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PENINGKATAN VOLUME USAHA ANGGOTA MELALUI OPTIMALISASI PENGGUNAAN PLATFORM DIGITAL PADA KOPERASI DISABILITAS INDONESIA

2021· article· en· W3215365964 on OpenAlexaboutno aff
Tri Haryanto, Angga Erlando, Muhammad Mubin, Zidna Fitriyana, Wahyu Setyorini, Shochrul Rohmatul Ajija

Bibliographic record

VenueJurnal Layanan Masyarakat (Journal of Public Services) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsIndonesianBusinessQuarter (Canadian coin)Coronavirus disease 2019 (COVID-19)Government (linguistics)Indonesian governmentPandemicWork (physics)Economic growthEconomicsMedicineGeography

Abstract

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AbstractIndonesia is one of the countries affected by the Covid-19 pandemic, so many victims died. Seeing an increase in Covid-19 cases, the government made a physical distancing policy to reduce the spread of Covid-19. However, this policy has an impact on hampered economic activity. Badan Pusat Statistik (BPS) in 2020, the number of poor people in March 2020 was 26.42 million people, an increase of 1.63 million people compared to September 2019. In addition, the Indonesian economy in the second quarter of 2020 compared to the second quarter of 2019 experienced a growth contraction of 5.32 percent (y-on-y). One of the groups of people who feel the impact of the Covid-19 on the family's economic condition is a group of people with disabilities. Since the Covid-19 pandemic, many people with disabilities have lost their jobs. In addition, some of them have succeeded in developing cooperatives. Currently, there are many Cooperatives for Persons with Disabilities. However, their work is lacking in demand due to declining demand in the market. Therefore, this is the background for community service activities to increase business volume through optimizing the use of digital platforms at the Indonesian Disability Cooperative. With business assistance for people with disabilities, it can help improve the skills of people with disabilities and help promote their products so that they can expand the marketing reach of products for people with disabilities. Thus, the income of persons with disabilities will increase.Keywords: Cooperatives, Disability, Digital Platform, Covid-19AbstrakIndonesia merupakan salah satu negara yang terdampak pandemi Covid-19, sehingga banyak korban meninggal. Melihat adanya peningkatan kasus Covid-19, pemerintah membuat kebijakan pysical distancing untuk mengurangi penyebaran Covid-19. Namun, kebijakan tersebut berdampak pada aktivitas perekonomian yang terhambat. Badan Pusat Statistik (BPS) pada tahun 2020, jumlah penduduk miskin pada Maret 2020 sebesar 26,42 juta orang, meningkat 1,63 juta orang terhadap September 2019. Selain itu, ekonomi Indonesia triwulan II-2020 terhadap triwulan II-2019 mengalami kontraksi pertumbuhan sebesar 5,32 persen (y-on-y). Salah satu kelompok masyarakat yang merasakan dampak adanya Covid-19 terhadap kondisi perekonomian keluarga yaitu kelompok masyarakat penyandang disabilitas. Sejak adanya pandemi Covid-19, banyak dari para penyandang disabilitas yang kehilangan pekerjaannya. Disamping itu, sebagian dari mereka telah berhasil mengembangkan koperasi. Saat ini telah banyak Koperasi Penyandang Disabilitas. Namun demikian, hasil karya mereka sepi peminat karena permintaannya yang menurun di pasar. Oleh karena itu, hal tersebut menjadi latar belakang kegiatan pengabdian masyarakat dalam rangka peningkatan volume usaha melalui optimalisasi penggunaan plaftom digital pada Koperasi Disabilitas Indonesia. Dengan adanya pendampingan usaha untuk penyandang disabilitas, maka dapat membantu meningkatkan keterampilan para penyandang disabilitas serta membantu memasarkan produknya sehingga dapat memperluas jangkauan pemasaran produk para penyandang disabilitas. Dengan demikian, maka pendapatan para penyandang disabilitas akan meningkat.Kata Kunci: Koperasi, Disabilitas, Platform Digital, Covid-19

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)0.003
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 score0.066
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0660.012

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 head0.023
GPT teacher head0.270
Teacher spread0.248 · 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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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