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Table 5. Grouping of keywords in smart building articles

Table 5 Grouping of keywords in smart building articles

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Figure 1. Features of smart building [5]
Figure 1. Features of smart building [5]
Figure 2. KPMG creating the building of the future through internet of things (loT) technology [6]
Figure 2. KPMG creating the building of the future through internet of things (loT) technology [6]
The next stage is to filter documents that have been cited by other authors with the number of citations per document above 3 citations. This is to provide confidence that the article has been proven to be known and cited by other authors. From this filter, 122 documents were found. Furthermore, from 122 documents, an abstract filter was carried out according to the writing theme regarding components, roles, and trends, obtained 78. The last filter sorted out the suitability of 78 documents from full-text and obtained 56 documents to be reviewed. The number of selected papers as many as 56 are papers published in the Scopus database according to the filter carried out with the criteria previously mentioned and based on the year of publication between 2018 and 2021. The graph of published papers per year can be seen in Figure 5. In 2018 there were 14 articles, in 2019 there were 21 articles which was the highest number in 4 years, in 2020 there were 16 articles and in 2021 there were only 5 articles that met the screening criteria according to Figure 4. The number of articles in 2021 the number decreased drastically possibly. This is due to the COVID-19 pandemic conditions around the world, so that a lot of research on smart buildings is expected because they cannot freely collect data, are limited to conducting interviews with respondents and there are lots of buildings or properties for their activities during the COVID-19 pandemic.
The next stage is to filter documents that have been cited by other authors with the number of citations per document above 3 citations. This is to provide confidence that the article has been proven to be known and cited by other authors. From this filter, 122 documents were found. Furthermore, from 122 documents, an abstract filter was carried out according to the writing theme regarding components, roles, and trends, obtained 78. The last filter sorted out the suitability of 78 documents from full-text and obtained 56 documents to be reviewed. The number of selected papers as many as 56 are papers published in the Scopus database according to the filter carried out with the criteria previously mentioned and based on the year of publication between 2018 and 2021. The graph of published papers per year can be seen in Figure 5. In 2018 there were 14 articles, in 2019 there were 21 articles which was the highest number in 4 years, in 2020 there were 16 articles and in 2021 there were only 5 articles that met the screening criteria according to Figure 4. The number of articles in 2021 the number decreased drastically possibly. This is due to the COVID-19 pandemic conditions around the world, so that a lot of research on smart buildings is expected because they cannot freely collect data, are limited to conducting interviews with respondents and there are lots of buildings or properties for their activities during the COVID-19 pandemic.
Figure 5. Number of articles from 2018-2021
Figure 5. Number of articles from 2018-2021
Smart building trend, role and position: a systematic literature review (Prasetya Cahya Saputra) ne EE IIE lg EES sls aaeial diesen aaa aaa ee To see what components are contained in in smart building, the relationship between keywords is used and the keyword used is the author keyword contained in the 56 full-text documents reviewed. As a tool for mapping the keywords used in 56 full-text documents, the VOSviewer application [20] is used. This VOSviewer application can provide a view of the relationship between keywords from export files such as RIS and CSV. In this study, the file used is an imported file from the Scopus database in CSV format. Before the author keyword mapping process, improvements were made to combine several of the same keywords. In one article the author keyword wrote "internet of things" some wrote "IoT", the author combined if there were conditions such as to obtain keywords that were not duplicated.
Smart building trend, role and position: a systematic literature review (Prasetya Cahya Saputra) ne EE IIE lg EES sls aaeial diesen aaa aaa ee To see what components are contained in in smart building, the relationship between keywords is used and the keyword used is the author keyword contained in the 56 full-text documents reviewed. As a tool for mapping the keywords used in 56 full-text documents, the VOSviewer application [20] is used. This VOSviewer application can provide a view of the relationship between keywords from export files such as RIS and CSV. In this study, the file used is an imported file from the Scopus database in CSV format. Before the author keyword mapping process, improvements were made to combine several of the same keywords. In one article the author keyword wrote "internet of things" some wrote "IoT", the author combined if there were conditions such as to obtain keywords that were not duplicated.
Table 1. Keyword clustering using VOSviewer application The clustering of keywords above is based on the proximity between keywords. Keywords in the same color are closer together than keywords in different color clusters. Smart building is placed in cluster 5 with purple color, one cluster with building information management, demand response, and sensors. This provides information that smart buildings have various roles in the discussion on building information management, demand response, and sensors. In cluster 1 in red, there are the keywords heating, renewal energy and smart grid, meaning that there is a close relationship between the keywords in the discussion of heating, renewal energy and smart grid, which are closely related to artificial neural network (ANN), big data and machine learning. The green color represents cluster 2 in which some keywords play a role and are close together, namely building management system, deep learning, energy harvesting, fault-tolerant control, smart city.
