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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Applied Data Sciences Journal of Software Engineering and Multimedia (JASMED)
Martryatus Sofia
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Frontend Design of Spatial Working Memory Test Application for Junior and Vocational High School Students Using the Waterfall Method Martryatus Sofia; Rifki Adhitama
Journal of Software Engineering and Multimedia (JASMED) Vol. 4 No. 1 (2026): Journal of Software Engineering and Multimedia (JASMED)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/jasmed.v4i1.11245

Abstract

Spatial Working Memory (SWM) is an essential cognitive function that supports learning processes, reasoning, mathematical abilities, and academic achievement among students. Despite its importance, accessible and digital-based tools for assessing spatial working memory remain limited, particularly for junior and vocational high school students. This study focuses on designing and developing the frontend of a web-based SWMT application, guided by the Waterfall development approach involving literature studies, interviews with psychology experts, observations, questionnaires, interface design using Figma, implementation with Laravel, HTML, and CSS, and usability and accessibility evaluations. Accessibility testing was conducted using the WebAIM Contrast Checker based on Web Content Accessibility Guidelines (WCAG), while usability assessment was carried out through the SUS instrument with 72 participants from junior and vocational high school levels. The results indicate that seven out of eleven evaluated interface elements successfully met WCAG 2.0 Level AA accessibility standards, with contrast ratios exceeding the minimum requirement of 4.5:1. Four elements, namely the login form and login button, the independent test start button, the test score display, and the PDF download button, require further improvement due to insufficient contrast levels. Usability evaluation produced an average SUS score of 66.15, categorized as "OK" with marginal acceptance, indicating that the application can be used independently while still requiring improvements in navigation consistency, instruction clarity, and overall user experience. This study offers a meaningful contribution toward building an open and inclusive digital tool for cognitive assessment in Indonesian educational settings and provides a foundation for future integration of backend functionality and psychometric validation.
Dimension-Expanding MLP in Transformer: Inappropriate Sentences and Paragraph Digital Content Filtering Ariq Cahya Wardhana; Andi Prademon Yunus; Rifki Adhitama; Muhammad Abdul Latief; Martryatus Sofia
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.627

Abstract

The creation of digital content is now a pivotal element of today’s digital environment, driven by the need for both individuals and organizations to engage audiences effectively. As digital platforms grow in scope and impact, ensuring the security, professionalism, and appropriateness of user-generated content has become crucial. This study introduces a new approach for filtering inappropriate digital content by integrating dimension-expanding multi-layer perceptions (MLPs) into transformer architectures. The dimension-expanding MLP processed more high-dimensional features in the Transformers network, giving the ability to understand more specific contexts. Experimental findings reveal that the proposed model outperforms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), Transformer (Baseline) in accuracy, computational efficiency, and scalability. The research highlights the model’s practical applications in areas like social media content moderation, legal document compliance monitoring, and filtering harmful content in e-learning and gaming platforms with 0.744 accuracy.
Co-Authors Andi Prademon Yunus Ariq Cahya Wardhana Muhammad Abdul Latief Rifki Adhitama