Rahmat Fadhil* | Muhammad Ikhsan Sulaiman | Muhammad Rizki Farhan
© 2022 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
Currently, processed coffee products with the addition of nutritious ingredients such as herbs are experiencing rapid development. Various herbal mixtures can produce delicious coffee, but consumer acceptance varies due to the sometimes-inconsistent brewing composition between coffee and the blending ingredients. Therefore, this research aims to determine the best decision-making system for accepting the Gayo Arabica coffee brewed product with a mixture of herbs using the MOORA method. There were 5 products analyzed, namely cuminpresso, olivepresso, kurmapresso, karipresso, and honeypresso. Furthermore, 30 ml espresso was used as the mixture base with the addition of 5 ml, 7 ml, and 10 ml of herbs for each product. The results showed that the panelists had different acceptance levels for each type of brewing. Therefore, the alternatives were obtained such that cuminpresso attained an acceptable level with the addition of 5 ml of black cumin oil, olivepresso with the addition of 5 ml of olive oil, kurmapresso with the addition of 10 ml of dates, karipresso with the addition of 5 ml of boiled water from curry leaves and honeypresso with the addition of 7 ml of honey.
Gayo Arabica coffee, espresso brew, acceptance level, MOORA method, herbal mixture
As a refreshing ingredient, coffee is much liked by the community due to its taste [1]. Along with the times, the beverages known today are not just black coffee. There are many popular variations, such as latte, flat white, mochaccino, and cappuccino [2]. According to Kurniawan and Ridlo [3], the consumption level of beverages processed from coffee has experienced rapid development due to a shift in lifestyle. Currently, the development of increasingly diverse coffee beverages makes the connoisseurs have diverse tastes [4].
Espresso, one of the most popular types of coffee, is defined as a beverage brewed from compressed coffee grounds and then sprayed with hot water at high pressure, where the energy from the water pressure passes through the grounds, as stated by Illy and Viani [5]. Also, it is the basis for a variety of popular coffee dishes circulating in the market [6]. Furthermore, Gayo Arabica coffee, as a popular raw ingredient of espresso, is used to produce beverages of higher quality and variety [7].
The different formulations in the development of espresso-based processed beverages have become an exciting challenge in determining the best-preferred taste. Research by Fisdiana et al. [8], which assesses the preference level for coffee syrup with full cream milk, shows that the panelists generally assess the different preference levels and formation for each type of brewing. This was conducted based on aroma, taste, color, after taste, and viscosity criteria. Hence, the acceptance level of the different formulations of espresso-based beverages was based on the taste factors. Research by Angeloni et al. [9] stated that the brewing method is one of the factors used in determining the consumer acceptance level when consuming coffee.
The acceptance of the preferred product is performed through a decision-making system (DSS) such as the Multi-Objective Optimization based on Ratio Analysis (MOORA), which was first introduced by Brauers and Zavadskas [10]. It has simple mathematical calculations and is easy to implement. Furthermore, the results obtained are also more accurate and targeted in helping decision-making [11]. In general, there are four principles in solving problems using the MOORA method, including making a decision matrix, decision matrix normalization, attribute optimization, and alternative ranking [12]. Making a decision matrix is done by determining the number of alternatives to be considered based on certain criteria. Decision matrix normalization is to get uniform matrix element values by uniting each matrix element. The attribute optimization is to optimize multi objectives where the normalization process with the maximum value minus the minimum value, and the final stage is the ranking of several alternatives to be decided.
This method has been widely applied in various fields of research, such as regional development [13], selection of manufacturing materials [14-18], employee performance appraisal [19, 20], selection of consulting firm [21], supply chain management [22-24], banking [25, 26], industrial management [27-29], and agriculture [11, 30, 31].
This research further aims to determine the best decision-making system for accepting the Gayo Arabica coffee brewed product with the addition of herbal variants using the MOORA method. This research is essential to get the optimal steeping composition with the best (highest) product acceptance. Considering that so far, the mixing of coffee with herbs is often inconsistent, resulting in an unbalanced coffee taste and even being rejected by consumers. The MOORA method is multi-criteria technique processes simultaneously to optimize two or more conflicting attributes of value (benefit) or detrimental (cost) [32], it means that through the decision-making system of the MOORA method, product acceptance preferences consider both.
Gayo Arabica coffee was obtained from the Redinesh Coffee Roastery coffee shop with direct supplies from farmers in Bergendal Village, Wih Pesam Sub-District, Bener Meriah Regency, Aceh Province, Indonesia, with a planting height of 1200-1400 masl. The coffee used was processed with complete wash and roasting at a medium level. Meanwhile, the herbs used as ingredients for coffee are black cumin (Nigella sativa), olives (Olea europaea), dates (Phoenix dactylifera), curry leaves (Murraya koenigii (L.) Spreng), and honey obtained from supermarkets and traditional markets.
The stages are divided into three parts, namely product making, sensory testing, and decision making using the MOORA method, as follows:
2.1 Product making
2.1.1 Espresso making
Gayo Arabica coffee beans were roasted and grounded using Macap M5 Grinder Made in Italy with a satisfactory level of fineness. Afterward, the grounds were weighed as much as 18-20 g and brewed using the La Pavoni Cellini Classic Espresso Machine E61 Group - Made in Italy for 20-25 seconds as much as 30 ml.
2.1.2 Herbs preparation
In this process, the herbs used as ingredients were black cumin oil from the 'Hemani' brand, packaged olive extract from the 'Rafael Salgado' brand, and honey from the 'Ad-Dawa Multiflora' brand obtained from the nearest supermarket.
Subsequently, the dates were prepared by peeling to separate the flesh and seeds. The flesh was then washed, weighed to 50 g, and crushed using a blender until smooth, then 100 ml of water was added [33].
