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Journal : International Journal of Engineering Continuity

E-Government Maturity Model Based on SPBE at Department of Spatial Planning and Development Amalia, Endang; Hamdani, Dani; Nugraha, Ucu
International Journal of Engineering Continuity Vol. 3 No. 2 (2024): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v3i2.262

Abstract

Sistem Pemerintahan Berbasis Elektronik known as SPBE is a government administration that utilizes information and communication technology to provide services with the aim of realizing clean, effective, transparent, and accountable government governance, quality, and trustworthy public services. Because he role of ICT in supporting work processes and services in achieving the vision and mission of the Regional Government of West Java Province is very important, it is necessary to evaluate the use and implementation of ICT solutions for continuous improvement in all Regional Apparatus. In the department spatial planning and development, e-government maturity measurements are carried out in 4 domains (application, infrastructure, information security and IT governance and) by adopting a descriptive research method using questionnaire data with a scale of 5 likers. The results showed the evaluation of the maturity of the SPBE in department spatial planning and development obtained a “Very Poor” category with a total index value of 0.61. This research also provides recommendations and considerations in making decisions on how to take policies in implementing e-Government Implementation in the community development service to determine the development priority scale from the level of maturity.
Revealing Consumer Preferences in the Fashion Industry Using K-Means Clustering Sulianta, Feri; Ulfah, Khaerani; Amalia, Endang
International Journal of Engineering Continuity Vol. 3 No. 2 (2024): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v3i2.280

Abstract

The fashion industry, driven by rapidly shifting e-commerce trends and consumer preferences, demands precise data analysis to optimize marketing strategies and enhance customer satisfaction. This study utilizes data mining techniques, specifically K-Means Clustering and the Elbow Method, to reveal consumer preferences within a dataset of 1,000 fashion product sales records, which include attributes such as product ID, name, brand, category, price, rating, color, and size. By grouping data into distinct clusters based on price and rating preferences, the analysis uncovers four key consumer segments. The optimal number of clusters is confirmed using the WCSS (Within-Cluster Sum of Square) method. These insights offer valuable guidance for refining marketing strategies in the fashion industry. Future research should consider additional variables and employ advanced tools for deeper analysis.