Data Clustering for Identification of Building Conditions Using Hybrid Multivariate Multinominal Distribution Soft Set (MMDS) Method

Abstract

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

Modeling and Application of Credit Scoring Based on A Multi-Objective Approach to Debtor Data in PT. Bank Riau Kepri

“…The K-Nearest Neighbor algorithm is the standard prediction algorithm [23]. The k-NN algorithm is one of the classification algorithms in data mining that calculates the similarity of the object with the nearest (k) neighbor [24]. The steps to calculate the K-NN algorithm are as follows:…”

“… Collecting class Y labels (K-Nearest Neighbor Classification).  The calculated query instance value can be predicted using the K-Nearest Neighbor category, which is the majority [24].…”