The curry leaves were first dried at room temperature for 48 hours, then 50 g of them were weighed and crushed into small pieces. Furthermore, it was boiled for 30 minutes with the addition of 250 ml of water and then filtered to obtain the boiled water [34].
2.1.3 Mixing espresso with herbs
The composition was 30 ml of espresso as the basis for each mixing. The amount of herb added as a mixture using 3 variations, namely 5 ml, 7 ml, and 10 ml, resulted from direct observation during the trial process of making the product and references from several local coffee shops. This amount is a composition that is commonly used in serving coffee-based beverages, especially those using the espresso brewing method. The mixing of espresso with herbs, namely black cumin, olives, dates, curry leaves, and honey, are called cuminpresso, olivepresso, kurmapresso, karipresso, and honeypresso, respectively.
2.2 Sensory test
The sensory method used was the levelization test providing information on how much a product can be accepted based on the perception of each panelist. Furthermore, this was conducted to identify the flavors of various Gayo Arabica coffee brews with herbal mixtures based on predetermined criteria to obtain alternative product acceptance.
The selected panelists were asked to fill in the criteria weight of interest, including the modified coffee flavor parameter from SCAA [35]. Furthermore, this was assessed based on the panelists' knowledge about the coffee taste. This aimed to determine the weight of interest of each criterion used in assessing the taste (Table 1).
Table 1. Assessment on the weight of interest of the coffee taste criteria
|
Criteria |
Definition |
Weight of Interest (%) |
|
Aroma |
The smell that comes from the result of mixing coffee and herbs. |
…. |
|
Flavor |
The unique impression between aroma and after taste, which is felt on the tongue and nose when the aroma of steam flows from the mouth to the nose. |
…. |
|
After taste |
The length of time the favorable flavor (taste and aroma) lasts after the coffee is swallowed. |
…. |
|
Balance |
The balance of flavors caused by the taste, after taste, and body when coffee is drunk. |
…. |
|
Body |
The sensation of fullness (heavy/thick or light) in the mouth that is felt between the tongue and the palate. |
…. |
|
Defect |
Negative taste or defects that reduce the quality of coffee taste. |
…. |
|
TOTAL |
100% |
|
The interest weighting was conducted before presenting the product sample based on the definition of each criterion and the knowledge of the panelists during their time as Arabica coffee connoisseurs. The value of the weight of interest determines the amount for each criterion. Therefore, the result plays an essential role in the decision-making using the MOORA method.
In the next stage, the panelists assessed the acceptance of the Gayo Arabica coffee brewed product mixed with herbs based on the taste criteria as presented in Table 1. For example, the panelists were presented with honeypresso, which consisted of 3 treatments, namely the addition of 5 ml, 7 ml, and 10 ml honey, in which they identified the taste of each variant served and assessed the variations. The rating scale used was based on the product acceptance level, as shown in Table 2.
Table 2. Product acceptance levelization
|
Value |
Acceptance Level |
|
1 |
Level 1 (lowest) |
|
2 |
Level 2 |
|
3 |
Level 3 |
|
4 |
Level 4 |
|
5 |
Level 5 (highest) |
2.3 Panelists
The sensory test used 10 identified panelists, with modified criteria from [36, 37].
The selection process of the panelist was conducted through interviews and filling out questionnaires related to consuming coffee habits and basic knowledge of coffee taste attributes.
The benefit of consistently determining the panelist's criteria is to ensure that the results of product acceptance decisions correctly meet the expectations of coffee connoisseurs who can identify flavors accurately through sensory testing.
2.4 Multi-objective optimization based on ratio analysis (MOORA) method
MOORA is a multi-objective method of decision-making that optimizes two or more conflicting attributes simultaneously [38]. Furthermore, this method solves highly complex problems and risks [10]. The mechanism is to weigh each specified criterion. Then, the results will be obtained from the weight assessment in the form of a set of alternative options with the highest to lowest values [39].
Quoted from Gorener et al. [25], the steps for completing the MOORA method are follows.
2.4.1 Making a decision matrix
${{X}_{ij}}=\left( \begin{matrix} {{X}_{11}} & {{X}_{12}} & {{X}_{1n}} \\ {{X}_{21}} & {{X}_{22}} & {{X}_{2n}} \\ {{X}_{m1}} & {{X}_{m2}} & {{X}_{mn}} \\ \end{matrix} \right)$ (1)
The decision matrix is represented as Xij, where i represents m which is the number of alternatives, while j represents n which is the number of criteria.
2.4.2 Decision matrix normalization
The normalizing matrix was performed by dividing each score on the criteria by the denominator, which is the square root of the sum of the squares of each alternative per attribute [40]. The following describes the ratio:
$X_{ij}^{*}=\frac{{{x}_{ij}}}{\sqrt{\left[ \sum\nolimits_{i=1}^{m}{{{x}_{ij}}^{2}} \right]}}$ (2)
where, X*ij is a number included in the interval [0,1] and represents the result of the i-th alternative normalization on the j-th attribute.
2.4.3 Attribute optimization
The attribute optimization value is obtained based on the maximum (benefit) minus the minimum value (cost). It is described using the following formula:
${{y}_{i}}^{*}=\sum\nolimits_{j=1}^{g}{x_{ij}^{*}-\sum\nolimits_{j=g+1}^{n}{x_{ij}^{*}}}$ (3)
where, g and (n-g) represent the maximum and minimum attribute, respectively, and yi is the normalized value of the i-th alternative on all attributes. Generally, it is often observed that some attributes are more important than others. Therefore, it is multiplied by the weight of interest to increase its priority. The following formula results when considering the attribute weight of interest.
${{y}_{i}}^{*}=\sum\nolimits_{j=1}^{g}{{{w}_{j}}x_{ij}^{*}-\sum\nolimits_{j=g+1}^{n}{{{w}_{j}}x_{ij}^{*}}}$ (4)
where, wj is the weight of the interest of the j criteria.
