Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Dinda : Data Science, Information Technology, and Data Analytics
Tua, Anri Hafiz
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Penerapan Penerapan Jaringan Syaraf Tiruan Untuk Pengklasifikasi Mahasiswa Berpotensi Drop Out Lubis, Dikko Rizky Bintang; Tua, Anri Hafiz; Siregar, Muharram Soleh; Armansyah, Armansyah
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.8225

Abstract

This research aims to classify students who have the potential to drop out using the Multilayer Perceptron (MLP) Backpropagation Artificial Neural Network method. The dataset consists of 1337 students which are then divided into training and test data with a ratio of 80%:20%. The classifier results show an accuracy of 94.7% for training data and 95.9% for test data. These findings indicate that the Backpropagation method with the MLP model is able to provide a very high level of accuracy, on average reaching 95%. This research is important because it can help campuses identify students who have the potential to drop out and provide timely intervention to prevent this. In this way, drop out prevention efforts can be improved, ensuring student academic success.
ROC and COPRAS Methods in New Student Admissions Application (PPDB) MAN HUMBANG HASUNDUTAN Tua, Anri Hafiz; Putri, Raissa Amanda
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.2015

Abstract

The development of information and communication technology, especially in the education sector, has opened up opportunities to increase efficiency and transparency in various processes, including New Student Admissions (PPDB). MAN Humbang Hasundutan faces challenges in manually screening hundreds of prospective students every year, which often introduces bias and inaccuracies in the selection process. Therefore, this research aims to develop a web-based PPDB application with the integration of the Rank Order Centroid (ROC) method for weighting criteria and Complex Proportional Assessment (COPRAS) for ranking. The ROC method assigns weights to criteria based on their level of importance, while the COPRAS method determines the ranking by taking into account the level of significance and utility of alternatives. The implementation of this application enables the processing of prospective student data quickly and objectively, as well as increasing the fairness and transparency of the selection process. Based on the results of previous research, the COPRAS method with ROC weighting has proven to be effective in assisting decision making in various fields. The proposed PPDB application is expected to simplify the selection process at MAN Humbang Hasundutan while increasing the credibility of the educational institution.
Co-Authors Armansyah Armansyah Lubis, Dikko Rizky Bintang Raissa Amanda Putri Siregar, Muharram Soleh