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Record W2911089415 · doi:10.33506/rb.v4i2.172

Studi Perencanaan Prasarana Jaringan Transportasi Moda Becak-Motor Bemor Di Kota Makassar

2018· article· id· W2911089415 on OpenAlexaff
Muhammad Nurwahyudi

Bibliographic record

VenueJurnal Teknik Sipil Rancang Bangun · 2018
Typearticle
Languageid
FieldHealth Professions
TopicOccupational Health and Safety Management
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk (1) menganalisa faktor-faktor yang mempengaruhi pemilihan moda transportasi bemor di Kota Makassar; (2) menganalisa karakteristik operasional bemor sebagai moda transportasi alternatif di Kota Makassar; (3) merumuskan konsep penetapan NSPK pada perencanaan prasarana bemor di Kota Makassar. Pada studi ini dilakukan penelitian survey yang dalam pelaksanaannya dilakukan pengambilan sampel dari populasi menggunakan kuisioner sebagai alat pengumpul data. Analisis dilakukan dengan analisis deskriptif untuk memberikan gambaran secara sistematis dan akurat mengenai fenomena sosial berupa fakta, keadaan, sifat, individu atau kelompok serta hubungan diantaranya. Hasil penelitian menunjukkan bahwa (1) Persepsi masyarakat pengguna terhadap keberadaan bentor di Kota Makassar, cukup positif karena waktu tempuh yang singkat, aman, mudah dan tarif yang terjangkau sertamempunyai aksesibilitas yang cukup tinggi, tetapi persepsi pemerintah cenderung khawatir terhadap pemakai bemor dari faktor keselamatan kendaraan bemor tidak memenuhi persyaratan teknis. Pengelola/pengendara bemor lebih memperhatikan aspek keterbukaan lapangan kerja dan memperoleh pendapatan dengan keberadaan bemor. (2) Banyaknya pangkalan bemor yang tersebar di Kota Makassar baik didaerah permukiman, perdagangan, dan sekolah menunjukkan besarnya permintaan/kebutuhan akan sarana transportasi ini. (3) Kontribusi tidak langsung keberadaan bemor ditinjau dari segi sektor migas yaitu dengan banyaknya jumlah bemor yang beroperasi maka akan terjadi peningkatan penjualan BBM hal ini dapat memberikan kontribusi secara tidak langsung bagi pendapatan negara.

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)0.002
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.056
GPT teacher head0.383
Teacher spread0.327 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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