2.4.4 Alternative ranking
The value of yi can be positive or negative, depending on the total maximum and minimum attributes in the decision matrix. The best alternative has the highest yi value, while the worst has the lowest yi value.
The overall research stages above can be summarized in Figure 1.
Figure 1. Research stage
3.1 Criteria weight of interest
The evaluation of the criteria weight of interest in percentages based on panelists' assessment shows that aroma weights interest of 23%, flavor 23%, after taste 18%, balance 15%, body 12%, and defect 9% (Figure 2).
Figure 2. Evaluation of criteria weight of interest
Aroma and flavor have the highest weight of interest, among other criteria. Furthermore, Agustina et al. [41] stated that aroma plays an essential role in product acceptance testing because it greatly determines the level of consumer acceptance of a product. Likewise, according to Ranitaswari et al. [42], the flavor is essential in describing the product quality. Also, it was observed by Fadhil and Nurba [43] that in the sensory assessment of Gayo Arabica coffee, the flavor is the most or very important criterion according to consumer preference.
This proves that aroma and flavor have a significant influence on the flavor parameters of the coffee. Hence, it is natural that these two criteria have a higher significance of interest than the others.
The weight of interest is the level of interest compared to other criteria in a multi-criteria decision-making system. In the MOORA method, the interest of the criteria plays an essential role in the alternative ranking process, which affects the final decision making [44].
3.2 Decision matrix normalization
The sensory test results for assessing the taste of Gayo Arabica coffee with the addition of various herbal mixtures are as shown in Table 3. Furthermore, there were normalized with the matrix using Eq. (2).
The decision matrix normalization for each Gayo Arabica coffee brewed product with the addition of herbs is shown in Table 4.
3.3 Attribute optimization
Attribute optimization is the multiplication of the evaluation results of the importance of criteria weights by the panelists (Figure 2) with the results of normalization of the decision matrix (Table 4), resulting in data as shown in Table 5. Optimizing this attribute is very meaningful to help produce decisions from some alternatives considered. According to Khan and Maity [45], attribute normalization is an essential part of the MOORA method because this process determines the final result in the form of the best alternative.
Table 3. Recapitulation of panelists’ assessment results
|
No |
Product (A) |
Amount of Added Herbs (H) |
Criteria (C) |
|||||
|
1 |
2 |
3 |
4 |
5 |
6 |
|||
|
Aroma |
Flavor |
Aftertaste |
Balance |
Body |
Defect |
|||
|
1 |
Cuminpresso |
5 ml (1) |
2.2 |
1.9 |
1.6 |
1.9 |
2.2 |
2.4 |
|
7 ml (2) |
2.2 |
1.7 |
1.6 |
1.8 |
1.9 |
2.5 |
||
|
10 ml (3) |
2.2 |
1.8 |
1.5 |
1.9 |
2.0 |
2.4 |
||
|
2 |
Olivepresso |
5 ml (1) |
2.7 |
1.7 |
1.6 |
1.8 |
1.7 |
2.1 |
|
7 ml (2) |
2.7 |
1.6 |
1.5 |
1.6 |
1.7 |
2.1 |
||
|
10 ml (3) |
2.3 |
1.5 |
1.4 |
1.6 |
1.3 |
1.9 |
||
|
3 |
Kurmapresso |
5 ml (1) |
3.5 |
3.0 |
2.9 |
2.9 |
3.1 |
2.8 |
|
7 ml (2) |
3.5 |
2.9 |
2.7 |
2.9 |
2.6 |
2.4 |
||
|
10 ml (3) |
3.1 |
3.1 |
3.0 |
3.2 |
3.1 |
2.8 |
||
|
4 |
Karipresso |
5 ml (1) |
3.5 |
2.9 |
2.7 |
2.9 |
2.8 |
2.3 |
|
7 ml (2) |
3.1 |
2.6 |
2.9 |
2.5 |
2.6 |
2.1 |
||
|
10 ml (3) |
3.1 |
2.7 |
2.9 |
2.5 |
2.4 |
2.2 |
||
|
5 |
Honeypresso |
5 ml (1) |
3.9 |
3.9 |
3.7 |
3.4 |
3.2 |
2.5 |
|
7 ml (2) |
3.7 |
4.3 |
3.6 |
3.7 |
3.6 |
2.6 |
||
|
10 ml (3) |
3.3 |
3.3 |
3.3 |
3.3 |
3.4 |
2.7 |
||
Table 4. Result of decision matrix normalization
|
No |
Product (A) |
Amount of Added Herbs (H) |
Criteria (C) |
|||||
|
1 |
2 |
3 |
4 |
5 |
6 |
|||
|
Aroma |
Flavor |
Aftertaste |
Balance |
Body |
Defect |
|||
|
1 |
Cuminpresso |
5 ml (1) |
0.577 |
0.609 |
