Pengaruh Penambahan Asbuton LGA B50/30 terhadap Kinerja Campuran Aspal Beton AC-WC Berdasarkan Parameter Marshall dan Indeks Kekuatan Sisa
Bibliographic record
Abstract
Indonesia memiliki luas wilayah 1.904.569 km², sehingga pembangunan infrastruktur jalan menjadi tantangan besar, terutama karena ketergantungan terhadap impor aspal. Padahal, Indonesia memiliki cadangan aspal alam yang signifikan, salah satunya aspal Buton. Namun, pemanfaatannya masih terbatas karena kurangnya kesadaran industri akan potensinya.Penelitian ini bertujuan untuk menganalisis dampak penambahan Asbuton LGA B50/30 terhadap performa campuran Asphalt Concrete-Wearing Course (AC-WC) berdasarkan pengujian Marshall dan Indeks Kekuatan Sisa (IKS). Metode yang digunakan meliputi pembuatan campuran AC-WC dengan gradasi rapat dan variasi kadar aspal penetrasi 60/70 sebesar 5%; 5,5%; 6%; 6,5%; dan 7%. Campuran ini kemudian dimodifikasi dengan penambahan Asbuton LGA B50/30 sebesar 0%; 5%; 7,5%; 10%; 12,5%; dan 15%, yang selanjutnya diuji dengan perendaman pada suhu 60°C selama 24 jam. Hasil penelitian menunjukkan bahwa kadar aspal optimum (KAO) adalah 6,925%, sementara kadar Asbuton optimum ditemukan sebesar 2,85%. Penambahan Asbuton LGA B50/30 dalam jumlah optimal meningkatkan stabilitas dan durabilitas campuran AC-WC, dengan nilai IKS mencapai 90,31%, yang telah memenuhi standar Spesifikasi Umum Bina Marga 2018 Revisi 2. Dengan demikian, pemanfaatan Asbuton berpotensi mengurangi ketergantungan pada aspal impor serta meningkatkan ketahanan perkerasan jalan di Indonesia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".