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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika G-Tech : Jurnal Teknologi Terapan
Marlinda Sanglise
University of Papua, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Designing UI/UX Thrift shop Website Using User Centered Design (UCD) Method (Case Study: Backfold Market) Bachtiar Firmansyah; Dedi I. Inan; Ratna Juita; Marlinda Sanglise
G-Tech: Jurnal Teknologi Terapan Vol 8 No 2 (2024): G-Tech, Vol. 8 No. 2 April 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i2.4040

Abstract

Backfold Market, a business currently relying on conventional sales and social media, recognizes the need for technological advancement. To address this, they aim to establish a sales platform in the form of a website. This website will serve as a marketplace for second-brand clothes from renowned brands. The user centered design (UCD) method guides the development of the user interface and user experience. By focusing on users at every stage, the UCD method ensures that each design aligns with user needs. Through interviews identifies user goals and desires, translating them into requirements and features. The resulting designs and prototypes are evaluated using the Maze.design application, achieving an impressive average usability score of 97. This score indicates that the website’s interface design is user-friendly and easy to navigate for its audience.
Comparison of Naive Bayes and Support Vector Machine for Sentiment Analysis of BPJS Health Service Deactivation Vitriayanti Payung Allo; Marlinda Sanglise; Julius Panda Putra Naibaho
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16261

Abstract

The deactivation of BPJS Health services has become a topic of public discussion on social media, particularly on platform X, where users frequently express opinions regarding healthcare access and membership status. This study aims to analyze public sentiment toward the deactivation of BPJS Health services and compare the performance of Naive Bayes and Support Vector Machine (SVM) for sentiment classification. The dataset consisted of 8,357 tweets collected from social media X, of which 7,546 tweets were retained after preprocessing, including data cleaning, case folding, tokenizing, stopword removal, and stemming. TF-IDF and FastText were employed as text representation techniques, while model evaluation was conducted using 5-Fold Cross Validation, Grid Search Cross Validation for hyperparameter optimization, and a paired t-test for statistical significance analysis. Classification performance was measured using accuracy, precision, recall, and F1-score metrics. The results showed that negative sentiment dominated public opinion, accounting for 70.63% of the dataset, followed by neutral sentiment (26.48%) and positive sentiment (2.89%). The SVM model with TF-IDF achieved the highest performance, with an accuracy of 80.97%, precision of 80.16%, recall of 80.97%, and F1-score of 79.70%, outperforming Naive Bayes with TF-IDF (79.01%), Naive Bayes with FastText (64.26%), and SVM with FastText (80.31%). Furthermore, a paired t-test confirmed that the performance difference between Naive Bayes and SVM was statistically significant (p = 0.011). These findings indicate that SVM combined with TF-IDF is more effective for sentiment classification of high-dimensional social media text data and provide empirical evidence regarding the effectiveness of different text representation approaches for healthcare policy-related sentiment analysis.  
Co-Authors Bachtiar Firmansyah Dedi I. Inan Julius Panda Putra Naibaho Ratna Juita Vitriayanti Payung Allo