0.589 |
0.587 |
0.624 |
0.569 |
|
7 ml (2) |
0.577 |
0.545 |
0.589 |
0.557 |
0.538 |
0.593 |
||
|
10 ml (3) |
0.577 |
0.577 |
0.553 |
0.587 |
0.567 |
0.569 |
||
|
2 |
Olivepresso |
5 ml (1) |
0.606 |
0.613 |
0.615 |
0.623 |
0.622 |
0.596 |
|
7 ml (2) |
0.606 |
0.577 |
0.576 |
0.553 |
0.622 |
0.596 |
||
|
10 ml (3) |
0.516 |
0.541 |
0.538 |
0.553 |
0.476 |
0.539 |
||
|
3 |
Kurmapresso |
5 ml (1) |
0.599 |
0.577 |
0.584 |
0.557 |
0.608 |
0.605 |
|
7 ml (2) |
0.599 |
0.558 |
0.543 |
0.557 |
0.510 |
0.518 |
||
|
10 ml (3) |
0.531 |
0.596 |
0.604 |
0.615 |
0.608 |
0.605 |
||
|
4 |
Karipresso |
5 ml (1) |
0.624 |
0.612 |
0.550 |
0.634 |
0.621 |
0.603 |
|
7 ml (2) |
0.553 |
0.549 |
0.591 |
0.547 |
0.576 |
0.551 |
||
|
10 ml (3) |
0.553 |
0.570 |
0.591 |
0.547 |
0.532 |
0.577 |
||
|
5 |
Honeypresso |
5 ml (1) |
0.618 |
0.584 |
0.604 |
0.566 |
0.543 |
0.555 |
|
7 ml (2) |
0.587 |
0.644 |
0.588 |
0.615 |
0.611 |
0.577 |
||
|
10 ml (3) |
0.523 |
0.494 |
0.539 |
0.549 |
0.577 |
0.599 |
||
Table 5. Attribute optimization process
|
Alternatives |
Aroma |
Flavor |
Aftertaste |
Balance |
Body |
Defect |
|
Cuminpresso |
||||||
|
5 ml |
0.23 × 0.577 |
0.23 × 0.609 |
0.18 × 0.589 |
0.15 × 0.587 |
0.12 × 0.624 |
0.9 × 0.569 |
|
7 ml |
0.23 × 0.577 |
0.23 × 0.545 |
0.18 × 0.589 |
0.15 × 0.557 |
0.12 × 0.538 |
0.9 × 0.593 |
|
10 ml |
0.23 × 0.577 |
0.23 × 0.577 |
0.18 × 0.553 |
0.15 × 0.587 |
0.12 × 0.567 |
0.9 × 0.569 |
|
Olivepresso |
||||||
|
5 ml |
0.23 × 0.606 |
0.23 × 0.613 |
0.18 × 0.615 |
0.15 × 0.623 |
0.12 × 0.622 |
0.9 × 0.596 |
|
7 ml |
0.23 × 0.606 |
0.23 × 0.577 |
0.18 × 0.576 |
0.15 × 0.553 |
0.12 × 0.622 |
0.9 × 0.596 |
|
10 ml |
0.23 × 0.516 |
0.23 × 0.541 |
0.18 × 0.538 |
0.15 × 0.553 |
0.12 × 0.476 |
0.9 × 0.539 |
|
Kurmapresso |
||||||
|
5 ml |
0.23 × 0.599 |
0.23 × 0.577 |
0.18 × 0.584 |
0.15 × 0.557 |
0.12 × 0.608 |
0.9 × 0.605 |
|
7 ml |
0.23 × 0.599 |
0.23 × 0.558 |
0.18 × 0.543 |
0.15 × 0.557 |
0.12 × 0.510 |
0.9 × 0.518 |
|
10 ml |
0.23 × 0.531 |
0.23 × 0.596 |
0.18 × 0.604 |
0.15 × 0.615 |
0.12 × 0.608 |
0.9 × 0.605 |
|
Karipresso |
||||||
|
5 ml |
0.23 × 0.624 |
0.23 × 0.612 |
0.18 × 0.550 |
0.15 × 0.634 |
0.12 × 0.621 |
0.9 × 0.603 |
|
7 ml |
0.23 × 0.553 |
0.23 × 0.549 |
0.18 × 0.591 |
0.15 × 0.547 |
0.12 × 0.576 |
0.9 × 0.551 |
|
10 ml |
0.23 × 0.553 |
0.23 × 0.570 |
0.18 × 0.591 |
0.15 × 0.547 |
0.12 × 0.532 |
0.9 × 0.577 |
|
Honeypresso |
||||||
|
5 ml |
0.23 × 0.618 |
0.23 × 0.584 |
0.18 × 0.604 |
0.15 × 0.566 |
0.12 × 0.543 |
0.9 × 0.555 |
|
7 ml |
0.23 × 0.587 |
0.23 × 0.644 |
0.18 × 0.588 |
0.15 × 0.615 |
0.12 × 0.611 |
0.9 × 0.577 |
|
10 ml |
0.23 × 0.523 |
0.23 × 0.494 |
0.18 × 0.539 |
0.15 × 0.549 |
0.12 × 0.577 |
0.9 × 0.599 |
Table 6. Attribute optimization results
|
Alternatives |
Aroma |
Flavor |
Aftertaste |
Balance |
Body |
Defect |
|
Cuminpresso |
||||||
|
5 ml |
0.135 |
0.142 |
0.103 |
0.088 |
0.077 |
0.048 |
|
7 ml |
0.135 |
0.127 |
0.103 |
0.083 |
0.066 |
0.050 |
|
10 ml |
0.135 |
0.135 |
0.097 |
0.088 |
0.070 |
0.048 |
|
Olivepresso |
||||||
|
5 ml |
0.141 |
0.143 |
0.108 |
0.093 |
0.077 |
0.051 |
|
7 ml |
0.141 |
0.135 |
0.101 |
0.083 |
0.077 |
0.051 |
|
10 ml |
0.120 |
0.126 |
0.094 |
0.083 |
0.059 |
0.046 |
|
Kurmapresso |
||||||
|
5 ml |
0.140 |
0.135 |
0.102 |
0.084 |
0.075 |
0.051 |
|
7 ml |
0.140 |
0.131 |
0.095 |
0.084 |
0.063 |
0.044 |
|
10 ml |
0.124 |
0.140 |
0.106 |
0.092 |
0.075 |
0.051 |
|
Karipresso |
||||||
|
5 ml |
0.145 |
0.143 |
0.096 |
0.095 |
0.076 |
0.051 |
|
7 ml |
0.129 |
0.128 |
0.103 |
0.082 |
0.071 |
0.047 |
|
10 ml |
0.129 |
0.133 |
0.103 |
0.082 |
0.065 |
0.049 |
|
Honeypresso |
||||||
|
5 ml |
0.144 |
0.137 |
0.106 |
0.085 |
0.067 |
0.047 |
|
7 ml |
0.137 |
0.151 |
0.103 |
0.092 |
0.075 |
0.049 |
|
10 ml |
0.122 |
0.116 |
0.094 |
0.082 |
0.071 |
0.051 |
The results of attributes optimization of each criterion on the Gayo Arabica coffee brewed product with the addition of herbs are shown in Table 6.
