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Penyuluhan dan Pendampingan Penataan Lingkungan Pada Permukiman Kumuh Di Kelurahan Bontorannu, Kecamatan Mariso, Kota Makassar

2020· article· id· W3010581358 on OpenAlexaff
Mimi Arifin

Bibliographic record

VenueJURNAL TEPAT Applied Technology Journal for Community Engagement and Services · 2020
Typearticle
Languageid
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsHumanitiesArt

Abstract

fetched live from OpenAlex

Permukiman padat di pinggiran Kanal Jongaya Kelurahan Bontorannu adalah salah satu penyebab tercemarnya lingkungan Pantai Losari sebagai landmark Kota Makassar. Penumpukan sampah di sepanjang kanal makin menguatkan perilaku masyarakat membuang sampah di kanal. Kawasan Kelurahan Bontorannu memiliki beberapa potensi yang dapat dijadikan faktor pendukung dalam upaya revitalisasi yakni kanal yang dapat dimanfaatkan sebagai transportasi air karena kanal Jongaya terhubung ke Pantai Losari dan pulau – pulau disekitarnya. Sebagai permukiman nelayan yang berada di perkotaan serta di kawasan strategis koridor pesisir yang memiliki nilai lebih sebagai Pusat Bisnis dan Pariwisata Terpadu. (RT/RW Kota Makassar 2015-2034) tentu menjadi daya tarik. Tujuan pengabdian masyarakat untuk 1) Mengidentifikasi karakteristik prasarana permukiman masyarakat dan preferensi masyarakat terhadap kondisi permukiman tepian Kanal Jongaya di Kelurahan Bontorannu Kecamatan Mariso Kota Makassar. 2) Menyusun arahan penataan lingkungan pada permukiman kumuh di Kelurahan Bontorannu Kecamatan Mariso Kota Makassar berbasis masyarakat. Metode yang digunakan yaitu focus group discussion (FGD) untuk mendapatkan masukan akan kebutuhan dan masalah di lingkungan masyarakat serta pemberian stimulus yang tepat kepada masyarakat sehingga persepsi dan usulan penanganan masalah terhadap lingkungan dan kondisi permukiman sesuai dengan yang diharapkan. Arahan penataan yang direkomendasikan dalam mengatasi kondisi permukiman antara lain: pengadaan lampu jalan, penambahan tempat sampah di setiap lorong dan tepi kanal, pembebasan lahan di RW 1, RW 2, RW 3, dan RW 4 untuk membuka akses jalan, menyediakan lahan parkir bagi wisatawan yang datang berkunjung, serta keinginan masyarakat untuk berjualan kuliner, cendramata, dan pembuatan spot foto untuk menarik kunjungan wisatawan sehingga dapat meningkatkan perekonomian masyarakat.

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: none
Teacher disagreement score0.045
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0450.009

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 head0.036
GPT teacher head0.252
Teacher spread0.217 · 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
Published2020
Admission routes1
Has abstractyes

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