Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Sistem Informasi dan Informatika (SIMIKA) International Journal Software Engineering and Computer Science (IJSECS)
Suwarno
International University of Batam
Computer Science & IT Control & Systems Engineering

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

Found 2 Documents
Search

 1 
Analysis Acceptance of XYZ Company Digital Membership Using Technology Acceptance Model Skynyrd; Suwarno
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3496

Abstract

The current study explores the acceptance of XYZ Company’s digital membership program in the light of the Technology Acceptance Model (TAM). The objective is to find some key values that predict user adoption, focusing mainly on the predictors of Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) in behavioral intention (BI). Data was collected from 378 respondents and Structural Equation Modeling (SEM) was used on the data obtained. The data provides evidence for the significant effects of PU and PEOU on BI as expected, underlining how important these constructions are to influence user acceptance. The study extends the TAM with other variables such as Trust, data privacy, and user experience (UX) to provide a broader understanding. These variables are important, especially in the case of a digital membership program operated by a company, for the technological requirements that need to satisfy multiple customers and match those needs with industry constraints. The study findings reveal a significant mediating effect of UX on the relationship between PEOU and PU, as well as a moderating impact of trust and data privacy on the relationship between PEOU and PU, which in turn creates a more satisfying level of assurance and satisfaction. While the findings inform, in a very specific way what Company XYZ can do from a management perspective to improve the user experience and enhance the benefits of its digital membership program and user adoption/engagement. In line with the academic literature, this study places TAM into the context of corporate digital membership offering relevant knowledge and practical recommendations for organizations to replicate similar initiatives. The strategic implications of these results demonstrate the importance for companies to design their digital solutions according to user expectations and needs, to consistently deliver optimal customer value and satisfaction in a highly competitive sales environment.
MACHINE LEARNING-BASED ASSESSMENT OF SKINCARE PRODUCT VALUE FOR MONEY USING CUSTOMER REVIEWS Suwarno; Deli; Devina Benhans
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4620

Abstract

Skincare products play an important role in personal care and consumer satisfaction. However, the wide variety of products and price ranges often makes it difficult for consumers to assess whether a product offers good Value for Money. This study proposes a machine learning approach to evaluate the Value for Money of skincare products using customer reviews from the Sephora platform. Review texts were preprocessed using Natural Language Processing (NLP) techniques, including lowercasing, tokenization, stopword removal, and lemmatization. Text features were extracted with TF-IDF and combined with product price, helpfulness score, and sentiment score. Support Vector Machine (SVM) and Logistic Regression were compared for classification, while SMOTE was applied to address class imbalance. Both models achieved similar performance, with an accuracy of 0.76, a macro-average F1-score of 0.70, and a weighted-average F1-score of 0.78. The results indicate that combining customer reviews with product-related features can effectively assess Value for Money and support consumers in making better purchasing decisions.
Co-Authors Deli Devina Benhans Skynyrd