3.4 Alternative ranking
The last stage is the alternative ranking process for each Gayo Arabica coffee brewed product with a mixture of various herbs, namely the criteria that have a benefit value minus a detrimental value (cost), as shown in Table 7.
3.5 Product acceptance level
In the sensory assessment of the Gayo Arabica coffee brewed product, each panelist has a different acceptance level for each alternative product presented primarily because of the use of different types of herbs as mixed ingredients.
Furthermore, each herb used has a distinctive taste characteristic that affects the taste and level of panelists' preference for the product based on the concentration of the mixture. This is in line with the research conducted by Fitriyah et al. [46] regarding the quality of Arabica ground coffee, which states that each panelist has a different acceptance level based on different processing methods. Fadhil et al. [37] stated that implementing the decision-making system method in multi-criteria decision-making was easy and successful in selecting several alternatives to a product based on the panelist's assessment. In principle, the decision-making system method aims to determine the best alternative from the available [25]. Based on the calculations using the MOORA method, the following shows the best alternative for each type of Gayo Arabica coffee brewing.
3.5.1 Cuminpresso
Based on the sensory assessment of cuminpresso, the panelists preferred adding 5 ml black cumin oil with a score of 0.135 aroma, 0.142 flavor, 0.103 after taste, 0.088 balance, 0.077 body, and 0.048 defect (Table 6). All the cuminpresso alternatives produce the same value on the aroma criteria because black cumin oil has a pungent aroma. Hence, it is a little difficult to distinguish between each alternative. According to Hayulistya et al. [47], this pungent aroma is produced from volatile compounds in black cumin. Generally, the alternative of adding 5 ml of black cumin oil was superior to each of the criteria compared to others. Pratiwi et al. [48] stated that in research on the manufacture of carbonated coffee beverages, the addition of black cumin oil was not suitable, hence, the panelists have less interest in the product of the coffee produced.
3.5.2 Olivepresso
For the sensory assessment of olivepresso, the panelists preferred adding 5 ml olive oil with a score of 0.141 aroma, 0.143 flavor, 0.108 after taste, 0.093 balance, 0.077 body, and 0.051 defect (Table 6). Furthermore, the olivepresso with the addition of 7 ml of olive oil has almost the same score as 5 ml. However, the alternative of adding 5 ml of olive oil was superior in terms of flavor, after taste, and balance criteria. Its excessive addition to the 30 ml espresso base made the panelists less interested in the resulting beverage. In research conducted by Arranz et al. [49] regarding the making of functional beverages with olive oil, it was stated that respondents prefer products with a composition of olive oil that is not too much but also not too little.
3.5.3 Kurmapresso
Based on the sensory assessment, the alternative of adding 10 ml dates was the most preferred by the panelists with a score of 0.124 aroma, 0.140 flavor, 0.106 after taste, 0.092 balance, 0.075 body, and 0.051 defect (Table 6). However, the panelists preferred the aroma criteria for the alternative addition of 5 ml and 7 ml of dates to the 10 ml because of their strong coffee aroma. Generally, the panelists preferred adding 10 ml of dates because its characteristic sweet taste was able to dominate and cover the bitter taste of coffee. Raiesi Ardali et al. [50] stated that the higher the concentration of date palm juice used, the higher the acceptability of a beverage product. Furthermore, the high fructose content in dates is good for health and has a higher sweet taste.
3.5.4 Karipresso
In the karipresso sensory assessment, the panelists preferred the addition of 5 ml curry leaf boiled water with a score of 0.145 aroma, 0.143 flavor, 0.096 after taste, 0.095 balance, 0.076 body, and 0.051 defect (Table 6). Furthermore, the panelists preferred the alternative of adding 5 ml because the aroma and after taste caused by curry leaves were not too dominant and did not eliminate the character of the coffee taste. Based on research conducted by Aurelia [51] regarding the making of functional beverages with the addition of curry leaf essential oil, the high use of its essential oil made the panelists less interested in the product.
Table 7. Ranking for each product
|
Alternatives |
Maximum |
Minimum |
Yi = Max – Min |
Rank |
|
C1+C2+C3+C4+C5 |
C6 |
|||
|
Cuminpresso |
||||
|
5 ml |
0.545 |
0.048 |
0.497 |
1 |
|
7 ml |
0.515 |
0.050 |
0.464 |
3 |
|
10 ml |
0.524 |
0.048 |
0.476 |
2 |
|
Olivepresso |
||||
|
5 ml |
0.562 |
0.051 |
0.511 |
1 |
|
7 ml |
0.536 |
0.051 |
0.486 |
2 |
|
10 ml |
0.482 |
0.046 |
0.437 |
3 |
|
Kurmapresso |
||||
|
5 ml |
0.535 |
0.051 |
0.484 |
2 |
|
7 ml |
0.512 |
0.044 |
0.468 |
3 |
|
10 ml |
0.536 |
0.051 |
0.485 |
1 |
|
Karipresso |
||||
|
5 ml |
0.556 |
0.051 |
0.505 |
1 |
|
7 ml |
0.513 |
0.047 |
0.467 |
2 |
|
10 ml |
0.513 |
0.049 |
0.464 |
3 |
|
Honeypresso |
||||
|
5 ml |
0.538 |
0.047 |
0.491 |
2 |
|
7 ml |
0.558 |
0.049 |
0.509 |
1 |
|
10 ml |
0.485 |
0.051 |
0.434 |
3 |
Descriptions: C1= Aroma; C2= Flavor; C3= Aftertaste; C4= Balance; C5= Body; C6= Defect
Figure 3. The best formulation of Gayo Arabica coffee brewed with a mixture of herbs
3.5.5 Honeypresso
Based on the sensory assessment of honeypresso, the panelists preferred the alternative of adding 7 ml of honey with a score of 0.137 aroma, 0.151 flavor, 0.103 after taste, 0.092 balance, 0.075 body, and 0.049 defect. The addition of 7 ml of honey was an alternative product with the highest value compared to 5 ml and 10 ml because the distinctive bitter taste in coffee does not entirely disappear and creates the right blend. According to Aji et al. [52] research on functional beverages with honey as a sweetener, panelists have different preferences for each parameter. However, the appropriate honey and coffee flavors mixture has the best acceptance level from the various products available. Sharma et al. [53] added that honey is very well used as a natural sweetener in beverage products, especially for consumers concerned about their health. Based on the results above, the composition of the Gayo Arabica coffee brewed product with a mixture of herbs is as shown in Figure 3.
