The SME performance recommendation system facing the 4.0 industrial revolution uses the Fuzzy ANP method

Kustiyahningsih, Yeni; Anamisa, Devie Rosa; Mufarroha, Fifin Ayu

doi:10.1088/1742-6596/1836/1/012036

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Among the nutrients needed in the human body, protein, fat, and carbohydrates are nutrients that can generate heat in the body during the process of human metabolism. It is an indispensable nutrient in the human body [21,22]. At the same time, these nutrients must be ingested in a certain proportion.…”

Section: Recommendation Accuracy Rate Of the Recommendationmentioning

confidence: 99%

Implementation of Key Technologies for a Healthy Food Culture Recommendation System Using Internet of Things

Xie

Lou

2022

Mobile Information Systems

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With the development of catering culture, the types of diets are changing with each passing day. The types of food are becoming more and more abundant, and the concept of healthy eating has become more and more prominent in people’s thinking. The development of Internet of Things technology enables people to live a variety of information in the network, and the amount of information increases sharply. With the development of the Internet of Things, the number of online recipes has increased significantly, and a number of choices are now available to people to find suitable recipes. It is still troublesome for people to select the menu of three meals on daily basis. In recent years, many people have long-term unhealthy eating habits, which has greatly increased the incidence of some diseases. Therefore, a balanced diet is very important for good health. This paper builds a fusion of healthy eating culture with the help of Internet of Things, deep learning, and other related technologies. A more practical healthy diet recommendation system is built, which aims to help people find diet recipes that conform to their personal eating preferences and habits and their health status. Experimental results show that the healthy food culture recommendation system constructed in this paper can more accurately recommend suitable healthy food recipes for individuals and promote people’s health. And the launched recipes can ensure nutrition and meet individual taste preferences, which has great practical significance.

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Section: Recommendation Accuracy Rate Of the Recommendationmentioning

confidence: 99%

Implementation of Key Technologies for a Healthy Food Culture Recommendation System Using Internet of Things

Xie

Lou

2022

Mobile Information Systems

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“…The latest average growth data proves that MSMEs in Indonesia are almost above 10% every year. MSMEs dominate domestic economic growth by utilizing resources [2]. The existence of MSMEs in society with its various advantages can increase the level of community involvement in improving the economy.…”

Section: Introductionmentioning

confidence: 99%

“…In the East Java region, the average growth of MSMEs reaches 54.34% per year and around 98% of the workforce can be absorbed in this sector. In order to increase the competitiveness of MSMEs in its region, the East Java Provincial Government is committed to providing support to the MSME sector which is currently increasingly popular [2][5] [6]. Much research on MSME clustering has been carried out using the K-Means Clustering method using cluster optimization using SSE, ELBOW and Sillhoutte Coefficient [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Integration K-Means clustering and AHP for recommendations batik MSMEs

Kustiyahningsih,

Khozaimi,

Khotimah

et al. 2024

E3S Web Conf.

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Batik MSME industry is a creative industry sector in Indonesia which contributes quite a lot to Gross Domestic Product. Batik products have been recognized worldwide as one of creative products from Indonesia by UNESCO which confirmed batik as an intangible Cultural Heritage of Humanity. There are around 250 batik makers in Madura Indonesia. The problem is that the large number of batik craftsmen makes it difficult for cooperatives to determine MSME priorities and the Cooperative Work Program. Some batik indicator data is not all filled and there is still categorical and numerical data. The aim is to group batik based on the number of workers, number of products, age, education, business license, turnover, and number of batik motifs. The method used is data preprocessing using Min-Max normalization to convert categorical data into numerical and averages to overcome imputation of empty data. The data grouping method uses K-Means Clustering. AHP is used to determine indicators that have most influence on clustering and ranking of Batik MSMEs. The research contribution is integration of K-Means with AHP and preprocessing techniques. The most optimal cluster evaluation technique uses SSE. Based on the test results, the optimal cluster is K=3, with an SSE value = 0.287, Cluster 1 (Low) = 28%, Cluster 2 (medium) = 33%, and cluster 3 (High) = 39%. The results of recommendations for four highest weighting criteria using AHP are number of customers 24%, employee training 18.8%, product branding 17%, market place 16.3%.

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The SME performance recommendation system facing the 4.0 industrial revolution uses the Fuzzy ANP method

Cited by 2 publications

References 13 publications

Implementation of Key Technologies for a Healthy Food Culture Recommendation System Using Internet of Things

Implementation of Key Technologies for a Healthy Food Culture Recommendation System Using Internet of Things

Integration K-Means clustering and AHP for recommendations batik MSMEs

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