKASI METODE NAIVE BAYES UNTUK KELANCARAN PEMBAYARAN KREDIT LEASING SEPEDA MOTOR

2017 ◽  
Vol 8 (3) ◽  
pp. 146
Author(s):  
BUDI RAMADHANI
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Swarm Optimization ◽  
Number Of Children ◽  
Naive Bayes Method

Permasalahan yang sering timbul pada perusahaan leasing adalah banyaknya pelanggan yang mengalami kesulitan dalam membayar cicilannya, maka diperlukan suatu sistem yang dapat mengklasifikasikan konsumen yang masuk ke grup saat ini, kelompok kurang lancar dan konsumen yang masuk ke dalam kelompok tidak lancar dalam membayar cicilan cicilan sepeda motor. Sehingga sewa bisa mengatasi masalah awal. Sebuah perusahaan leasing harus memiliki data yang sangat besar. Banyak yang tidak menyadari bahwa pengolahan data data tersebut bisa memberikan informasi seperti klasifikasi data konsumen yang akan bergabung dengan perusahaan itu sendiri. Penerapan teknik data mining diharapkan dapat memberikan informasi yang berguna mengenai teknik klasifikasi data konsumen yang akan bergabung dengan grup saat ini, kelompok kurang lancar atau tidak lancar dalam membayar premi.Langkah penelitian meliputi pengumpulan dan pengujian data algoritma Naive Bayes. Dalam penelitian ini, kumpulan data yang digunakan adalah Customer, Employment, Number of Children, Status Houses, region, angsuran.Penelitian ini bertujuan untuk mengetahui Klasifikasi Metode Naive Bayes Berbasis Metode PSO Untuk Smooth Credit Leasing MotorcyclesHasil percobaan menggunakan metode Naïve Bayes untuk mengukur pengukuran lancar dan tidak lancar yang diperoleh pengukuran memiliki Naïve Baiyes tertinggi adalah 96,43% namun sekarang metode algoritma Naive Bayes Particle Swarm Optimization sebesar 96,88%, adalah akurasi namun baik Keywords: Current and Non Current, Naive Bayes Method Based PSO

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%.

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. 

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