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OPTIMALISASI KETERJANGKAUAN LAYANAN BANK SAMPAH DI WILAYAH KELURAHAN KEBAGUSAN KOTA ADMINISTRASI JAKARTA SELATAN

2024· article· id· W4402023876 on OpenAlexaff
Danny Primasmada, Laili Fuji Widyawati, Mohamad Yohan, Prama Ardha Aryaguna, Ratnawati Yuni Suryandari

Bibliographic record

VenueREKSABUMI · 2024
Typearticle
Languageid
FieldEnvironmental Science
TopicWaste Management and Recycling
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Jakarta sebagai Ibukota Negara Indonesia yang tidak terlepas dari permasalahan persampahan. Persoalan persampahan di DKI Jakarta seperti tidak ada habisnya, hal ini terjadi karena adanya peningkatan jumlah penduduk dan jumlah kontribusi sampah serta jenisnya, salah satunya sampah kemasan yang sangat mendominasi dan juga sulit untuk terurai secara alami serta berujung pada semakin bertambahnya volume sampah yang dihasilkan oleh masyarakat. Berdasarkan informasi data yang telah diperoleh melalui Satuan Pelaksana Lingkungan Hidup Kecamatan Pasar Minggu Kota Administrasi Jakarta Selatan, di Kecamatan Pasar Minggu Jakarta Selatan terdapat 7 (tujuh) Kelurahan salah satunya Kelurahan Kebagusan dimana volume sampah yang terdapat di Kelurahan Kebagusan mengalami peningkatan setiap tahunnya dan mencapai kurang lebih 20 ton/hari. Tujuan penelitian ini yakni untuk menganalisis jangkauan optimal bank sampah di Kelurahan Kebagusan secara spasial, dengan metode penelitian kuantitatif deskriptif. Teknik analisis yang digunakan yaitu Buffering dan perhitungan daya jangkau pelayanan (Isoline). Hasil daya jangkauan pelayanan bank sampah terluas yaitu Bank Sampah Flamboyan yang berlokasi di RW 02, sementara daya jangkauan pelayanan terkecil yakni Bank Sampah Durian di RT 003/08. Kemudian dilakukan penentuan rata-rata jangkauan maksimum pelayanan. Hasil perhitungan yang diperoleh jumlah rata-rata jangkau pelayanan maksimum yakni sejauh 569 meter yang dijadikan standar jangkauan optimal pelayanan bank sampah di wilayah Kelurahan Kebagusan. Selain itu, hasil daya jangkau pelayanan yang diperoleh telah ditemukan 3 klasifikasi pelayanan bank sampah di wilayah kelurahan kebagusan.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.009

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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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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