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All Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat JLBG (Jurnal Lingkungan dan Bencana Geologi) (Journal of Environment and Geological Hazards) JOIV : International Journal on Informatics Visualization Angkasa: Jurnal Ilmiah Bidang Teknologi MIDA : Jurnal Pendidikan Dasar Islam Jurnal Loyalitas Sosial: Journal of Community Service in Humanities and Social Sciences National Conference on Applied Business, Education & Technology (NCABET) Journal of Artificial Intelligence and Digital Business Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII International Journal of Advanced Science Computing and Engineering Kreativitas Pada Pengabdian Masyarakat (Krepa)
Ani Apriani, Ani
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Journal : JOIV : International Journal on Informatics Visualization

 1 
Fuzzy Soft Set Clustering for Categorical Data Yanto, Iwan Tri Riyadi; Apriani, Ani; Wahyudi, Rofiul; WaiShiang, Cheah; Suprihatin, -; Hidayat, Rahmat
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2364

Abstract

Categorical data clustering is difficult because categorical data lacks natural order and can comprise groups of data only related to specific dimensions. Conventional clustering, such as k-means, cannot be openly used to categorical data. Numerous categorical data using clustering algorithms, for instance, fuzzy k-modes and their enhancements, have been developed to overcome this issue. However, these approaches continue to create clusters with low Purity and weak intra-similarity. Furthermore, transforming category attributes to binary values might be computationally costly. This research provides categorical data with fuzzy clustering technique due to soft set theory and multinomial distribution. The experiment showed that the approach proposed signifies better performance in purity, rank index, and response times by up to 97.53%. There are many algorithms that can be used to solve the challenge of grouping fuzzy-based categorical data. However, these techniques do not always result in improved cluster purity or faster reaction times. As a solution, it is suggested to use hard categorical data clustering through multinomial distribution. This involves producing a multi-soft set by using a rotated based soft set, and then clustering the data using a multivariate multinomial distribution. The comparison of this innovative technique with the established baseline algorithms demonstrates that the suggested approach excels in terms of purity, rank index, and response times, achieving improvements of up to ninety-seven-point fifty three percent compared to existing methods.
Fast Clustering Environment Impact using Multi Soft Set Based on Multivariate Distribution Yanto, Iwan Tri Riyadi; Apriani, Ani; Hidayat, Rahmat; Mat Deris, Mustafa; Senan, Norhalina
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.628

Abstract

Every development activity is always related to human or community aspects. This can also lead to changes in the characteristics of the community. The community's increasing awareness and critical attitude need to be accommodated to avoid the emergence of social conflicts in the future. This research is to find out how the public perception about the impact of development on the environment. Two methods are used, i.e., MDA (Maximum Dependency Attribute) and MSMD (the Multi soft set multivariate distribution function). The MDA is to determine the most influential attribute and the Multi soft set multivariate distribution function (MSMD) is to group the selected data into classes with similar characteristics. This will help the police producer plan the right mediation and take quick activity to make strides in the quality of the social environment. The experiment conducted level of impact based on the clustering results with the greatest number of member clusters is cluster 1 (very low impact) with 32.25 % of total data following cluster 5 (Very High impact) with 24.25 % of total data. The experiment obtains the level of impact based on the clustering results. The greatest number of member clusters is cluster 1 (extremely low impact) with 32.25 % of total data following cluster 5 (Very High impact) with 24.25 % of total data. The scatter area impact is spread at districts 6, 7, 10, 11, the most of very high impact and districts 1,2,3,4,5,8 the lowest impact. 
Co-Authors - Suprihatin, - Alfariji, Moh. Alfat, Nisa Amalia Ayu Candra Kurniati Bayurohman Pangacella Putra Cahyo, Gilang Ramadhan Dwi Chalista, Angel Ribka Dewi, Mina Wati Fahrurrozi, Muhammad Ikrom Fatimah fatimah Fatimah Gulo, Yanto Nius Habib, Juhair Al Hentihu, Shintia Pratiwi Iwan Tri Riyadi Yanto, Iwan Tri Riyadi Marsyom, Insren Loisa Mustafa Mat Deris, Mustafa Pamungkas, Giantoro Paramitha Tedja Trisnaning Rahmat Hidayat Revalina, Suci Rizky Eka Putri, Rizky Eka Rufiatun, Yayuk Sabila, Laily Rahma Senan, Norhalina Sumargiyani Sumargiyani Sumargiyani Triana, Harry Wahyudi, Rofiul WaiShiang, Cheah