PENERAPAN SISTEM PAKAR BIMBINGAN KONSELING PADA SMK MUHAMMADIYAH 3 METRO
Bibliographic record
Abstract
Bimbingan konseling sangat diperlukan terutama dalam membantu siswa untuk menghadapi permasalahan yang dialami. Jumlah siswa yang banyak berbanding dengan sedikitnya guru BK sehingga tidak semua siswa bisa mendapatkan bimbingan konseling. Dengan penerapan sistem pakar bimbingan konseling membantu guru BK mengetahui bidang masalah yang dialami oleh siswa. Tujuan penelitian untuk menganalisa kebutuhan sistem diagnosa bimbingan konseling yang akan di bangun menggunakan metode certainty factor, menerapkan hasil analisa kedalam sebuah rancangan sistem diagnosa permasalahan pada siswa SMK Muhammadiyah 3 Metro dan menerapkan hasil rancangan kedalam sistem berbasis web. Metode yang digunakan adalah metode Certainty Factor (CF) untuk mengukur nilai kepastian/ketidakpastian. Metode teknik desain perangkat lunak yang digunakan dalam perancangan sistem ini menggunakan metode OOSE yang tahapannya terdiri dari requirement, analisis, desain, implementasi dan testing. Aplikasi ini dibuat menggunakan bahasa pemrograman PHP, HTML, dan database MySQL. Aplikasi ini mampu mengenali 3 bentuk masalah diantaranya, masalah pribadi, masalah penyesuaian sosial dan masalah akademik.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".