PROGRAMMER'S PERSPECTIVE IN YOGYAKARTA ABOUT OBJECT ORIENTED PROGRAMMING (OOP) IN SOFTWARE DEVELOPMENT USING CORRELATION ANALYSIS
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it