Analisis Tingkat Kerusakan Perkerasan Jalan Lentur Menggunakan Metode Bina Marga dan Aplikasi Roadlab Pro
Bibliographic record
Abstract
Kerusakan jalan menjadi masalah yang sering kali terjadi, terkhusus infrastruktur jalan di Kabupaten Bengkalis dari total panjang jalan yang mencapai 1.311,961 km, lebih dari 629 km diantaranya mengalami kerusakan ringan hingga berat, terutama pada jalan lentur yang memiliki umur layanan dan daya dukung terbatas, permasalahan sering kali terjadi akibat beban lalu lintas, perubahan cuaca, serta umur layanan jalan. Oleh karena itu, penting untuk melakukan analisis yang mendalam terhadap tingkat kerusakan jalan. Metode yang digunakan adalah metode Bina Marga 1990, Surface Distress Index (SDI), Road Condition Survey (RCS), Road Condition Index (RCI), dan International Roughness Index (IRI), dan Aplikasi Roadlab Pro. Berdasarkan data hasil metode Bina Marga 1990, nilai kondisi jalan ˃7 terdapat 284 STA perlu dilakukan pemograman pemeliharaan rutin, nilai kondisi jalan 4 – 6 terdapat 16 STA perlu dilakukan pemograman pemeliharaan berkala. Hasil pengaplikasian RoadLab Pro memiliki nilai RCI rata-rata sebesar 8,49 dan survei manual di lapangan nilai RCI rata-rata sebesar 7,11. Perbandingan selisih nilai yang cukup signifikan penggunaan aplikasi RoadLab Pro dapat membantu mempercepat penilaian kekasaran jalan, akurasinya belum sepenuhnya konsisten. Selisih nilai dengan survei manual menunjukkan aplikasi ini sebaiknya digunakan sebagai alat pendukung
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".