The assessment of acceptance of the Gayo Arabica coffee brewed product with a mixture of herbs discovered the best alternative for each type of brewing to be cuminpresso with 30 ml espresso and 5 ml black cumin oil, olivepresso with 30 ml espresso and 5 ml olive oil, kurmapresso with 30 ml espresso and 10 ml dates, karipresso with 30 ml espresso and 5 ml curry leave-water, and honeypresso with 30 ml of espresso and 7 ml of honey. Therefore, using the MOORA method as a decision-making system solved the problem of determining the best alternative for each type of Gayo Arabica coffee brewed with a mixture of herbs, hence it is easier to make decisions about product acceptance from some available alternatives.
The contribution of this research lies in two things at once. First, to produce the best brewing composition for mixing Arabica coffee with various herbs, it becomes a separate reference for coffee entrepreneurs and coffee connoisseurs around the world. Second, the application of the MOORA method for product acceptance decision making can be one of the methods that can be useful in the coffee taste sensory assessment system. Therefore, this article is one of the treasures of knowledge related to research on mixing coffee with various herbs that can enrich similar research related to brewing coffee.
The author would like to express his gratitude and highest appreciation to KEMENDIKBUDRISTEK and Riset Inovatif Produktif (RISPRO) Lembaga Pengelola Dana Pendidikan (LPDP) for the support of the Riset Keilmuan for the 2022 fiscal year Number 011/E4.1/AK.04.RA/2021.
[1] Syah, H., Yusmanizar, Y., Maulana, O. (2013). Karakteristik fisik bubuk kopi arabika hasil penggilingan mekanis dengan penambahan jagung dan beras ketan. Jurnal Teknologi dan Industri Pertanian Indonesia, 5(1): 32-37. https://doi.org/10.17969/jtipi.v5i1.1000
[2] Yulianti, Y., Deliana, Y. (2018). Gaya hidup kaitannya dengan keputusan konsumen dalam membeli minuman kop. Jurnal AGRISEP: Kajian Masalah Sosial Ekonomi Pertanian dan Agribisnis, 17(1): 39-50. https://doi.org/10.31186/jagrisep.17.1.39-50
[3] Kurniawan, A., Ridlo, M.R. (2017). Perilaku konsumtif remaja penikmat warung kopi. DILEMA, 32(1): 9-22.
[4] Asmono, S.L., Kristiawan, A.B., Handayani, H.T., Kusumaningtyas, R.N. (2021). Penambahan bubuk daun stevia pada minuman kopi arabika terhadap tingkat kesukaan konsumen. Jurnal Ilmiah Inovasi, 21(1): 27-32. https://doi.org/10.25047/jii.v21i1.2631
[5] Illy, A., Viani, R. (Eds.). (2005). Espresso Coffee: The Science of Quality. Academic Press.
[6] Khezri, M., Shahriari, S., Shahsavani, L. (2017). The effect of xanthan gum and temperature on foam stability of milk-based espresso coffees. Journal of Food Biosciences and Technology, 7(1): 15-22.
[7] Abubakar, Y., Sabariana, S., Rasdiansyah, R., Hasni, D. (2021). Sensory characteristic of espresso coffee prepared from Gayo arabica coffee roasted at various times and temperatures. In IOP Conference Series: Earth and Environmental Science, 667(1): 012048. https://doi.org/10.1088/1755-1315/667/1/012048
[8] Fisdiana, U., Anggriani, R.A., Hariyanto, B., Hasanah, F. (2021). Analisis tingkat kesukaan konsumen pada produk sirup kopi dengan penambahan susu full cream. In Agropross: National Conference Proceedings of Agriculture, pp. 197-206. https://doi.org/10.25047/agropross.2021.222
[9] Angeloni, G., Guerrini, L., Masella, P., Bellumori, M., Daluiso, S., Parenti, A., Innocenti, M. (2019). What kind of coffee do you drink? An investigation on effects of eight different extraction methods. Food Research International, 116: 1327-1335. https://doi.org/10.1016/j.foodres.2018.10.022
[10] Brauers, W.K., Zavadskas, E.K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35(2): 445-469.
