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Maharani, Wianti
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Penerapan Data Mining untuk Memprediksi Kelulusan Mahasiswa Menggunakan Decision Tree Shevchenko, Angelus Galang; Maharani, Wianti; Wijaya, Andri
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

This study aims to apply data mining techniques to predict student graduation using the C4.5 Decision Tree algorithm in the Information Systems Study Program, Faculty of Science and Technology, Musi Charitas Catholic University. The data used in this research consist of academic records of students from 2018 to 2020, including Grade Point Average per semester (GPA) and Cumulative Grade Point Average (CGPA). The research method follows the Knowledge Discovery in Database (KDD) stages, namely data selection, preprocessing, transformation, data mining, and interpretation. Model development and evaluation were conducted using RapidMiner software with the 10-Fold Cross Validation method. The results indicate that Semester 8 GPA is the most influential attribute in determining student graduation status, followed by Semester 4 GPA as a supporting indicator. The generated decision tree model achieved an accuracy rate of 75.68%, indicating a good predictive performance. These findings demonstrate that the C4.5 Decision Tree algorithm can serve as an effective decision-support tool for early detection of students at risk of delayed graduation, thereby assisting academic institutions in improving on-time graduation rates and academic management quality.
Perancangan Data Warehouse Menggunakan Metode Nine Step Pada PT. XYZ Shevchenko, Angelus Galang; Maharani, Wianti; Wijaya, Andri
Journal Of Informatics And Busisnes Vol. 3 No. 3 (2025): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i3.3796

Abstract

PT. XYZ operates in the food distribution sector and handles a large number of inbound and outbound goods transactions each day. The high transaction volume creates challengess in managing inventory effectively, highlighting the need for an integrated data management solution. This research aims to develop a data warehouse for PT. XYZ by applying the Kimball Nine-Step methodology. Data were collected through interviews, field observations, and a review of relevant literature. The data warehouse design process includes identifying fact tables and dimension tables related to receiving and shipping activities, followed by the implementation of the Extract, Transform, Load (ETL) process using SQL Server Management Studio 19. The findings indicate that the proposed data warehouse is capable of integrating operational data and presenting inventory information through reports generated using Microsoft Excel. The system supports improved stock control and enables management to make faster and more accurate decisions.
Co-Authors Andri Wijaya Shevchenko, Angelus Galang