Analisis Faktor Yang Dipertimbangkan Oleh Konsumen Dalam Mengkonsumsi Kopi di Kedai Kota Jambi
Bibliographic record
Abstract
Coffee is one of the plantation commodities that plays a fairly high role in economic activities in Indonesia. Coffee is also a commodity Indonesia's exports are quite important as a foreign exchange earner besides oil and gas. According to BPS Indonesia, 2020, Jambi is one of the provinces in the Indonesian archipelago which is a coffee producer with an area of 30,603 hectares and a production of 18,613 tonnes. Coffee is a plantation crop consisting of 4 varieties, namely Arabica coffee, Robusta coffee, Liberica coffee and Eksela coffee (Pracaya and Kahono, 2016). However, Indonesian people are more familiar with two types of coffee, namely Arabica coffee and Robusta coffee, which are widely cultivated in Indonesia today. People's taste for coffee is driven by the high increase in population from year to year.In Jambi City there are around 22 coffee shops that sell various coffee variants, both local and imported coffee. Of the 22 coffee shops, there are 3 sub-districts that have the largest number of coffee shops, namely Jelutung, Telanai and Danau Sipin. Each sub-district has 4 to 6 coffee shops so that one of each coffee shop in the sub-district will be used as a research site. With this the researcher will take samples at the Foresthree coffee shop in Telanai Subdistrict, Duniawi in Jelutung Subdistrict, and Quarter in Jelutung Subdistrict. Lake Sipin. The increasing number of coffee shops creates increasingly fierce competition and business people must be able to read the preferences that influence consumers in choosing a coffee shop, whether it is aroma, taste, price, location, facilities, atmosphere, service, interior design and promotion of each of these things. This greatly influences consumer preferences in choosing which coffee shop to visit because each consumer has different preferences.This research was conducted because we wanted to see what the picture of coffee consumption in Jambi City coffee shops is, what factors consumers consider in choosing a coffee shop in Jambi City. Where there are 22 coffee shops/coffee shops in Jambi City spread across 7 sub-districts, 3 sub-districts with the largest number of coffee shops were taken, namely in Telanaipura, Jelutung, and Danau Sipin sub-districts, so the coffee shops chosen as research locations were foresthree, duniawi, and quarter using the accidental sampling method. From the results of research and analysis of preference factors in choosing a coffee shop in Jambi City, it can be concluded that there are 3 new factors or variables with Eigenvalues > 1, there are 3, namely type of coffee, aroma and taste. These three factors are important factors that consumers in Jambi City consider when choosing a coffee shop.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".