PROGRAMMER'S PERSPECTIVE IN YOGYAKARTA ABOUT OBJECT ORIENTED PROGRAMMING (OOP) IN SOFTWARE DEVELOPMENT USING CORRELATION ANALYSIS
Bibliographic record
Abstract
Pesatnya perkembangan teknologi menghasilkan era digitalisasi. Permintaan pengembangan perangkat lunak dan insinyur perangkat lunak di berbagai sektor industri, bisnis, dan pendidikan sangat tinggi. Yogyakarta adalah kota pendidikan, dimana banyak perguruan tinggi dan universitas berdiri. Namun, calon programmer sering memiliki pemahaman yang kurang memadai tentang paradigma OOP dari perspektif praktisi industri IT. Oleh karena itu, survei berikut melibatkan praktisi programmer profesional dilakukan untuk menganalisis bagaimana mereka melihat Object-Oriented Programming (OOP) ketika mengembangkan perangkat lunak dan bagaimana pengalaman mereka, dengan menggunakan analisis korelasi. Penelitian ini dilakukan untuk mengkaji aspek yang mempengaruhi preferensi programmer terhadap OOP. Hasil analisis korelasi menunjukkan bahwa programmer yang lebih berpengalaman akan lebih memilih paradigma OOP untuk menyelesaikan proyek meskipun mengalami beberapa hambatan dalam implementasi OOP, tetapi mereka tidak yakin bahwa OOP akan tetap digunakan sebagai paradigma yang mumpuni di masa depan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".