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Identifikasi Faktor Internal dan Faktor Eksternal dalam Pengembangan Agrowisata Kopi di Kecamatan Sumber Jaya Kabupaten Lampung Barat

2024· article· id· W4405858681 on OpenAlexaff
Munafatin Afifah, Sumaryo Gitosaputro, Kordiyana K Rangga, Muhammad Irfan Affandi

Bibliographic record

VenueSuluh Pembangunan Journal of Extension and Development · 2024
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsHumanities

Abstract

fetched live from OpenAlex

Penelitian dilaksanakan dengan tujuan untuk mengidentifikasi faktor internal dan faktor eksternal dalam bentuk kekuatan, kelemahan, peluang dan ancaman yang dapat dijadikan dasar untuk merumuskan strategi pengembangan Agrowisata Kopi di Sekolah Kopi Lampung Barat. Penelitian ini dilaksanakan di Kecamatan Sumber Jaya Kabupaten Lampung Barat. Metode analisis data pada penelitian ini menggunakan analisis deskriptif kuantitatif. Pengambilan data di lapangan dilaksanakan pada bulan November hingga Desember 2022. Pengumpulan data penelitian dilaksanakan melalui FGD pada 3 desa sekitar lokasi agrowisata, responden penelitian berjumlah 36 orang pemangku kepentingan. Berdasarkan hasil penelitian, diketahui jumlah total skor faktor kekuatan 2,20 dan kelemahan 0,65, sehingga diketahui jumlah total skor IFE adalah 2,85, hal ini menunjukkan posisi faktor internal berada pada skala penilaian sedang. Jumlah total skor EFE adalah 3,17, dari hasil ini menunjukkan faktor eksternal berada pada posisi penilaian kuat dengan total skor faktor peluang 2,90 dan ancaman 0,27, sehingga dari hasil tersebut dapat diketahui bahwa total matrik EFE lebih besar dibandingkan matrik IFE, hal tersebut menggambarkan bahwa keadaan internal Agrowisata berada pada kondisi tumbuh dan kembangkan dengan memanfaatkan peluang yang ada. Kata kunci: agrowisata kopi, faktor internal, dan faktor eksternal

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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