Data Clustering for Identification of Building Conditions Using Hybrid Multivariate Multinominal Distribution Soft Set (MMDS) Method
Identifying building conditions for user safety is an urgent matter, especially in earthquake-prone areas. Clustering buildings according to their conditions in the categories of danger, vulnerable, normal, and safe is important information for residents and the government to take further action. Th...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Padang
2022-06-01
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| Series: | JOIV: International Journal on Informatics Visualization |
| Subjects: | |
| Online Access: | https://joiv.org/index.php/joiv/article/view/986 |
| _version_ | 1811159834364477440 |
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| author | Rohmat Saedudin Iwan Tri Riyadi Yanto Avon Budiono Sely Novita Sari Mustafa Mat Deris Norhalina Senan |
| author_facet | Rohmat Saedudin Iwan Tri Riyadi Yanto Avon Budiono Sely Novita Sari Mustafa Mat Deris Norhalina Senan |
| author_sort | Rohmat Saedudin |
| collection | DOAJ |
| description | Identifying building conditions for user safety is an urgent matter, especially in earthquake-prone areas. Clustering buildings according to their conditions in the categories of danger, vulnerable, normal, and safe is important information for residents and the government to take further action. This study introduces a new method, namely hybrid multivariate multinomial distribution with the softest (MMDS) in working on the process of clustering building conditions into the most appropriate category and comparable to the condition data presented in the building data set. Research using the MMDS method is very important to map the condition of existing buildings in an area supported by available data sets. The results of the measurements carried out can provide information related to the building index and were clustered based on the index value of the condition of the building. The dataset used in this study is data on school buildings in the West Java region. There are 286 school building data with four condition parameters: foundation, concrete reinforcement, easel pole, and roof. From existing data and defined condition parameters, buildings can be classified accurately and in proportion to the facts on the ground. This study also compared the proposed method, MMDS, with the baseline method, namely Fuzzy Centroid Clustering (FCC) and Fuzzy k-means Clustering (FKC). The results show that the proposed method is superior to the baseline method with a faster processing time |
| first_indexed | 2024-04-10T05:47:38Z |
| format | Article |
| id | doaj.art-8824babfa191471d9fad533fcff62d84 |
| institution | Directory Open Access Journal |
| issn | 2549-9610 2549-9904 |
| language | English |
| last_indexed | 2024-04-10T05:47:38Z |
| publishDate | 2022-06-01 |
| publisher | Politeknik Negeri Padang |
| record_format | Article |
| series | JOIV: International Journal on Informatics Visualization |
| spelling | doaj.art-8824babfa191471d9fad533fcff62d842023-03-05T10:28:41ZengPoliteknik Negeri PadangJOIV: International Journal on Informatics Visualization2549-96102549-99042022-06-016228428910.30630/joiv.6.2.986359Data Clustering for Identification of Building Conditions Using Hybrid Multivariate Multinominal Distribution Soft Set (MMDS) MethodRohmat Saedudin0Iwan Tri Riyadi Yanto1Avon Budiono2Sely Novita Sari3Mustafa Mat Deris4Norhalina Senan5Department of Information Systems, Telkom University, Bandung, West Java, IndonesiaDepartment of Information Systems, Universitas Ahmad Dahlan, IndonesiaDepartment of Information Systems, Telkom University, Bandung, West Java, IndonesiaInstitute Teknologi Nasional Yogyakarta, IndonesiaUniversiti Tun Hussein Onn Malaysia, Johor, MalaysiaUniversiti Tun Hussein Onn Malaysia, Johor, MalaysiaIdentifying building conditions for user safety is an urgent matter, especially in earthquake-prone areas. Clustering buildings according to their conditions in the categories of danger, vulnerable, normal, and safe is important information for residents and the government to take further action. This study introduces a new method, namely hybrid multivariate multinomial distribution with the softest (MMDS) in working on the process of clustering building conditions into the most appropriate category and comparable to the condition data presented in the building data set. Research using the MMDS method is very important to map the condition of existing buildings in an area supported by available data sets. The results of the measurements carried out can provide information related to the building index and were clustered based on the index value of the condition of the building. The dataset used in this study is data on school buildings in the West Java region. There are 286 school building data with four condition parameters: foundation, concrete reinforcement, easel pole, and roof. From existing data and defined condition parameters, buildings can be classified accurately and in proportion to the facts on the ground. This study also compared the proposed method, MMDS, with the baseline method, namely Fuzzy Centroid Clustering (FCC) and Fuzzy k-means Clustering (FKC). The results show that the proposed method is superior to the baseline method with a faster processing timehttps://joiv.org/index.php/joiv/article/view/986clusteringsoft setmultivariate multinomial distribution |
| spellingShingle | Rohmat Saedudin Iwan Tri Riyadi Yanto Avon Budiono Sely Novita Sari Mustafa Mat Deris Norhalina Senan Data Clustering for Identification of Building Conditions Using Hybrid Multivariate Multinominal Distribution Soft Set (MMDS) Method JOIV: International Journal on Informatics Visualization clustering soft set multivariate multinomial distribution |
| title | Data Clustering for Identification of Building Conditions Using Hybrid Multivariate Multinominal Distribution Soft Set (MMDS) Method |
| title_full | Data Clustering for Identification of Building Conditions Using Hybrid Multivariate Multinominal Distribution Soft Set (MMDS) Method |
| title_fullStr | Data Clustering for Identification of Building Conditions Using Hybrid Multivariate Multinominal Distribution Soft Set (MMDS) Method |
| title_full_unstemmed | Data Clustering for Identification of Building Conditions Using Hybrid Multivariate Multinominal Distribution Soft Set (MMDS) Method |
| title_short | Data Clustering for Identification of Building Conditions Using Hybrid Multivariate Multinominal Distribution Soft Set (MMDS) Method |
| title_sort | data clustering for identification of building conditions using hybrid multivariate multinominal distribution soft set mmds method |
| topic | clustering soft set multivariate multinomial distribution |
| url | https://joiv.org/index.php/joiv/article/view/986 |
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