Analisis Faktor Untuk Pemetaan Karakteristik pada Percobaan Dekafeinasi Kopi Robusta

Zulhan Widya Baskara; Lisa Harsyiah; Dewa Nyoman Adi Paramartha; Qabul Dinanta Utama

doi:10.29303/emj.v5i1.139

Authors

Zulhan Widya Baskara Program Studi Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Mataram
Lisa Harsyiah Program Studi Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Mataram
Dewa Nyoman Adi Paramartha Program Studi Ilmu dan Teknologi Pangan, Fakultas Teknologi Pangan dan Agroindustri, Universitas Mataram
Qabul Dinanta Utama Program Studi Ilmu dan Teknologi Pangan, Fakultas Teknologi Pangan dan Agroindustri, Universitas Mataram

DOI:

https://doi.org/10.29303/emj.v5i1.139

Abstract

In recent years, there has been a positive trend in coffee consumption in Indonesia. Coffee that was initially identical to older man's drinks is starting to be liked by teenagers and children because coffee contains caffeine which can have an addictive effect. Coffee has various benefits, such as preventing drowsiness, antioxidants, improving brain performance, and reducing fatigue. However, drinking a lot of coffee than your body can tolerate will cause symptoms of insomnia, excessive anxiety, and increased blood pressure. Various experiments have been made to reduce the caffeine content in coffee (decaffeination), one of which is mixing coffee with chayote juice (Sechium edule). Furthermore, this article classified the characteristics of decaffeinated products, caffeine content, moisture content, total acid titration, ash content, hue color, and L value. Using factor analysis, it is known that the characteristics can be mapped into three principal components. The first principal component consists of variables of caffeine content, water content, and hue color value. The second principal component consists of ash content and total acid content titration variables, and the third principal component, this factor, consists only of the characteristic L. It is also known that these three main components can explain 74.2% of the diversity of origin.

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