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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Scientific Journal of Informatics
Agung Wibowo
Universitas Ngudi Waluyo Semarang
Computer Science & IT

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The Utilization of Naive Bayes and C.45 in Predicting The Timeliness of Students’ Graduation Wibowo, Agung; Manongga, Danny; Purnomo, Hindriyanto Dwi
Scientific Journal of Informatics Vol 7, No 1 (2020): May 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i1.24241

Abstract

An assessment of the success of a college is if the student's graduation rate is on time and high every year. The timeliness of students' graduation can be influenced by several factors. This study aims to determine the profile of the students who graduated both on time and not on time given a certain graduation predicate set by the institution and to know the factors influencing students’ gradution. The model used in this study using the NBC to determine the graduation pattern and the Decision tree to determine the influencing factors. In calculating the NBC algorithm using rapidminer, it was found that the profiles of students who graduated on time and late with the predicate of less satisfactory, satisfactory, very satisfactory and cumlaude. In the Desicion Tree calculation, the highest gain values are obtained in the IPK3, IPS1 and IPK2 attributes. This research needs to be developed further by increasing the number of attributes and data, and it is necessary to make a system to determine the accuracy of students’ graduation from the patterns that have been produced so that it can help universities to increase the level of students’ graduation every year.
The Utilization of Naive Bayes and C.45 in Predicting The Timeliness of Students’ Graduation Wibowo, Agung; Manongga, Danny; Purnomo, Hindriyanto Dwi
Scientific Journal of Informatics Vol 7, No 1 (2020): May 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i1.24241

Abstract

An assessment of the success of a college is if the student's graduation rate is on time and high every year. The timeliness of students' graduation can be influenced by several factors. This study aims to determine the profile of the students who graduated both on time and not on time given a certain graduation predicate set by the institution and to know the factors influencing students’ gradution. The model used in this study using the NBC to determine the graduation pattern and the Decision tree to determine the influencing factors. In calculating the NBC algorithm using rapidminer, it was found that the profiles of students who graduated on time and late with the predicate of less satisfactory, satisfactory, very satisfactory and cumlaude. In the Desicion Tree calculation, the highest gain values are obtained in the IPK3, IPS1 and IPK2 attributes. This research needs to be developed further by increasing the number of attributes and data, and it is necessary to make a system to determine the accuracy of students’ graduation from the patterns that have been produced so that it can help universities to increase the level of students’ graduation every year.
Prediksi Predikat Kelulusan Mahasiswa Menggunakan Naive Bayes dan Decision Tree pada Universitas XYZ Agung Wibowo; Abdul Rohman
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 12, No 2 (2022): December
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/expert.v12i2.2810

Abstract

The success of universities in managing learning standards can be seen from the number of students who can graduate with high predicates and on time. The graduation predicate of a student is influenced by several factors. This study aims to find out the profile of students who graduated with a set predicate and what are the factors that influence it. The method used in this study is the Cross Industry Standard Process for Data Mining CRISP-DM by utilizing the Naïve Bayes algritma in looking for graduate predicate patterns and Decision Tree in looking for causative factors. In the calculations using the NBC algorithm it was found that the profiles of students who passed the predicate were less satisfactory, satisfactory, very satisfactory and with praise . In the Desicion Tree calculation, the highest gain value is obtained at the attributes of IPK4, IPS5 and IPK5. The factors that most influence graduation are the cumulative achievement index in semesters 4 and 5 and the achievement index in semester 5. The pattern of graduating with predicate can be known from the second year of the incoming student to the third year. This research needs to be developed again by increasing the number of attributes and data, and it is necessary to create a system for determining student graduation predicates from the patterns that have been produced in order to help universities to improve the quality of student graduation in each period.
Co-Authors Abdul Rohman Danny Manongga Hindriyanto Dwi Purnomo Yayi Suryo Prabandari