Department recommendations for prospective students Vocational High School of information technology with Naïve 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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OPTIMASI METODE NAIVE BAYES DENGAN FEATURE SELECTION INFORMATION GAIN UNTUK PREDIKSI KETERLAMBATAN PEMBAYARAN SPP SEKOLAH

Jurnal Ilmiah SINUS ◽

10.30646/sinus.v17i1.378 ◽

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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Classification of community opinion on the use of the Transjakarta bus based on twitter social network using naïve bayes method

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1010/1/012030 ◽

2021 ◽

Vol 1010 ◽

pp. 012030

Author(s):

B. D Meilani ◽

R K Hapsari ◽

I F Novian

Keyword(s):

Social Network ◽

Naive Bayes ◽

Naïve Bayes ◽

Bayes Method ◽

Naive Bayes Method

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

International Journal of Computer Applications ◽

10.5120/ijca2017913842 ◽

2017 ◽

Vol 165 (4) ◽

pp. 1-5 ◽

Cited By ~ 4

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

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Classification of Trends in Lecturer Research Fields Using Naive Bayes Method

10.1109/ieit53149.2021.9587385 ◽

2021 ◽

Author(s):

Sulthan Rafif ◽

Pramana Yoga Saputra ◽

Moch Zawaruddin Abdullah

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Bayes Method ◽

Research Fields ◽

Naive Bayes Method

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Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ

Jurnal ULTIMATICS ◽

10.31937/ti.v12i2.1715 ◽

2020 ◽

Vol 12 (2) ◽

pp. 104-107

Author(s):

Nurhayati . ◽

Nuraeny Septianti ◽

Nani Retnowati ◽

Arief Wibowo

Keyword(s):

Data Mining ◽

Information Technology ◽

Data Processing ◽

Naive Bayes ◽

Naïve Bayes ◽

Bayes Method ◽

Processing Data ◽

Student Graduation ◽

Phase Data ◽

Bayes Algorithm

Data processing is imperative for the development of information technology. Almost any field of work has information about data. The data is made use of the analysis of the job. Nowadays, information data is imperatively processed to help workers in making decisions. This study discusses student prediction graduation rates by using the naïve Bayes method. That aims at providing information to college if they can use it properly to utilize the data of students who graduated by processing data mining. Based on the data mining process, steps founded that used producing information, namely predicting student graduation on time. The method of this study is Naïve Bayes with classification techniques. At this study, researchers used a six-phase data mining process of industry crossing standards in data mining known as CRISP-DM. The results of research concluded that the application of the Naive Bayes algorithm uses 4 (four) parameters namely ips, ipk, the number of credits, and graduation by getting an accuracy value of 80.95%.

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