[11] Fadlan, C., Windarto, A.P., Damanik, I.S. (2019). Penerapan metode MOORA pada sistem pemilihan bibit cabai (kasus: Desa bandar siantar kecamatan gunung malela). Journal of Applied Informatics and Computing (JAIC), 3(2): 42-46. https://doi.org/10.30871/jaic.v3i2.1324
[12] Stanujkic, D., Magdalinovic, N., Stojanovic, S., Jovanovic, R. (2012). Extension of ratio system part of MOORA method for solving decision-making problems with interval data. Informatica, 23(1): 141-154. http://dx.doi.org/10.15388/Informatica.2012.353
[13] Brauers, W.K.M., Ginevičius, R., Podvezko, V. (2010). Regional development in Lithuania considering multiple objectives by the MOORA method. Technological and Economic Development of Economy, 16(4): 613-640. https://doi.org/10.3846/tede.2010.38
[14] Karande, P., Chakraborty, S. (2012). Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Materials & Design, 37: 317-324. https://doi.org/10.1016/j.matdes.2012.01.013
[15] Patnaik, P.K., Swain, P.T.R., Mishra, S.K., Purohit, A., Biswas, S. (2020). Composite material selection for structural applications based on AHP-MOORA approach. Materials Today: Proceedings, 33: 5659-5663. https://doi.org/10.1016/j.matpr.2020.04.063
[16] Raju, S.S., Murali, G.B., Patnaik, P.K. (2020). Ranking of Al-CSA composite by MCDM approach using AHP–TOPSIS and MOORA methods. Journal of Reinforced Plastics and Composites, 39(19-20): 721-732. https://doi.org/10.1177/0731684420924833
[17] Shihab, S., Khan, N., Myla, P., Upadhyay, S., Khan, Z., Siddiquee, A. (2018). Application of MOORA method for multi optimization of GMAW process parameters in stain-less steel cladding. Management Science Letters, 8(4): 241-246. http://dx.doi.org/10.5267/j.msl.2018.2.002
[18] Tundo, T., Nugroho, W.D. (2020). An Alternative in Determining the Best Wood for Guitar Materials Using MOORA Method. IJID (International Journal on Informatics for Development), 9(1): 37-44. https://doi.org/10.14421/ijid.2020.09106
[19] Fadli, S., Imtihan, K. (2019). Implementation of MOORA method in evaluating work performance of honorary teachers. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 4(1): 128-135. https://doi.org/10.33395/sinkron.v4i1.10192
[20] Hanifatulqolbi, D., Ismail, I.E., Hammad, J., Al-Hooti, M.H. (2019). Decision support system for considering the best teacher performance using MOORA method. In Journal of Physics: Conference Series, 1193(1): 012018. https://doi.org/10.1088/1742-6596/1193/1/012018
[21] El-Santawy, M.F., El-Dean, R.A.Z. (2012). Selection of a consulting firm by using SDV-MOORA. Life Sci J, 9(2s): 126-128.
[22] Chand, M., Raj, T., Shankar, R. (2015). A comparative study of multi criteria decision making approaches for risks assessment in supply chain. International Journal of Business Information Systems, 18(1): 67-84.
[23] Dey, B., Bairagi, B., Sarkar, B., Sanyal, S. (2012). A MOORA based fuzzy multi-criteria decision making approach for supply chain strategy selection. International Journal of Industrial Engineering Computations, 3(4): 649-662. https://doi.org/10.5267/j.ijiec.2012.03.001
[24] Sahu, A.K., Datta, S., Mahapatra, S.S. (2014). Supply chain performance benchmarking using grey-MOORA approach: An empirical research. Grey Systems: Theory and Application, 4(1): 24-55. https://doi.org/10.1108/GS-07-2013-0011
[25] Gorener, A., Dinçer, H., Hacioglu, U. (2013). Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for bank branch location selection. International Journal of Finance & Banking Studies, 2(2): 41-52. https://doi.org/10.20525/ijfbs.v2i2.145
[26] Özbek, A. (2015). Efficiency analysis of foreign-capital banks in Turkey by OCRA and MOORA. Research Journal of Finance and Accounting, 6(13): 21-30.
[27] Attri, R., Grover, S. (2014). Decision making over the production system life cycle: MOORA method. International Journal of System Assurance Engineering and Management, 5(3): 320-328. https://doi.org/10.1007/s13198-013-0169-2
[28] Mallick, B., Sarkar, B., Das, S. (2017). Application of the moora method for multi-criteria inventory classification. Indian Science Cruiser, 31(6): 15-21. https://doi.org/10.24906/isc%2F2017%2Fv31%2Fi6%2F166459
[29] Pérez-Domínguez, L., Mojica, K.S., Pabón, L.O., Díaz, M.C. (2018). Application of the MOORA method for the evaluation of the industrial maintenance system. In Journal of Physics: Conference Series, 1126(1): 012018. https://doi.org/10.1088/1742-6596/1126/1/012018
[30] Kusnadi, S., Jaelani, L. (2020). Sistem pendukung keputusan pemilihan lahan untuk tanam bibit pandanwangi dengan menggunakan metode moora di dinas pertanian perkebunan pangan dan hortikultura kabupaten cianjur. Media Jurnal Informatika, 12(1): 18-28. https://doi.org/10.35194/mji.v12i1.1193
[31] Pasaribu, S.W., Rajagukguk, E., Sitanggang, M., Rahim, R., Abdillah, L.A. (2018). Implementasi multi-objective optimization on the basis of ratio analysis (MOORA) untuk menentukan kualitas buah mangga terbaik. JURIKOM (Jurnal Riset Komputer), 5(1): 50-55. http://dx.doi.org/10.30865/jurikom.v5i1.571
[32] Sutarno, S., Mesran, M., Supriyanto, S., Yuliana, Y., Dewi, A. (2019). Implementation of multi-objective optimazation on the base of ratio analysis (MOORA) in improving support for decision on sales location determination. In Journal of Physics: Conference Series, 1424(1): 012019. https://doi.org/10.1088/1742-6596/1424/1/012019
[33] Bachtiar, R. (2011). Pembuatan Minuman Instan Sari Kurma (Phoenix dactylifera) (Making Instant Beverage of Date Essence (Phoenix dactylifera)). http://repository.ipb.ac.id/handle/123456789/53057, accessed on Aug. 2, 2021. (in Indonesian)
[34] Fachraniah, F., Kurniasih, E., Novilasi, D.T. (2012). Ekstraksi Antioksidan dari Daun Kari. Jurnal Reaksi (Journal of Science and Technology), 10(21): 35-44. http://jurnal.pnl.ac.id/wp-content/plugins/Flutter/files_flutter/1393552126EkstrakKari.pdf.
