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All Journal International Journal of Advances in Applied Sciences Journal of Computing and Informatics Research
Dedin Fathudin
Universitas Pamulang
Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Confirmatory factor analysis: model testing of financial ratios with decision support systems approach T. Husain; Maulana Ardhiansyah; Dedin Fathudin
International Journal of Advances in Applied Sciences Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (373.461 KB) | DOI: 10.11591/ijaas.v10.i2.pp115-121

Abstract

The decision support systems approach can be developed into both computer-based and quantitative analysis tools. This research uses a model test with a confirmatory factor analysis (CFA) technique on matrix covariance against structural equation modelling (SEM) methods to measure financial ratios. Decision support system (DSS) analysis uses numerical calculations aided by mathematical models through six phases. The first three phases of a structured approach to building multivariate models and the next three phases, namely estimation, interpretation, and validation, are developing from data input that has been selected using LISREL version 8.72. The financial ratio’s testing model with a CFA approach derived into a mathematical (quantitative) model can explain the complexity of the relationship between the goodness-of-fit models (GOF) with a different approach from prior research. The goodness-of-fit test results in this study produced scores on each of the financial ratio measurement models at an accuracy level of CR of 78.49, TATO of 1.26, DER of 41.41, ROA of minus 0.033, and PBV of 540.92. This means that PBV has the highest standardized loading factors to determine the measurement of financial ratios. The CFA measurement based on SEM can be used to make appropriate decisions and combine a model comparison and redevelopment of the CFA technique and model testing with other software such as SPSS, PLS, AMOS, and others.
Utilizing K-Means Clustering to Understanding Audience Interest in SEO-Optimized Media Content Erlin Windia Ambarsari; Dedin Fathudin; Gravita Alfiani
Journal of Computing and Informatics Research Vol 3 No 2 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i2.1207

Abstract

This study observes k-means clustering for segmenting SEO data to understand audience interests, identifying the elbow method as crucial for determining the optimal number of clusters. It highlights notable differences in content engagement across clusters, emphasizing the need for refined SEO strategies and a deeper understanding of audience segmentation. Despite challenges like SEO's dynamic nature and data reliance, this methodology provides a strong foundation for enhancing content strategies. Future research suggestions include cross-platform data integration, longitudinal studies, sentiment analysis, content experimentation, user experience (UX) focus, and monitoring algorithm updates to develop more adaptive content and SEO strategies aligned with changing audience behaviors.
Co-Authors Erlin Windia Ambarsari Gravita Alfiani Maulana Ardhiansyah T. Husain