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Sakinah, Awit
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Mathematics

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Partial Least Square Second Order dengan Pendekatan Two Stage untuk Mengukur Tingkat Adopsi Digital UMKM Sakinah, Awit; Listiani, Lina; Agustina, Nova
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4266

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Tasikmalaya have a relatively low rate of technology adoption, particularly in e-commerce. Initial survey results show that 66% of MSMEs supported by the Tasikmalaya Kamar Dagang Industri (KADIN) have not been able to leverage e-commerce adoption. This limitation significantly impacts the growth of MSMEs and their ability to compete in a market increasingly integrated with digital technology. This study aims to analyze the factors influencing the adoption of e-commerce by MSMEs in Tasikmalaya using the Technology Acceptance Model (TAM). The analysis method employs Partial Least Square (PLS) Second Order, as the variables in TAM are multidimensional. The second-order approach selected is the two-stage method, which can minimize residuals of correlated indicators. Data were collected using simple random sampling with a rule-of-thumb sample size of 145 MSMEs. The study results indicate a significant influence of self-efficacy on perceived usefulness and perceived ease of use. There is also a significant influence of perceived ease of use on perceived usefulness. Additionally, perceived usefulness and perceived ease of use significantly and positively influence attitudes toward e-commerce adoption among MSMEs, with a total influence of 87.2%. Furthermore, there is a significant influence of perceived usefulness and attitude towards e-commerce adoption on intention to adopt e-commerce by 91.5%. The intention to use e-commerce significantly affects actual use (adoption) by 91.1%. This indicates that the more positive the perception of usefulness and ease of technology, the higher the intention and actual use of e-commerce technology by MSMEs. To increase e-commerce adoption among Tasikmalaya MSMEs, training on user-friendly e-commerce platforms and intensive mentoring is needed to enhance the self-efficacy of MSME actors in utilizing technology.
Optimization E-Commerce Consumer Segmentation Based On K-Means Clustering And Machine Learning Sakinah, Awit; Awaliyah, Dewi Syifa
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9548

Abstract

The rapid growth of e-commerce in Indonesia has driven the need for more targeted marketing strategies. Consumer segmentation is an effective approach to understanding purchasing behavior. This study implements the K-Means Clustering algorithm, an unsupervised machine learning method, to perform consumer segmentation based on e-commerce product data. The dataset was obtained from the Kaggle platform, with key features including product ratings, prices, and sales volume. The number of clusters is determined automatically using the Silhouette Score method to achieve optimal segmentation. The segmentation results are visualized through a web-based application using Streamlit, allowing users to easily explore the characteristics of each cluster. Each cluster is analyzed to provide insights into consumer behavior and potential marketing strategies. This study demonstrates that a data-driven approach using machine learning can be effectively applied to support business decision-making in the e-commerce domain
Co-Authors Agustina, Nova Awaliyah, Dewi Syifa Listiani, Lina