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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) G-Tech : Jurnal Teknologi Terapan de-lite
Wella
Universitas Multimedia Nusantara
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Engineering Languange, Linguistic, Communication & Media Other

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Sistem Berbasis Web menggunakan Framework CodeIgniter untuk Pengelolaan Kurikulum SD Santa Patricia Vionica Vionica; Wella
G-Tech: Jurnal Teknologi Terapan Vol 8 No 2 (2024): G-Tech, Vol. 8 No. 2 April 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i2.3945

Abstract

Perubahan kurikulum didasarkan pada pemikiran tentang tantangan masa depan, yaitu tantangan abad kedua puluh satu, yang ditandai dengan abad ilmu pengetahuan, masyarakat berbasis pengetahuan, dan kemampuan masa depan. Sekolah Dasar Santa Patricia adalah salah satu sekolah yang menggunakan kurikulum 2013 saat ini. Perangkat lunak Microsoft Excel, dalam format yang sudah ada, masih digunakan oleh siswa di Sekolah Dasar Santa Patricia untuk mengolah nilai mereka. Pola penilaian rapor Kurikulum 2013 diketahui sangat kompleks, dan evaluasi yang menggunakan Microsoft Excel tidak optimal. Untuk mengatasi kekurangan yang ada, sistem penilaian akademik berbasis web dibangun berdasarkan alasan ini. Untuk membuat sistem ini, metode Extreme Programming (XP) digunakan karena dapat disesuaikan dengan kebutuhan sekolah. Selain itu, sistem ini dibangun menggunakan framework CodeIgniter, yang membantu developer membuat website yang lebih mudah digunakan. Hasil penelitian ini menghasilkan sistem penilaian akademik berbasis web, dan dari hasil pengujiannya, dapat disimpulkan bahwa sistem ini secara umum sangat disukai oleh pengguna.
Leveraging Machine Learning to Predict Academic Specialization Pathways in Higher Education Rendy Wirawan Tamrin; Wella
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6941

Abstract

This study developed a machine learning-based model to predict academic concentration selection among information systems students at Universitas Multimedia Nusantara (UMN). A survey of 125 students from the 2024 cohort revealed that 90% experienced difficulties in choosing a specialization, primarily due to limited information on course relevance, unclear academic pathways, and career uncertainty. While the survey provides a contextual background, the predictive model was trained using historical academic performance data from the 2021–2023 cohorts. The three classification algorithms, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost) were implemented following the CRISP-ML methodology. To address class imbalance in the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, followed by hyperparameter tuning and feature selection. The Random Forest model demonstrated superior performance, achieving an accuracy of 78.08% on the 2021–2022 cohort data, outperforming Decision Tree and XGBoost across all experimental settings. This result highlights Random Forest's robustness in this context, particularly after the integration of SMOTE and optimization procedures. The main contribution of this study lies in the application of machine learning for academic pathway prediction in an Indonesian higher education setting, providing a data-driven decision support tool to assist students in making informed and personalized specialization choices.
User-Centered Design for a Multi-Role Academic Support System in Higher Education Fonita Theresia Yoliando; Wella; Ririn Ikana Desiyanti
de-lite Vol. 5 No. 1 (2025): July 2025
Publisher : Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37312/de-lite.v5i1.10105

Abstract

Effective academic support is a critical component of student success in higher education. As institutions increasingly adopt student-centered learning models, academic counseling emerges as a key mechanism to provide guidance and address individual learning needs. Despite its importance, the counseling process often suffers from structural limitations. Lecturers face heavy workloads that constrain their ability to offer personalized support, while counseling records are typically fragmented and difficult to track. This study aims to design and develop a multi-user system to streamline academic advising and student progress monitoring. The platform incorporates four essential functions: academic scheduling, counseling engagement, performance tracking, and study plan records. It is intended to serve a range of users, including students, faculty advisors, academic staff, and institutional leaders, by facilitating timely and data-informed decision-making. To ensure the system aligns with user needs, a User-Centered Design (UCD) methodology was applied. Focus group discussions were conducted with stakeholders across various roles, including students, lecturers, program heads, deans, and academic staff. The insights were translated into functional requirements, flowcharts, information architecture, and user interface design. The paper also discusses the iterative design process, highlights challenges encountered during development, and offers recommendations for institutions seeking to implement integrated academic support systems.
Co-Authors Fonita Theresia Yoliando Rendy Wirawan Tamrin Ririn Ikana Desiyanti Vionica Vionica