Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB
Sara Khusnul Mumtazah
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Penerapan Algoritma Decision Tree untuk Prediksi Kelulusan Mahasiswa Berdasarkan Data Akademik Menggunakan RapidMiner Laela Nur Rohmah; Sara Khusnul Mumtazah; Alvina Damayanti; Amali
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i1.4332

Abstract

Higher education has an important role in the long-term development of each individual. One of the most important indicators of success for a high-performing educational institution is student achievement. There are several factors that might influence student achievement, including academic, demographic, and socioeconomic factors. This study employs the Decision Tree algorithm, which is one of several effective algorithms for making predictions or analyzing large amounts of data. This study aims to determine whether the Decision Tree algorithm can be used to predict student achievement by gathering information on accuracy, precision, and recall obtained during data collection. This study used RapidMiner tools to create a Decision Tree model and was carried out with the following steps: data collection, data analysis, Decision Tree modeling, method development, and results evaluation. Data collection on the dataset will be divided into two parts: 70% for training and 30% testing. The results of the study on the decision tree algorithm show that it has a good performance with a high accuracy of 73.17%. It also performs well in predicting graduate students with a precision of 74.05% and a recall of 93.82%, as well as dropout students with a precision of 73.02% and a recall of 80.05%.
Co-Authors Alvina Damayanti Amali Laela Nur Rohmah