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Record W4411300681 · doi:10.59934/jaiea.v4i3.1007

Development of a Web-Based Entrance Examination System to Increase the Efficiency and Accuracy of New Student Selection at the STMIK Kaputama Campus

2025· article· en· W4411300681 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsSelection (genetic algorithm)Computer scienceMathematics educationPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

This research aims to develop a web-based entrance examination system so that the new student selection process at STMIK Kaputama becomes more efficient and accurate. This system is designed to replace traditional methods that often require a lot of time, paper and are prone to human error. System development involves literature analysis, creating interface mockups, drawing business process userflow, and creating entity-relationship diagrams (ERD) for optimal database management. This system allows prospective students to take exams online with results that can be processed and displayed in real-time. The results of this research include design documents, user guides, comprehensive final research reports, as well as the publication of scientific articles in journals discussing the development of information systems and educational technology. With this system, it is hoped that the campus can manage the new student selection process more efficiently, accurately and transparently.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.354
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it