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All Journal Jurnal Keteknikan Pertanian Tropis dan Biosistem Machine : Jurnal Teknik Mesin
Rukmana, Fitri Hardiyanti
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Agriculture, Biological Sciences & Forestry Automotive Engineering Control & Systems Engineering Electrical & Electronics Engineering Energy Mechanical Engineering

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SENSORY PREFERENCE DETERMINATION OF GREEN TEA EXTRACTS FROM UNCONTROLLED AND RELAY-CONTROLLED BREWING PROCESSES USING RANK ORDER CENTROID AND SIMPLE ADDITIVE WEIGHTING Pratama, Borneo Satria; Alfiqriyansyah, Alfiqriyansyah; S, Arya Dwicahya; Rukmana, Fitri Hardiyanti; Hartati, Intan Dwi; Cahya Brutu, Henrike Lehla; Winda, Winda; Sariati, Sariati
Machine : Jurnal Teknik Mesin Vol 10 No 2 (2024): Machine : Jurnal Teknik Mesin
Publisher : Jurusan Teknik Mesin Fakultas Sains dan Teknik Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jm.v10i2.5727

Abstract

Green tea extract is a popular beverage known for its health benefits. To increase its market appeal, understanding the sensory profiles—taste, aroma, and color—of green tea extract is essential to ensure customer satisfaction. In this study, experiments were conducted to determine the optimal extraction conditions by varying extraction times (3 or 6 minutes) and temperatures (60°C, 80°C, or 100°C) in two brewing methods: (1) a relay-controlled brewing system utilizing Arduino, and (2) a standard brewing process without automated control. Sensory evaluations were conducted by 30 untrained panelists using a 9-point hedonic scale.. Decision-making methods, Rank Order Centroid (ROC) and Simple Additive Weighting (SAW), were then applied to identify the most preferred product. Results indicated that the highest taste score was achieved by the 80°C-6 min sample, while the best aroma and color scores were observed in the 100°C-3 min sample. Overall, the decision-making process using the combination of ROC and SAW methods concluded that the green tea extract from the 100°C-3 min sample was the most preferred by the panelists.
Development and Characterization of a Sustainable and Vegan Lip Balm Utilizing Soybean Oil and Carnauba Wax: Pengembangan dan Karakterisasi Lip Balm Berkelanjutan dan Vegan yang Menggunakan Minyak Kedelai dan Lilin Carnauba Subara, Deni; Rukmana, Fitri Hardiyanti; Afifah, Amalia
Journal of Tropical Agricultural Engineering and Biosystems - Jurnal Keteknikan Pertanian Tropis dan Biosistem Vol. 13 No. 2 (2025): August 2025
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jkptb.2025.013.02.05

Abstract

The demand for organic cosmetics, including lip balms, is increasing. While soybean oil and carnauba wax are known for their beneficial properties in cosmetics, their specific combination in a lip balm formulation has not been previously explored. This study aimed to determine the optimal lip balm formulation from a combination of soybean oil and carnauba wax and to analyze the effect of varying formulations on the product's physicochemical characteristics. The lip balms were prepared using a melt-and-mix method. Physicochemical properties (pH, melting point, adhesion time, loss on drying and greasiness) were evaluated according to the Indonesian National Standard (SNI) 16-4399-1996. A Non-Factorial Completely Randomized Design (CRD) was employed, and the data were analyzed using ANOVA followed by Duncan’s Multiple Range Test (DMRT). Results indicated that the formulation significantly affected (p<0.05) the melting point, adhesion time, greasiness and loss on drying but did not significantly affect the pH. Formulation 5 (F5) was selected as the optimal formulation, exhibiting the following characteristics: a pH of 6.206, a melting point of 66.00 °C, an adhesion time of 9.00 seconds, loss on drying (LoD) 0.043% and greasiness 8.20 cm. Furthermore, hedonic testing revealed that F5 was the formulation most preferred by panelists. In conclusion, the combination of soybean oil and carnauba wax can yield a natural lip balm with desirable properties and high consumer acceptability, with F5 identified as the optimal formulation in this research.
SENSORY PREFERENCE DETERMINATION OF GREEN TEA EXTRACTS FROM UNCONTROLLED AND RELAY-CONTROLLED BREWING PROCESSES USING RANK ORDER CENTROID AND SIMPLE ADDITIVE WEIGHTING Pratama, Borneo Satria; Alfiqriyansyah, Alfiqriyansyah; S, Arya Dwicahya; Rukmana, Fitri Hardiyanti; Hartati, Intan Dwi; Cahya Brutu, Henrike Lehla; Winda, Winda; Sariati, Sariati
Machine : Jurnal Teknik Mesin Vol 10 No 2 (2024): Machine : Jurnal Teknik Mesin
Publisher : Jurusan Teknik Mesin Fakultas Sains dan Teknik Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jm.v10i2.5727

Abstract

Green tea extract is a popular beverage known for its health benefits. To increase its market appeal, understanding the sensory profiles—taste, aroma, and color—of green tea extract is essential to ensure customer satisfaction. In this study, experiments were conducted to determine the optimal extraction conditions by varying extraction times (3 or 6 minutes) and temperatures (60°C, 80°C, or 100°C) in two brewing methods: (1) a relay-controlled brewing system utilizing Arduino, and (2) a standard brewing process without automated control. Sensory evaluations were conducted by 30 untrained panelists using a 9-point hedonic scale.. Decision-making methods, Rank Order Centroid (ROC) and Simple Additive Weighting (SAW), were then applied to identify the most preferred product. Results indicated that the highest taste score was achieved by the 80°C-6 min sample, while the best aroma and color scores were observed in the 100°C-3 min sample. Overall, the decision-making process using the combination of ROC and SAW methods concluded that the green tea extract from the 100°C-3 min sample was the most preferred by the panelists.
Co-Authors Afifah, Amalia Alfiqriyansyah, Alfiqriyansyah Cahya Brutu, Henrike Lehla Hartati, Intan Dwi Pratama, Borneo Satria S, Arya Dwicahya Sariati Sariati, Sariati Subara, Deni Winda Winda