Penerapan Metode Two Step Cluster dalam Analisis Menu Engineering pada Usaha Kuliner

Setiyawati, Nina; Bangkalang, Dwi Hosanna

doi:10.25126/jtiik.2020722012

Cited by 4 publications

(10 citation statements)

References 7 publications

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“…In 2017, Tom and Annaraud [16] applied the fuzzy multi-criteria decision making (FMCDM) technique to model the engineering menu into nine quadrants. Setiyawati and Bangkalang also modeled menu engineering using a two-step cluster by adding one category variable, namely the item category, to see the dominant menu categories in each later formed cluster [31].…”

Section: Fig 1 Menu Engineering Matrixmentioning

confidence: 99%

“… Maintaining the quality of the menu, portions, and appearance according to the applicable recipe standards [16], [36]  Increasing the selling price periodically by taking into account the increase in existing demand and the selling price of competitors [31].  Customers tend to order items that "stand out", so menus in this classification can be placed in the visible section.…”

Section: B Menu Engineering Strategymentioning

confidence: 99%

“… Lowering prices while still paying attention to appropriate pricing strategies such as profit margin conditions, cost of goods sold, and competitors' selling price.  Give discounts on food delivery applications and/or promote the menu through social media to attract customers [31].  Offering larger portion sizes [37] or providing "addons" to increase the menu item's value.…”

Section: B Menu Engineering Strategymentioning

confidence: 99%

“… Removing or eliminating the menus. This is highly recommended to reduce substantial business burden [31].  Minimizing the number of menus in this classification as much as possible.…”

Section: ) Dogmentioning

confidence: 99%

“…With k-NN, a simple and effective classification method [28], modeling of menu item classification based on popularity index and contribution margin [29]- [31] is done using training data, where the results can be used as a basis for classifying new menu items in the test data. The process is done by calculating the distance of the menu item points on the test data with the menu item points on the training data using the Euclidean formula [32].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Setiyawati¹

2021

International Journal on Advanced Science, Engineering and Information Technology

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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 subsector 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.

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Section: Fig 1 Menu Engineering Matrixmentioning

confidence: 99%

Section: B Menu Engineering Strategymentioning

confidence: 99%

Section: B Menu Engineering Strategymentioning

confidence: 99%

Section: ) Dogmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Setiyawati¹

2021

International Journal on Advanced Science, Engineering and Information Technology

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Implementasi Metode TwoStep Clustering untuk Klasterisasi Karakteristik Akademik Mahasiswa

Santosa

Lukito

Chrismanto

2021

JEPIN

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Salah satu algoritma clustering yang paling banyak dipakai adalah K-Means dimana algoritma ini membutuhkan masukan jumlah klaster yang ingin dibentuk. Pada kenyataannya jumlah klaster yang tepat tidak bisa diketahui sehingga pemilihan nilai k bergantung pada subyektifitas peneliti. Kemudian algoritma K-Means hanya bisa menangani atribut dalam bentuk numerik kontinyu padahal ada atribut dalam bentuk kategorikal atau campuran keduanya. Pada penelitian ini dilakukan pengelompokkan data akademik mahasiswa dengan menggunakan algoritma twostep clustering yang dapat menentukan jumlah klaster secara otomatis dan dapat menangani atribut dalam bentuk kategorikal, numerik kontinyu atau campuran keduanya. Metode twostep clustering diterapkan pada data mahasiswa angkatan 2008-2019 dengan analisis diterapkan pada setiap angkatannya. Penelitian ini menghasilkan klaster-klaster yang mencerminkan tingkat heterogenitas setiap angkatan mahasiswa. Klaster-klaster yang didapat merupakan klaster yang optimal setelah diukur menggunakan Bayesian Information Criterion dan Ratio Distance Measure.

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Comparison of K-Means & K-Means++ Clustering Models using Singular Value Decomposition (SVD) in Menu Engineering

Setiyawati,

Bangkalang,

Purnomo

2023

JOIV : Int. J. Inform. Visualization

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The menu is one of the most fundamental aspects of business continuity in the culinary industry. One of the tools that can be used for menu analysis is menu engineering. Menu engineering is an analytical tool that assists restaurants, companies, and small and medium-sized enterprises (SMEs) in assessing and making decisions on marketing strategies, menu design, and sales so that it can produce maximum profit. In this study, several menu engineering models were proposed, and the performance of these models was analyzed. This study used a dataset from the Point of Sales (POS) application in an SME engaged in the culinary field. This research consists of three stages. First, pre-processing the data, comparing the models, and evaluating the models using the Davies Bouldin index. At the model comparison stage, four models are being compared: K-Means, K-Means++, K-Means using Singular Value Decomposition (SVD), and K-Means++ using SVD. SVD is used in the dataset transformation process. K-Means and K-Means++ algorithms are used for grouping menu items. The experiments show that the K-Means++ model with SVD produced the most optimal cluster in this research. The model produced an average cluster distance value of 0.002; the smallest Davies-Bouldin Index (DBI) value is 0.141. Therefore, using the K-Means++ model with SVD in menu engineering analysis produces clusters containing menu items with high similarity and significant distance between groups. The results obtained from the proposed model can be used as a basis for strategic decision-making of managing price, marketing strategy, etc., for SMEs, especially in the culinary business.

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Penerapan Metode Two Step Cluster dalam Analisis Menu Engineering pada Usaha Kuliner

Cited by 4 publications

References 7 publications

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

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

Implementasi Metode TwoStep Clustering untuk Klasterisasi Karakteristik Akademik Mahasiswa

Comparison of K-Means & K-Means++ Clustering Models using Singular Value Decomposition (SVD) in Menu Engineering

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