Article Per Year (5 Year)
p-Index From 2020 - 2025
3.223
P-Index
This Author published in this journals
All Journal Jurnal Teknoin Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi JOIN (Jurnal Online Informatika) Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Riset Informatika EXPLORE Automata EXPLORE Jurnal Komtekinfo Explore
Ahmad Raf'ie Pratama
Universitas Islam Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mechanical Engineering

Published : 27 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

 1 
Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk Sistem Rekomendasi Program Studi Ahmad Rafie Pratama; Rio Rizki Aryanto; Lizda Iswari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.919 KB) | DOI: 10.29207/resti.v5i5.3392

Abstract

Selecting a major can be quite difficult for prospective college students. The choice may have an effect not only on their academic life, but also on their career path. Due to some restrictions as the impact of the COVID-19 pandemic, universities must find novel ways to reach prospective students and assist them in choosing their majors, one of which is a college major recommendation system. This system can assist prospective students in determining the most appropriate majors for them based on data from the current students. Unlike other existing systems that employ either a rule-based or fuzzy model, this study employs a machine learning approach using data from undergraduate students at Universitas Islam Indonesia. This paper aims to compare several clustering models (i.e., K-means, Agglomerative, Birch, and DBSCAN) for the purpose of categorizing current students, to which the results will be used for classification purposes using various approaches (i.e., single stage vs. multistage), algorithms (i.e., multinomial logistic regression, random forest, and support vector machine), and scenarios (i.e., with or without GPA-based label). Our findings indicate that the K-means model outperformed all other clustering models and that the single stage with random forest classification model performed the best across all scenarios.
Co-Authors Adita Rianto Ahmad Fathan Hidayatullah, Ahmad Fathan Ahmad Shofwan Afif Alringga Rizky Widangsa AMIN NUR ROHIM Andri Setyadi Anggita Perdami Ikayanti Anggita Perdami Ikayanti Anggraeni Dias Saputri Arif Taufiq Mahendra Pratama Arwan Ahmad Khoiruddin Atika Tsamara Beni Ike Hendra Kuswara Erika Ramadhani Galih Rahmadi Geraldy Yusuf Pralampita Hidayat , Taufiq Ikayanti, Anggita Perdami Istanto, Tomy Lizda Iswari Muhamad Rovi Muhammad Raffi Akhyari Muhammad Rifqi Ramadhani Nastiti, Kartika Rizqi Nina Mia Aristi Nunu Vadila Rio Rizki Aryanto Septia Rani Setiawan, Mukhammad Andri Taufik Hidayat Teguh Dwiputra