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Record W4392852110 · doi:10.62603/konteks.v1i4.84

EVALUASI KUALITAS CAMPURAN ASPAL PANAS PRODUKSI AMP DENGAN BAHAN BAKAR LIMBAH CANGKANG KELAPA SAWIT

2024· article· id· W4392852110 on OpenAlexaff
Ary Setyawan, Florentina Pungky Pramesti, Muhammad Ridwan Maulana

Bibliographic record

VenueKonferensi Nasional Teknik Sipil (KoNTekS) · 2024
Typearticle
Languageid
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsAnimal scienceHorticultureBiology

Abstract

fetched live from OpenAlex

Meningkatnya jumlah kebutuhan Hot Mixed Asphalt akibat pembangunan dan perawatan jalan raya mendorong Asphalt Mixing Plant (AMP) meningkatkan lagi kapasitas produksinya sehingga meningkatkan penggunaan solar sebagai bahan bakar utama di AMP. Padahal, solar merupakan bahan bakar yang bersifat tidak dapat diperbaharui atau Irreversible. Perlu adanya terobosan baru untuk mengatasi hal tersebut, salah satunya dengan mengganti bahan bakar utama di AMP, terutama saat pembakaran agregat di dryer, yakni dengan menggunakan limbah cangkang kelapa sawit..Berdasarkan kondisi itulah, maka perlu adanya pemeriksaan terkait kualitas campuran aspal Panas yang dicampur di AMP menggunakan bahan bakar limbah cangkang kelapa sawit. Data yang dibutuhkan berupa data pemeriksaan bitumen cair ( Penetrasi, Daktilitas, Titik Lembek, Titik Nyala dan Bakar dan berat jenis bitumen), data pemeriksaan agregat (berat jenis total agregat, baik berat jenis kering total, semu total, dan efektif total agregat), data pemeriksaan campuran beraspal yang dalam penelitian ini menggunakan jenis perkerasan HRS-Base (variasi temperatur pencampuran, temperatur pemadatan, densitas campuran, volumetrik dan marshall) yang kemudian di bandingkan dengan Pedoman / Spesifikasi Teknis 2018 Bina Marga. Analisis yang akan dilakukan pada penelitian ini yakni analisis pengaruh variasi temperatur pemadatan terhadap densitas campuran, analisis pengaruh variasi temperatur pencampuran terhadap hasil uji parameter marshall dan analisis pengaruh variasi sampel campuran terhadap hasil uji parameter marshall dan densitas campuran. Hasil Penelitian ini menyimpulkan bahwa campuran aspal yang dicampur di AMP menggunakan bahan bakar limbah cangkang kelapa sawit memenuhi standar Spesifikasi Teknis 2018 Bina Marga, dengan Rekomendasi- rekomendasi yang diberikan antara lain masih perlu adanya penelitian lebih lanjut, terutama terkait dengan variasi campuran aspal, yakni lebih variatif atau lebih dari satu jenis perkerasan.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.043
GPT teacher head0.297
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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