Table 1. Keyword clustering using VOSviewer application The clustering of keywords above is based on the proximity between keywords. Keywords in the same color are closer together than keywords in different color clusters. Smart building is placed in cluster 5 with purple color, one cluster with building information management, demand response, and sensors. This provides information that smart buildings have various roles in the discussion on building information management, demand response, and sensors. In cluster 1 in red, there are the keywords heating, renewal energy and smart grid, meaning that there is a close relationship between the keywords in the discussion of heating, renewal energy and smart grid, which are closely related to artificial neural network (ANN), big data and machine learning. The green color represents cluster 2 in which some keywords play a role and are close together, namely building management system, deep learning, energy harvesting, fault-tolerant control, smart city.
Figure 7. The country where the smart building research is conducted 31 papers specifically mention the country where the research was carried out and 25 papers do not mention it, Figure 7. Of the 31 papers spread across 17 countries where smart building research was conducted, there are only 3 regions. Regions Europe and Central Asia, East Asia and Pacific and North America as ranked by the World Bank [76] most of them are countries with income levels at the “high income” and “upper middle income” levels and in Figure 8 it can be seen that there are 71% of countries that occupy the “high income” position and 23% of countries with “upper middle income” category and only 6% occupy the position as a country with the income category of "lower middle income". It means that the country where smart building is currently being researched is dominated by countries with a good economy and in terms of income, the country with a minimum level is "lower middle income".
Figure 7. The country where the smart building research is conducted 31 papers specifically mention the country where the research was carried out and 25 papers do not mention it, Figure 7. Of the 31 papers spread across 17 countries where smart building research was conducted, there are only 3 regions. Regions Europe and Central Asia, East Asia and Pacific and North America as ranked by the World Bank [76] most of them are countries with income levels at the “high income” and “upper middle income” levels and in Figure 8 it can be seen that there are 71% of countries that occupy the “high income” position and 23% of countries with “upper middle income” category and only 6% occupy the position as a country with the income category of "lower middle income". It means that the country where smart building is currently being researched is dominated by countries with a good economy and in terms of income, the country with a minimum level is "lower middle income".
‘igure 8. Category based on the income of the country where the smart building research is conducted
‘igure 8. Category based on the income of the country where the smart building research is conducted
Table 4. Smart building research locations by property type The grouping of research location data based on the type of property in Figure 9 displays information with 2 types of large groups, namely residential and commercial shown in Figure 8. Research using non- commercial properties as much as 41% and commercial properties as much as 59%. Residential properties consist of residential/apartment/family house has 6 papers and government building has 1 paper. As for commercial property, there are 3 types, namely hotels with | paper, campus/school building with 6 papers, and office building with 4 papers.
Table 4. Smart building research locations by property type The grouping of research location data based on the type of property in Figure 9 displays information with 2 types of large groups, namely residential and commercial shown in Figure 8. Research using non- commercial properties as much as 41% and commercial properties as much as 59%. Residential properties consist of residential/apartment/family house has 6 papers and government building has 1 paper. As for commercial property, there are 3 types, namely hotels with | paper, campus/school building with 6 papers, and office building with 4 papers.
Figure 9. Category based on property type where the smart building research is conducted From the number of articles per property type, a graph is made to be able to see the percentage of the smart building research location grouping. The results of the percentage per property type can be seen in Figure 9. Research with the location of commercial building as much as 65%, namely there are 11 articles from 17 articles and residential property as much as 35% or 6 articles from 17 articles which inform the location of the smart building research.
Figure 9. Category based on property type where the smart building research is conducted From the number of articles per property type, a graph is made to be able to see the percentage of the smart building research location grouping. The results of the percentage per property type can be seen in Figure 9. Research with the location of commercial building as much as 65%, namely there are 11 articles from 17 articles and residential property as much as 35% or 6 articles from 17 articles which inform the location of the smart building research.
Smart building trend, role and position: a systematic literature review (Prasetya Cahya Saputra)
Smart building trend, role and position: a systematic literature review (Prasetya Cahya Saputra)
Figure 10. Research discussion trends in smart building
Figure 10. Research discussion trends in smart building
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