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Record W4232717532 · doi:10.23960/jiia.v8i1.4346

ANALISIS KEPUASAN KONSUMEN DAN BAURAN PEMASARAN PADA AGROINDUSTRI KOPI BUBUK CAP JEMPOL DI KOTA BANDAR LAMPUNG

2020· article· en· W4232717532 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Ilmu-Ilmu Agribisnis · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsAgribrands Purina (Canada)
Fundersnot available
KeywordsSnowball samplingBusinessCoffee shopAgricultural scienceMarketingMarketing mixCustomer satisfactionAdvertisingBusiness administrationMathematicsStatistics

Abstract

fetched live from OpenAlex

This study aims to analyze the level of consumer satisfaction of coffee powder and the marketing mix of coffee powder agroindustry in Bandar Lampung City. The research method used was a case study. Research location was determined purposively. The number of interviewed samples was 60 respondents chosen using snowball sampling method. Respondents of this study consisted of consumers of coffee powder, owners, industry and trade in Bandar Lampung City, and experts of University Lampung chosen using snowball sampling. The study was conducted in February-April 2018. The data analysis methods used are the analysis of the Customer Satisfaction Index (CSI) and descriptive analysis. The results showed that: consumers of Jempol brand coffee powder in Bandar Lampung City were in satisfied criteria. The marketing mix for agroindustry has implemented marketing strategy (marketing mix).Key words: agroindustry, consumer satisfaction, coffee powder, marketing mix

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.276
Teacher spread0.235 · 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