Article Per Year (5 Year)
p-Index From 2021 - 2026
1.978
P-Index
This Author published in this journals
All Journal JIM Pendididikan Bahasa dan Sastra Indonesia ADMINISTRASI PUBLIK IKRA-ITH ABDIMAS Jurnal Onoma: Pendidikan, Bahasa, dan Sastra Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Bahasa dan Sastra East Asian Journal of Multidisciplinary Research (EAJMR) Arif: Jurnal Sastra dan Kearifan Lokal Journal of Embedded Systems, Security and Intelligent Systems Jurnal Penelitian Pendidikan Indonesia kawanad
Yusri Yusuf
Unknown Affiliation
Religion Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Embedded Systems, Security and Intelligent Systems

 1 
Comparison Of Automated Machine Learning and Manual Modeling In Data Science Education Toward Pipeline Understanding and Model Interpretability: A Qualitative Experimental Study Abdi Anugrah; Wahyullah; Yusri Yusuf; Zulham Abidin; Dian Kumala Azis; Fandi Armawan
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.11528

Abstract

The rapid development of Automated Machine Learning (AutoML) has transformed modeling practices in data science by automating preprocessing, feature selection, and hyperparameter tuning. However, its pedagogical implications in higher education remain underexplored. This study aims to compare the impact of AutoML and manual modeling approaches on students’ understanding of machine learning pipelines and model interpretability. A qualitative quasi-experimental design was employed involving final-year undergraduate students enrolled in a Data Science course. Participants were divided into two groups: one using AutoML tools and the other applying manual modeling procedures. Data were collected through in-depth interviews, learning observations, reflective reports, and artifact analysis of coding assignments. Thematic analysis was used to identify differences in conceptual understanding and learning experiences. The findings indicate that manual modeling fosters deeper structural comprehension of pipeline stages, including preprocessing, feature engineering, and evaluation mechanisms. In contrast, AutoML enhances efficiency and reduces technical barriers but tends to obscure internal modeling processes, potentially limiting interpretative insight. These results highlight important implications for curriculum design in data science education, suggesting the need for balanced integration between automation tools and foundational modeling practices.
Co-Authors Abdi Anugrah Budi Arianto, Budi Deni Iskandar Denni Iskandar Dian Kumala Azis Eli Jamilah Mihardja Fandi Armawan Harun, Moh Harun, Mohd. Herinadharma, Vicky Arief Herman R, Herman R Hermansyah Hermansyah Indah Handayani Jarjani Usman Lestari, Linda Mantovani, Ichsan Mohd. Harun Muhammad Banu Lailika Muhammad Idham Munawar Munawar Nisak, Khairatin Nteseo, Rian Wahyudi Putra Nuramalina Harvianti Nurhikmah H Nurhiyasti, Mira Pattaufi, Pattaufi Prasatya, Andityas Bima Raiyana Putri Kana Ramli Ramli Ramli Razali Razali . Saiful Tari Rahayu, Tari Taufik, Unirma M. Wahyullah Wildan Wildan Wildan Wildan Wildan Yusri Yusuf, Indah Handayani Zulham Abidin