Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal East Asian Journal of Multidisciplinary Research (EAJMR) Immortalis Journal of Interdisciplinary Studies
Umi Hayati
STMIK IKMI Cirebon, Indonesia
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementation of Fuzzy C-Means Algorithm with Optimized Parameter Grid for Clustering Electronic Product Sales Rini Astuti; Nining Rahaningsih; Umi Hayati; Cep Lukman Rohmat; Nana Suarna
East Asian Journal of Multidisciplinary Research Vol. 2 No. 4 (2023): April 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/eajmr.v2i4.3929

Abstract

The sales of electronic products have increased rapidly over the past few years. However, grouping products based on certain criteria is still an unresolved issue. Therefore, research is needed to develop more accurate clustering methods. Currently, the problem with electronic product clustering using the k-means method still has limitations, such as sensitivity to initial centroid values and inability to handle overlap between clusters. Therefore, research is needed to optimize the grid parameter of the Fuzzy C-Means algorithm to produce more accurate clustering. The purpose of this study is to implement the Fuzzy C-Means algorithm with optimized grid parameters to cluster electronic product sales more accurately. The method used in this study is an experimental research method. Electronic product sales data were obtained from specific stores, and the Fuzzy C-Means algorithm with optimized grid parameters was applied to cluster electronic products. The results show that implementing the Fuzzy C-Means algorithm with optimized grid parameters can produce more accurate electronic product clustering compared to the k-means method. By using optimized grid parameters, the Fuzzy C-Means algorithm can handle overlap between clusters and produce more stable centroids with a Dbi accuracy value of 0.510 for Numerical Measure and 0.611 for Mixed Measure.
Transforming Information Systems into Intelligent Socio-Technical Ecosystems via Machine Learning and Explainable AI: A Systematic Review Misti Jahrani Putri; Martanto Martanto; Fathurrohman Fathurrohman; Edi Tohidi; Umi Hayati
Immortalis Journal of Interdisciplinary Studies Vol. 2 No. 3 (2026): July - September
Publisher : PT. Caesarindo Triloka Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67307/ijis.v2i3.128

Abstract

Rapid developments in Machine Learning (ML) and Data Analytics (DA) are reshaping Information Systems (IS) from transactional tools into intelligent, socio-technical ecosystems. However, existing literature remains fragmented between purely algorithmic focus and organizational adoption. This Systematic Literature Review (SLR) synthesizes 48 high-impact Scopus-indexed studies published between 2020 and 2026, following the PRISMA 2020 framework, to map the architectural and organizational integration of ML and DA in IS transformation. Our findings reveal a paradigm shift across five dominant thematic clusters: predictive forecasting, intelligent automation, big data integration, Explainable AI (XAI), and Human-AI collaboration. Rather than pure automation, the literature strongly underscores a transition toward Human-Centered AI, where model interpretability and socio-technical governance are critical to user trust and system performance. Furthermore, we identify core deployment bottlenecks—specifically regarding algorithmic transparency, cross-system interoperability, data privacy, and ethical governance. This study contributes a novel conceptual framework illustrating the interplay between technical ML capabilities, cross-cutting enablers, and organizational value creation, offering actionable guidelines for designing sustainable, transparent, and adaptive AI-driven IS.
Co-Authors Cep Lukman Rohmat Edi Tohidi Fathurrohman Fathurrohman Martanto Martanto Misti Jahrani Putri Nana Suarna Nining Rahaningsih Rini Astuti