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All Journal JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Berdaya Mandiri International Journal of Hydrological and Environmental for Sustainability
Iwan Tri Riyadi Yanto, Iwan Tri Riyadi
Universitas Ahmad Dahlan
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Journal : JOIV : International Journal on Informatics Visualization

 1 
Visualization of Data Inventory Using Visual Data Mining (VDM) and Exploratory Data Analysis (EDA) Methods Yanto, Iwan Tri Riyadi; Handayani, Ossie Purwanti
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4075

Abstract

Naavagreen Sriwijaya Skincare Clinic in Semarang encountered difficulties in interpreting inventory data, which led to operational inefficiencies, stock imbalances, and potential sales losses. To address this issue, we aim to transform raw data into comprehensible visual insights for better decision-making. The study employed Visual Data Mining (VDM) and Exploratory Data Analysis (EDA) methods using Tableau software to visualize and analyze inventory records from January 2019 to December 2020. The methods were implemented in three main phases, consisting of project planning, data preparation, and data analysis. In the project planning phase, we conducted justification and a project plan, and identified the top business question. In the data preparation phase, we choose, transform, and verify the dataset. In the data analysis phase, we chose visualization or mining tools, analyzed the visualization or mining model, and verified and presented the visualization or mining model. The results indicated that among ninety-eight products, three were identified as efficient and three as inefficient based on their stock and sales behavior. Product visualizations showed distinct inventory patterns, while sales turnover lacked consistent trends, with the highest increase occurring in January 2020 at 12.86%. The visualizations were reviewed and validated by the clinic’s administrative team, demonstrating their practical value in supporting inventory management improvements. The efficiency dashboard indicates that Ng Facial Wash, Ng Skin Toner, and Ng Moisturizing Sunscreen 1 are deemed inefficient due to the imbalance between sales and incoming stock. Conversely, the top three most efficient products are Ng-Neher Pagi, Ng Badan Pagi, and Naavagreen Moist Aha Cream. This analysis aids in making informed decisions regarding stock management and future sales strategies.
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.
Laying Chicken Algorithm (LCA) Based For Clustering Yanto, Iwan Tri Riyadi; Setiyowati, Ririn; Irsalinda, Nursyiva; Rasyidah, -; Lestari, Tri
JOIV : International Journal on Informatics Visualization Vol 4, No 4 (2020)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.4.467

Abstract

Numerous research and related applications of fuzzy clustering are still interesting and important. In this paper, Fuzzy C-Means (FCM) and Laying Chicken Algorithm (LCA) were modified to improve local optimum of Fuzzy Clustering presented by using UCI dataset. In this study, the proposed FCMLCA performance was also compared to baseline technique based on CSO methods. The simulation results indicate that the FCMLCA method have better performance than the compared methods.
A Framework of Mutual Information Kullback-Leibler Divergence based for Clustering Categorical Data Yanto, Iwan Tri Riyadi; Setiyowati, Ririn; Azizah, Nur; Rasyidah, -
JOIV : International Journal on Informatics Visualization Vol 5, No 1 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.1.462

Abstract

Clustering is a process of grouping a set of objects into multiple clusters, so that the collection of similar objects will be grouped into the same cluster and dissimilar objects will be grouped into other clusters. Fuzzy k-means Algorithm is one of clustering algorithm by partitioning data into k clusters employing Euclidean distance as a distance function. This research discusses clustering categorical data using Fuzzy k-Means Kullback-Leibler Divergence. In the determination of the distance between data and center of cluster uses mutual information known as Kullback-Leibler Divergence distance between the joint distribution and the product distribution from two marginal distributions. Extensive theoretical analysis was performed to show the effectiveness of the proposed method. Moreover, the proposed method's comparison results with Fuzzy Centroid and Fuzzy k-Partition approaches in terms of response time and clustering accuracy were also performed employing several datasets from UCI Machine Learning. The experiment results show that the proposed Algorithm provides good results both from clustering quality and accuracy for clustering categorical data as compared to Fuzzy Centroid and Fuzzy k-Partition.
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, - Ani Apriani Ani Apriani, Ani Cengiz, Korhan Danardono, D Eko Handoyo Hamzah, Romelan Handayani, Ossie Purwanti Haryatmi, Sri Imam Riadi Irsalinda, Nursyiva Mustafa Mat Deris, Mustafa Nur Azizah Ozyurt, Basak Rahmat Hidayat Rasyidah, - Rizqi Prastowo Sely Novita Sari Senan, Norhalina Septiana Fathurrohmah Setiyowati, Ririn Sumargiyani Sumargiyani Topac, Tuna Tri Lestari Wahyudi, Rofiul WaiShiang, Cheah