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Record W4386541816 · doi:10.29313/bcsurp.v3i2.8145

Strategi Pengembangan Agribisnis Manggis di Kecamatan Kiarapedes Kabupaten Purwakarta

2023· article· en· W4386541816 on OpenAlexaff
Ardelia Salwa Pratiwi, Ivan Chofyan

Bibliographic record

VenueBandung Conference Series Urban & Regional Planning · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Agroindustry Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsSWOT analysisBusinessGarcinia mangostanaAgribusinessAgricultural scienceAgricultureDescriptive statisticsService (business)Agricultural economicsMarketingGeographyMathematicsEconomicsStatistics

Abstract

fetched live from OpenAlex

Abstract.Mangosteen (Garcinia Mangostana L) is one of the leading commodities in Purwakarta District and has been recognized by the Decree of the Minister of Agriculture Number 571/KPTS/SR.120/9/2006 which states that the mangosteen fruit of Purwakarta Regency is recognized as a superior variety called Manggis Wanayasa. Kiarapedes sub-district is one of the mangosteen-producing sub-districts in Purwakarta District, which has a large potential area for developing mangosteen. In Kiarapedes sub-district, there is also mangosteen agricultural land belonging to the Food and Agriculture Service of Purwakarta District as an integrated demonstration garden. The potential possessed by Kiarapedes Sub-district has not been utilized optimally because there are still farmers who do not understand the importance of regulations and there is no advanced processing industry for mangosteen. Therefore, the purpose of this research is to determine the condition of the mangosteen commodity agribusiness in Kiarapedes Sub-district and to formulate strategies that can be implemented to support the development of mangosteen commodities in Kiarapedes Sub-district. The approach method of this research is descriptive quantitative, and the analytical method used is descriptive statistical analysis and SWOT analysis. From the results of the analysis of the condition of mangosteen agribusiness in Kiarapedes District, there are 8 internal factors, namely 4 strengths and 4 weaknesses, and 5 external factors, namely 3 opportunities and 2 threats. The mangosteen agribusiness development strategy in Kiarapedes Sub-district, especially in on farm agribusiness are in quadrant I, which is in a favorable situation, so the strategy that needs to be implemented is aggressive growth.
 Abstrak. Manggis (Garcinia Mangostana L) merupakan salah satu komoditas unggulan di Kabupaten Purwakarta dan telah diakui dengan adanya Keputusan Menteri Pertanian Nomor 571/KPTS/SR.120/9/2006 yang menyatakan bahwa buah manggis Kabupaten Purwakarta diakui sebagai varietas unggul yang disebut dengan Manggis Wanayasa. Kecamatan Kiarapedes merupakan salah satu kecamatan penghasil manggis di Kabupaten Purwakarta yang memiliki luas areal potensi pengembangan manggis yang masih luas, di kecamatan Kiarapedes juga terdapat lahan pertanian manggis milik Dinas Pangan dan Pertanian Kabupaten Purwakarta sebagai kebun percontohan terpadu. Potensi yang dimiliki oleh Kecamatan Kiarapedes tersebut belum dimanfaatkan secara optimal karena masih adanya perilaku petani yang belum mengerti pentingnya regulasi dan belum adanya industri pengolahan lanjutan manggis. Maka dari itu tujuan dilakukannya penelitian ini adalah untuk mengetahui kondisi agribisnis komoditas manggis di Kecamatan Kiarapedes dan merumuskan strategi yang dapat dilakukan untuk mendukung pengembangan agrbisnis manggis di Kecamatan Kiarapedes. Metode pendekatan dari penelitian ini yaitu deskriptif kuantitatif dan metode analisis yang digunakan yaitu analisis statistika deskriptif dan analisis SWOT. Dari hasil analisis kondisi agribisnis manggis di Kecamatan Kiarapedes memiliki 8 faktor internal yaitu 4 kekuatan dan 4 kelemahan, serta 5 faktor eksternal yaitu 3 peluang dan 2 ancaman. Strategi pengembangan agribisnis manggis di Kecamatan Kiarapedes khususnya pada subsistem usahatani berada pada kuadran I, yaitu berada pada situasi yang menguntungkan, sehingga strategi yang perlu diterapkan yaitu pertumbuhan agresif.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.077
GPT teacher head0.247
Teacher spread0.170 · 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 teacher head, 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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Citations0
Published2023
Admission routes1
Has abstractyes

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