[35] SCAA [Specialty Coffee Association of America]. (2021) http://scaa.org/index.php?goto=&page=resources&d=coffee-standards, accessed on Aug. 2, 2021.
[36] Lokaria, E., Susanti, I. (2018). Uji organoleptik kopi biji salak dengan varian waktu penyangraian. BIOEDUSAINS: Jurnal Pendidikan Biologi Dan Sains, 1(1): 34-42. https://doi.org/10.31539/bioedusains.v1i1.262
[37] Fadhil, R., Nurba, D., Sukmawati, E. (2021). Sensory Assessment of Gayo arabica coffee taste based on various varieties and manual brewing devices. Coffee Science, 16: e161918-e161918. https://doi.org/10.25186/.v16i.1918
[38] Chakraborty, S. (2011). Applications of the MOORA method for decision making in manufacturing environment. The International Journal of Advanced Manufacturing Technology, 54(9): 1155-1166. https://doi.org/10.1007/s00170-010-2972-0
[39] Yanifa, N.R. (2017). Implementasi Metode Moora (Multi–Objective Optimization on The Basis of Ratio Analysis) Pada Penerimaan Beasiswa Di Universitas Muhammadiyah Jember Berbasis Web (Doctoral dissertation, Universitas Muhammadiyah Jember).
[40] Poningsih, P., Saragih, R., Sinaga, S.B., Sinaga, J.L.S., Hasibuan, F.A., Agustina, N., Alifah, W., Deswiyan, I.A., Widiastari, A., Apriani, T., Wulandika, S., Solikhun, S. (2020). Sistem pendukung keputusan: Penerapan dan 10 contoh studi kasus. Medan, Indonesia: Yayasan Kita Menulis.
[41] Agustina, R., Nurba, D., Antono, W., Septiana, R. (2019). Pengaruh suhu dan lama penyangraian terhadap sifat fisik–kimia kopi arabika dan kopi robusta. In Prosiding Seminar Nasional, 285-299.
[42] Ranitaswari, P.A., Mulyani, S., Sadyasmara, C.A.B. (2018). Analisis kepuasan konsumen terhadap kualitas produk kopi dan kualitas pelayanan menggunakan metode importance perfomance analysis (studi kasus di geo coffee). Jurnal Rekayasa dan Manajemen Agroindustri, 6(2): 147-157.
[43] Fadhil, R., Nurba, D. (2019). Comparison of gayo arabica coffee taste sensory scoring system between eckenrode and fuzzy-eckenrode methods. In IOP Conference Series: Earth and Environmental Science, 365(1): 012040. https://doi.org/10.1088/1755-1315/365/1/012040
[44] Žižović, M., Miljković, B., Marinković, D. (2020). Objective methods for determining criteria weight coefficients: A modification of the CRITIC method. Decision Making: Applications in Management and Engineering, 3(2): 149-161. https://doi.org/10.31181/dmame2003149z
[45] Khan, A., Maity, K.P. (2016). Parametric optimization of some non-conventional machining processes using MOORA method. In International journal of engineering research in Africa, 20: 19-40. https://doi.org/10.4028/www.scientific.net/JERA.20.19
[46] Fitriyah, A.T., Kape, D., Baharuddin, B., Utami, R.R. (2021). Analisis mutu organoleptik kopi bubuk arabika (coffea arabica) bittuang toraja. Jurnal Industri Hasil Perkebunan, 16(1): 72-82. http://dx.doi.org/10.33104/jihp.v16i1.6902
[47] Hayulistya, D.P., Affandi, D.R., Sari, A.M. (2016). Pengaruh penambahan bubuk jintan hitam (nigella sativa) terhadap aktivitas antioksidan permen jelly herbal. Jurnal Teknosains Pangan, 5(4): 23-30.
[48] Pratiwi, A.T., Darma, G.C., Darusman, F. (2019). Formulasi Sediaan Kopi Berkarbonasi Mengandung Minyak Jintan Hitam dan Madu Melalui Fermentasi Menggunakan Ragi (Saccharomyces Cerevisiae).
[49] Arranz, S., Martínez-Huélamo, M., Vallverdu-Queralt, A., Valderas-Martinez, P., Illán, M., Sacanella, E., Lamuela-Raventos, R.M. (2015). Influence of olive oil on carotenoid absorption from tomato juice and effects on postprandial lipemia. Food Chemistry, 168: 203-210. https://doi.org/10.1016/j.foodchem.2014.07.053
[50] Raiesi Ardali, F., Rahimi, E., Tahery, S., Shariati, M.A. (2014). Production of a new drink by using date syrup and milk. Journal of Food Biosciences and Technology, 4(2): 67-72.
[51] Aurelia, L. (2021). Pengaruh konsentrasi minyak atsiri daun kari (Murraya koenigii (L.) Spreng) dan madu terhadap karakteristik minuman fungsional= The effect of curry leaves (Murraya koenigii (L.) Spreng) essential oil and honey concentration on characteristics of functional drink (Doctoral dissertation, Universitas Pelita Harapan).
[52] Aji, S.P., Anandito, R.B.K., Nurhartadi, E. (2013). Addition of various types of honey as alternative sweetener in white dragon (Hylocereus undatus) juice drink. Asian Journal of Natural Product Biochemistry, 11(1): 13-18. https://doi.org/10.13057/biofar/f110103
[53] Sharma, S., Vaidya, D., Rana, N. (2016). Honey as natural sweetener in lemon ready-to-serve drink. International Journal of Bio-resource and Stress Management, 7(2): 320-325. https://doi.org/10.5958/0976-4038.2016.00050.6
