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A Proposed Classification Method in Menu Engineering Using the K-Nearest Neighbors Algorithm

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@article{IJASEIT12172,
   author = {Nina Setiyawati},
   title = {A Proposed Classification Method in Menu Engineering Using the K-Nearest Neighbors Algorithm},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {4},
   year = {2021},
   pages = {1360--1365},
   keywords = {Menu engineering; k-NN; classification; 10-fold cross-validation.},
   abstract = {In the culinary business, the menu is crucial; therefore, the performance of each menu needs to be known to maintain business continuity. Menu engineering is a special technique used to see the performance comparison of each menu item. This research proposes modeling menu engineering with a new approach in classifying menu items using the k-Nearest Neighbors (k-NN) algorithm using the sales training data of sales data in 2019 belonging to one of the micro, small and medium-sized enterprises in the culinary sub-sector in Salatiga, Indonesia. In the modeling, the popularity index (menu mix) and item contribution margin are used as variables, while the menu item class is used as the label attribute of the classification. Determination of the k value in the k-NN algorithm was done by the experimental method so that it produces the most optimal k based on the highest accuracy value, while the distance calculation on k-NN was done using euclidean distance. Evaluation of the model was done using 10-fold cross-validation with four performance evaluation criteria, namely weighted mean recall, weighted mean precision, accuracy, classification error. Based on the evaluation results, an accuracy of 96.84% was obtained; thus, the proposed model is considered to have given good and accurate results. This proposed model has been implemented in MSME sales data to classify menu items. The results of this classification were used as a basis for recommending menu engineering strategies to MSMEs.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12172},
   doi = {10.18517/ijaseit.11.4.12172}
}

EndNote

%A Setiyawati, Nina
%D 2021
%T A Proposed Classification Method in Menu Engineering Using the K-Nearest Neighbors Algorithm
%B 2021
%9 Menu engineering; k-NN; classification; 10-fold cross-validation.
%! A Proposed Classification Method in Menu Engineering Using the K-Nearest Neighbors Algorithm
%K Menu engineering; k-NN; classification; 10-fold cross-validation.
%X In the culinary business, the menu is crucial; therefore, the performance of each menu needs to be known to maintain business continuity. Menu engineering is a special technique used to see the performance comparison of each menu item. This research proposes modeling menu engineering with a new approach in classifying menu items using the k-Nearest Neighbors (k-NN) algorithm using the sales training data of sales data in 2019 belonging to one of the micro, small and medium-sized enterprises in the culinary sub-sector in Salatiga, Indonesia. In the modeling, the popularity index (menu mix) and item contribution margin are used as variables, while the menu item class is used as the label attribute of the classification. Determination of the k value in the k-NN algorithm was done by the experimental method so that it produces the most optimal k based on the highest accuracy value, while the distance calculation on k-NN was done using euclidean distance. Evaluation of the model was done using 10-fold cross-validation with four performance evaluation criteria, namely weighted mean recall, weighted mean precision, accuracy, classification error. Based on the evaluation results, an accuracy of 96.84% was obtained; thus, the proposed model is considered to have given good and accurate results. This proposed model has been implemented in MSME sales data to classify menu items. The results of this classification were used as a basis for recommending menu engineering strategies to MSMEs.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12172
%R doi:10.18517/ijaseit.11.4.12172
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 4
%@ 2088-5334

IEEE

Nina Setiyawati,"A Proposed Classification Method in Menu Engineering Using the K-Nearest Neighbors Algorithm," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 4, pp. 1360-1365, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.4.12172.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Setiyawati, Nina
PY  - 2021
TI  - A Proposed Classification Method in Menu Engineering Using the K-Nearest Neighbors Algorithm
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 4
Y2  - 2021
SP  - 1360
EP  - 1365
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Menu engineering; k-NN; classification; 10-fold cross-validation.
N2  - In the culinary business, the menu is crucial; therefore, the performance of each menu needs to be known to maintain business continuity. Menu engineering is a special technique used to see the performance comparison of each menu item. This research proposes modeling menu engineering with a new approach in classifying menu items using the k-Nearest Neighbors (k-NN) algorithm using the sales training data of sales data in 2019 belonging to one of the micro, small and medium-sized enterprises in the culinary sub-sector in Salatiga, Indonesia. In the modeling, the popularity index (menu mix) and item contribution margin are used as variables, while the menu item class is used as the label attribute of the classification. Determination of the k value in the k-NN algorithm was done by the experimental method so that it produces the most optimal k based on the highest accuracy value, while the distance calculation on k-NN was done using euclidean distance. Evaluation of the model was done using 10-fold cross-validation with four performance evaluation criteria, namely weighted mean recall, weighted mean precision, accuracy, classification error. Based on the evaluation results, an accuracy of 96.84% was obtained; thus, the proposed model is considered to have given good and accurate results. This proposed model has been implemented in MSME sales data to classify menu items. The results of this classification were used as a basis for recommending menu engineering strategies to MSMEs.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12172
DO  - 10.18517/ijaseit.11.4.12172

RefWorks

RT Journal Article
ID 12172
A1 Setiyawati, Nina
T1 A Proposed Classification Method in Menu Engineering Using the K-Nearest Neighbors Algorithm
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 4
YR 2021
SP 1360
OP 1365
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Menu engineering; k-NN; classification; 10-fold cross-validation.
AB In the culinary business, the menu is crucial; therefore, the performance of each menu needs to be known to maintain business continuity. Menu engineering is a special technique used to see the performance comparison of each menu item. This research proposes modeling menu engineering with a new approach in classifying menu items using the k-Nearest Neighbors (k-NN) algorithm using the sales training data of sales data in 2019 belonging to one of the micro, small and medium-sized enterprises in the culinary sub-sector in Salatiga, Indonesia. In the modeling, the popularity index (menu mix) and item contribution margin are used as variables, while the menu item class is used as the label attribute of the classification. Determination of the k value in the k-NN algorithm was done by the experimental method so that it produces the most optimal k based on the highest accuracy value, while the distance calculation on k-NN was done using euclidean distance. Evaluation of the model was done using 10-fold cross-validation with four performance evaluation criteria, namely weighted mean recall, weighted mean precision, accuracy, classification error. Based on the evaluation results, an accuracy of 96.84% was obtained; thus, the proposed model is considered to have given good and accurate results. This proposed model has been implemented in MSME sales data to classify menu items. The results of this classification were used as a basis for recommending menu engineering strategies to MSMEs.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12172
DO  - 10.18517/ijaseit.11.4.12172