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All Journal Syntax Literate: Jurnal Ilmiah Indonesia JMM (Jurnal Masyarakat Mandiri) Jurnal Informatika Universitas Pamulang Jurnal Abadimas Adi Buana Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Infotekmesin Jurnal Sistem Komputer dan Informatika (JSON) Community Empowerment Journal Of Human And Education (JAHE) JHT: Journal of Hospitality and Tourism Journal of Manufacturing and Enterprise Information System SmartComp Journal of Information System and Computer Jurnal Informatika: Jurnal Pengembangan IT Science Information System and Technology ALMUISY: Journal of Al Muslim Information System
Wildani Eko Nugroho, Wildani Eko
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Journal : Infotekmesin

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Metode Naive Bayes Dalam Menentukan Program Studi Bagi Calon Mahasiswa Baru Wildani Eko Nugroho; Ali Sofyan; Oman Somantri
Infotekmesin Vol 12 No 1 (2021): Infotekmesin: Januari 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i1.491

Abstract

In a university, determining a study program for prospective students is something that is often done to focus on prospective students so that they are in accordance with their competencies. This is a very important hope, because prospective students can develop self-competence according to their academic abilities. This research method uses several stages, including data cleaning, data collection, determining criteria, determining probability, and final testing. The Naïve Bayes method with a case study at the Private Madrasah Aliyah PAB 6 Helvetia and testing of 100 student data with an accuracy rate of 90% is a previous research. The purpose of this study was to make a classification of majors based on the criteria, while in this study the aim of making a classification of study programs for prospective new students. In this study, the same method was used but the number of data records was different, the test data was 1671 student data records, the data was obtained from 2256 data records.From the total data records were 2256, after data cleaning and data collection were carried out, 1671 test data were obtained. In the test data, there are several probability values that contain various criteria and attributes used to determine the classification of study programs for prospective new students. The number of data records is divided into 2 parts, the first is used for training data with 1158 data with a percentage of 70%, and testing data with 513 data records with a percentage of 30%. From the test results with the same method with different number of data records, the accuracy rate is from 90% to 96% with an accuracy value of 96.68%. From this accuracy value shows that the classification results obtained show the Pharmacy DIII study program.
Optimalisasi Metode Naive Bayes untuk Menentukan Program Studi bagi Calon Mahasiswa Baru dengan Pendekatan Unsupervised Discretization Wildani Eko Nugroho; Teguh Prihandoyo; Oman Somantri
Infotekmesin Vol 13 No 1 (2022): Infotekmesin: Januari, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i1.1048

Abstract

The admission of prospective new students must consider various procedures to direct prospective new students in determining the study program they are interested in. This study will discuss the optimization of the Naive Bayes method to determine the study program or major for prospective new students with the Unsupervised Discritization method approach. There are several stages of research methods carried out in this study, including Data Cleaning, Data Collection, Criteria Determination, Probability Determination, and Data Testing. This research has been carried out using the same method, namely the Naïve Bayes method which is used to classify the interests of prospective new students in determining the study program with an accuracy value of 96.68%. Ongoing research uses the same method, namely Naive Bayes, then optimization is carried out with the Unsupervised Discretization method approach. For data testing, there are 1671 student data records. After testing with the same method and optimizing it, the accuracy value from 96.68% became 97.66% with the classification results showing the DIII Pharmacy study program. The purpose of this research is to produce a classification in determining the study program or major for prospective new students using the Naïve Bayes method by the optimization of the Unsupervised Discretization method. From the results of testing the data, the Naïve Bayes method after optimization with the Unsupervised Discretization method is very good compared to the application before optimization.
Co-Authors Abdul Basit Abdul Rohman Supriyono Adam, David Bani Ali Sofyan Arif Rakhman Bakhar, Muhamad Chotimatun Chasanah, Feti Christian Yulianto Rusli Eko Budi Raharjo, Eko Budi Eko Budihartono, Eko Endang Astuti Firmansyah, Hasbi Ghea Dwi Rahmadiane Ghea Dwi Rahmadiane Heru Saputro Humam, M. Humam, Mohammad Ida Afriliana Jatmiko Indriyanto Kurnia Bakti, Very Lukmanul Khakim M. Teguh Prihandoyo Meiriska Intan Syarifudin Mohammad Humam Much Rifqi Maulana Muhammad Teguh Prihandoyo Muljono, - Nishom, M Nur Azizah Nurohim Nurohim Oman Somantri Prihandoyo , M. Teguh Robiatul Adawiyah Sigit Ardianto Sulistyaningrum Syamsiar, Syamsiar Teguh Prihandoyo Very Kurnia Bakti, Very Kurnia Wibowo, Pranoto Yerry Febrian Sabanise Yerry Febrian Sabanise, Yerry Febrian