Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Justek : Jurnal Sains Dan Teknologi Journal of Information Technology (JIfoTech) Jurnal Ilmiah Edutic : Pendidikan dan Informatika
Yuli Sopianti
Universitas Pendidikan Indonesia
Computer Science & IT Control & Systems Engineering Education Mathematics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Pengembangan Sistem Automation Feedback Berbasis Gemini Sebagai Pendukung Problem-Based Learning Sifa Fauzan; Eki Nugraha; Yuli Sopianti
Journal of Information Technology Vol. 6 No. 2 (2026): Journal of Information Technology
Publisher : Institut Shanti Bhuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46229/jifotech.v6i2.1152

Abstract

A preliminary observation of 31 eleventh-grade students in the Network and Application Engineering (SIJA) program at a vocational school in Cimahi found that all students (100%) had come to rely on generative AI such as ChatGPT whenever they encountered coding difficulties, with 86.7% reporting an inability to complete programming tasks independently without AI assistance. This AI dependency risks hindering the development of students' critical reasoning. This research aims to design, implement, and test an automated feedback system named AutoFeed to support the Problem-Based Learning (PBL) model in mitigating AI dependency while enhancing students' critical thinking skills in Informatics subjects at vocational high schools (SMK). The research methodology applies the Waterfall development model, encompassing requirements analysis, system design, software implementation, and testing phases. The application was built using a technology architecture consisting of React on the frontend, FastAPI (Python) on the backend, and MySQL as the database, integrating the Google Gemini 2.5 Flash model as the processing engine for hybrid (corrective and reflective) feedback through Socratic questioning techniques. The results of expert judgment based on LORI standards showed an average usability score of 4.60, workspace-PBL feature alignment scored 4.25, while the validation of AI pedagogical response accuracy scored 4.00. Functional testing further confirmed that all system features operated as intended but still require refinement. Thus, the AutoFeed system is confirmed feasible as a digital learning assistant (co-thinker), designed to minimize delayed feedback constraints and support students' critical reasoning, with its actual learning impact recommended for further empirical validation.
Web-Based Computational Thinking Assessment Using K-Nearest Neighbor and Rule-Based Adaptive Treatment Muhammad Izzuddin Al Fatih; Erlangga Erlangga; Yuli Sopianti
EDUTIC Vol 13, No 2: 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v13i2.35292

Abstract

Conventional database-learning assessments generally provide aggregate scores without showing which computational thinking components require further support. This study developed a web-based adaptive assessment system for vocational high school students by integrating K-nearest neighbor classification, adaptive hints, and rule-based treatment. The research applied a development approach using the Smart Learning Environment Establishment Guideline through analysis, design, development, implementation, and evaluation stages. The KNN model was developed using 64 empirical records from students in XI RPL 2 and XI RPL 3. Four weighted computational thinking indicators—decomposition, pattern recognition, abstraction, and algorithmic thinking—were used as classification features. KNN, RandomOverSampler–KNN, and Synthetic Minority Over-sampling Technique (SMOTE)–KNN pipelines were evaluated using group-aware cross-validation. The selected SMOTE–KNN model used distance weighting with k = 9 and achieved 92.19% accuracy, 88.59% balanced accuracy, and 89.75% macro F1-score. The classification result adjusted adaptive hints, while explicit rules controlled indicator priority, difficulty progression, mastery points, and treatment transitions. All 24 black-box scenarios met the expected requirements. The novelty and main contribution of this study lie in integrating indicator-level CT diagnosis, data-driven classification, and transparent rule-based adaptation within a single web-based workflow for vocational database learning.
Design and Development of a Transparent AI-Driven Assessment Framework Based on BERT/RoBERTa and Explainable AI for Student Resume Evaluation Yogi Prasetyo; Yuli Sopianti; Latifahny Aridia Alfitri
Justek : Jurnal Sains dan Teknologi Vol 9, No 3 (2026): September
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v9i3.41616

Abstract

The evaluation of resume-based answers demands complex semantic understanding and knowledge structuring, making manual assessment prone to inter-rater inconsistency, while deep learning models such as BERT generally operate as a black box without adequate transparency. This study aims to design a Transparent AI-Driven Assessment Framework that integrates a BERT/RoBERTa model for resume assessment, an Explainable AI module based on SHAP and LIME, and a dual-validation mechanism based on multiple-choice analytics to verify learners' conceptual understanding. The study employs the Research and Development (R&D) method, covering needs analysis, architectural design, and the development of a prototype based on a microservices architecture interoperable with the Learning Management System. A formative evaluation of the prototype conducted on 15 respondents (7 lecturers and 8 students) yielded an average System Usability Scale score of 88.17 (Excellent category) and high perceived usefulness and transparency scores (4.78 and 4.72 out of 5), which preliminarily support the feasibility of the design, although these results remain indicative given the limited number of respondents.
Co-Authors Eki Nugraha Erlangga, Erlangga Latifahny Aridia Alfitri Muhammad Izzuddin Al Fatih Sifa Fauzan yogi prasetyo