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Record W4376607018 · doi:10.47600/jtst.v4i3.446

Pemetaan Kondisi Sarana dan Prasarana Infrastruktur Berkelanjutan Berbasis Foto Udara pada Kelurahan Kairagi Dua Kecamatan Mapanget Kota Manado

2023· article· id· W4376607018 on OpenAlexaff
Estrellita V. Y. Waney, Sherley Runtunuwu, Deyke Mandang, Donny Taju, Pendekar Lonan

Bibliographic record

VenueJurnal Teknik Sipil Terapan · 2023
Typearticle
Languageid
FieldEngineering
TopicWetland Management and Conservation
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsHumanitiesArt

Abstract

fetched live from OpenAlex

Abstrak Sarana prasarana infrastruktur diantaranya: jalan dan sistem drainase, merupakan suatu kerangka dasar pada suatu permukiman yang bermanfaat sebagai komponen pelayan masyarakat yang berfungsi mendukung segala aktifitas yang ada di permukiman tersebut melalui fasilitas-fasilitas yang disiapkan. Penelitian dengan skema Penelitian Dasar Produk Vokasi ini bertujuan untuk melakukan pemetaan berbasis foto udara dengan memanfaatkan citra satelit maupun wahana unmanned aerial vehicle (UAV) terhadap kondisi eksisting prasarana transportasi jalan dan jaringan drainase yang terdapat pada Kelurahan Kairagi Dua, Kecamatan Mapanget, Kota Manado. Lokasi penelitian dilakukan di wilayah Kelurahan Kairagi Dua, Kecamatan Mapanget, Kota Manado. Metodologi yang digunakan dalam penelitian ini diawali dengan pengambilan data sekunder di kantor Kelurahan dan dilanjutkan dengan survey lokasi untuk mendapatkan sebagian data primer. Data primer dan sekunder yang didapatkan kemudian diolah dan digunakan pada kegiatan pengambilan foto udara dengan Drone yang sudah dipasang kamera khusus untuk foto udara, yang dioperasikan dengan software-nya. Hasil foto udara diolah dengan aplikasi untuk kemudian dibuat pemetaan. Dari hasil penelitian didapat bahwa daerah Kelurahan Kairagi Dua menempati area seluas 382,63 Ha. Panjang jalan yang ada sejauh 62.164m, termasuk jalan kota dan lingkungan dengan material aspal sepanjang 45.707m dan material paving sepanjang 5.046m. Panjang jaringan drainase sejauh 47,499m, termasuk saluran drainase terbuka dan tertutup. Kata kunci: infrastruktur jalan, drainase, drone. Abstract Infrastructure facilities including: roads and drainage systems, are a basic framework in a residence that is useful as a component of community service that functions to support all activities in the residence through the facilities provided. This research with the scheme of Penelitian Dasar Produk Vokasi aims to carry out aerial photography-based mapping by utilizing satellite images and unmanned aerial vehicle (UAV) vehicles on the existing conditions of road transportation infrastructure and drainage networks in Kairagi Dua Village, Mapanget District, Manado City. The research location was conducted in the Kairagi Dua Village, Mapanget District, Manado City. The methodology used in this study begins with collecting secondary data at the Kelurahan office and continues with a site survey to obtain some primary data. The primary and secondary data obtained are then processed and used in aerial photography activities with drones that have been installed with special cameras for aerial photography, which are operated with the software. The results of aerial photos are processed with applications for later mapping. From the results of the study, it was found that the Kairagi Dua Village area occupies an area of ​​382.63 Ha. The length of the existing road is 62,164m, including city and environmental roads with 45,707m of asphalt material and 5,046m of paving material. The length of the drainage network is 47,499m, including open and closed drainage channels. Keyword: road infrastructure, drainage, drones

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)0.000
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: none
Teacher disagreement score0.028
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0280.010

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.

Opus teacher head0.016
GPT teacher head0.224
Teacher spread0.209 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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