EKOIST Journal of Econometrics and Statistics 2651-396X İstanbul Üniversitesi 10.26650/ekoist.2024.40.1344234 Econometrics (Other) Ekonometri (Diğer) TOPSIS Method Approach in Ranking Coffee Bean Quality Kahve Çekirdeği Kalitesi Sıralamasında TOPSIS Yöntemi Yaklaşımı https://orcid.org/0000-0001-8631-4585 Boyar Fatih ALTINBAŞ ÜNİVERSİTESİ, İŞLETME FAKÜLTESİ 06 26 2024 40 46 62 08 16 2023 12 05 2023 Copyright © 2005, EKOIST Journal of Econometrics and Statistics 2005 EKOIST Journal of Econometrics and Statistics

Coffee is the second-most traded commodity after oil. Quantifying the quality of coffee is a critical topic of research. Many aspects such as the taste of coffee, bean defects, production conditions, and sustainability conditions are considered for rating the quality. In this study, the best alternative method for quality standards accepted by Coffee Quality Institute (CQI) was discussed. The web scraping method and the Python 3.10.5 program was used to obtain the data in the CQI database. Aroma, taste profile, aftertaste, acidity, body, taste balance, stability, cleanliness, sweetness qualities were believed to positively affect quality. First-category defect, second-category defect, and its light color after roasting were considered to negatively affect coffee quality. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to select the best among the alternatives. In the weighting step, both the entropy method and equal weighting were used, and their positive and negative aspects were explained and discussed.

Kahve, petrolden sonra ticareti en çok yapılan ikinci emtiadır. Son yirmi yılda nitelikli kahve alanında önemli gelişmeler olmuştur. Bu gelişmelerle birlikte kahvenin tadı, çekirdeğin kusurları, üretim koşulları, sürdürülebilirlik koşulları gibi birçok konu tartışılagelmektedir. Bu çalışmada Kahve Kalitesi Enstitüsü (KKE) (CQI - Coffee Quality Institute) tarafından kabul edilen kahve kalitesi standartları ile kahveler arasında en iyi alternatifin bulunması hedeflenmiştir. KKE veri tabanında bulunan veri, ağ kazıma yöntemi ile Python 3.10.5 programı kullanılarak elde edilmiştir. Elde edilen veride aroma, tat profili, damakta kalıcılık, asitlik, gövde, tat dengesi, tutarlılık, temizlik, tatlılık nitelikleri olumlu yönde etkileyen ölçütler; 1. kategori kusur, 2. kategori kusur ve kavurma sonrası açık renk kalmış nitelikleri kahvenin kalitesini olumsuz yönde etkileyen ölçütlerdir. Alternatifler arasında en iyisinin seçimi için Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) kullanılmıştır. Ağırlıklandırma adımında hem entropi yöntemi hem de eşit ağırlıklandırma ele alınmış, olumlu ve olumsuz yönleri açıklanarak tartışılmıştır.

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