Analisis Faktor Yang Dipertimbangkan Oleh Konsumen Dalam Mengkonsumsi Kopi di Kedai Kota Jambi
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».