Article Per Year (5 Year)
p-Index From 2019 - 2024
1.513
P-Index
This Author published in this journals
All Journal Dinamika Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Komtika (Komputasi dan Informatika) Informasi Interaktif Respati Techno
Sudarmawan, Sudarmawan
UNiversitas AMIKOM Yogyakarta
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Techno

 1 
Prediksi Kelulusan Tepat Waktu Menggunakan Metode C4.5 DAN K-NN (Studi Kasus : Mahasiswa Program Studi S1 Ilmu Farmasi, Fakultas Farmasi, Universitas Muhammadiyah Purwokerto) Eko Purwanto; Kusrini Kusrini; Sudarmawan Sudarmawan
Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) Vol 20, No 2 (2019): Techno Volume 20 No.2 Oktober 2019
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/techno.v20i2.5160

Abstract

The graduation profile is one of the key elements for the accreditation standard of higher education. It mirrors the performance of the applied educational system within a period of time. The better it is, the better the accreditation will be. In support of this, a graduation prediction may be conducted to the academic database of the students. It is of pivotal to trace and classify the historical data into the data training and data testing, thus, to predict the on time-graduation. The step is importantly done to help decide the better management of learning processes. This study was therefore done to analyse certain variables applied to predict the on time-graduation using the algorythms of C.45 and K-Nearest Neighbour (K-NN). The data mining was done to the academic database of the students of the Pharmacy study programme, Pharmacy Faculty, Muhammadiyah University of Purwokerto by adding certain variables into the process. The data was then classified into the data training and data testing. Backward selection was done to select the best and most influential variables for the dataset. The study further resulted that by using the algorhythm of C.45 and backward selection, the accuracy of the graduation reached 92.75%. It is different from the acurracy the K-NN and backward selection showed that reached 96.14%. The result confirmed that the KNN showed the better accuracy than the C.45. It considerably benefitted the study programme to make better decisions on increasing the quality of services, in particular that of leraning processes.
Co-Authors Abdillah, M A Abidarin Rosidi Adiwisanghagni, Mohammad Agung Mulyono Agung, Kris Ahmad Zarkasih Amrullah, Ahmad Afief Arief Setyanto Arif Rahman Arik Sofan Tohir Sofan Tohir, Arik Sofan Tohir Bambang Soedijono W A, Bambang Soedijono Eko Purwanto Ema Utami Fatkhurohman, Ari Hendrik Setiawan, Hendrik Hermanto (STMIK AMIKOM Yogyakarta) JOKO PRAYITNO Kusrini Kusrini Kusrini Kusrini Mohammad Suyanto Napianto, Riduwan Nugroho, Muhammad Agung Nurmalasari, Maulidya Dwi Putra, Afrizal Yudano Perdana Rumini - Sukasna, Sukasna Tri Yusnanto