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All Journal Journal of Embedded Systems, Security and Intelligent Systems Journal of Computers, Informatics, and Vocational Education
Zulham Abidin
Universitas Negeri Makassar
Computer Science & IT Education Engineering Social Sciences Other

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Pengembangan Sistem E-Document Penempatan Tenaga Kerja dan Perluasan Kesempatan Kerja Disnaker Kota Makassar Berbasis Web Zulham Abidin; Iwan Suhardi; Abdul Wahid
Journal of Computers, Informatics, and Vocational Education Volume 1 Issue 3, November (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

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Abstract

Penelitian ini menyoroti masalah pengelolaan data pengesahan kartu AK1 pada bidang penempatan tenaga kerja dan perluasan kesempatan kerja di Dinas Ketenagakerjaan Kota Makassar yang masih menggunakan sistem konvensional dengan pencatatan manual. Tujuannya adalah mengembangkan solusi e-document untuk memudahkan pengelolaan data peserta pengesahan kartu AK1. Metode yang digunakan adalah pengembangan agile mode pengembangan scrum dengan pengujian menggunakan ISO 25010 yang mencakup delapan aspek pengujian. Hasil penelitian menunjukkan tingkat kelayakan yang baik pada aspek suitability, reliability, usability, efficiency, maintainability, portability, security, dan compatibility. Dengan nilai kategori yang mencapai "Dapat Diterima" hingga "Sangat Baik" dan hasil laporan keamanan yang menyatakan tidak adanya kerentanan berisiko tinggi, sistem yang dikembangkan sangat layak digunakan. Sistem E-Document Disnaker Kota Makassar Berbasis web ini telah memenuhi standar kualitas sistem, kompatibilitas dengan berbagai browser, serta dapat meningkatkan efisiensi pengelolaan data pengesahan secara signifikan.
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 Abdul Wahid Dian Kumala Azis Fandi Armawan Iwan Suhardi Wahyullah Yusri Yusuf