Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Informatics
Yadi Utama
Sriwijaya University
Computer Science & IT

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Evaluating Pinterest User Experience and Usability Using AttrakDiff and PLS-SEM Septhia Charenda Putri; Ali Ibrahim; Yadi Utama; Endang Lestari Ruskan; Fathoni Fathoni
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1408

Abstract

The rapid development of visual platforms such as Pinterest necessitates a comprehensive understanding of how functional and emotional aspects jointly influence users’ perception and engagement. This research addresses the gap in user experience (UX) evaluation of visually rich applications by examining the effects of Pragmatic Quality, Hedonic Quality-Stimulation, and Hedonic Quality Identity on the perceived Attractiveness of the Pinterest application. A quantitative approach was employed using the 28-item AttrakDiff instrument, based on data collected from a final sample of 524 valid respondents, predominantly aged 18–25 years, and using Pinterest several times a week. The data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the aid of SmartPLS to examine the relationships among latent variables. The findings demonstrate that the structural model exhibits a high level of explanatory capability, with an R² value of 0.684. With all three UX dimensions exerting positive and statistically significant effects on Attractiveness. PQ shows the strongest influence (path coefficient = 0.457), followed by HQS (0.391) and HQI (0.112). These findings confirm that functional usability remains the primary driver of attractiveness on Pinterest, while hedonic qualities play a complementary role in enhancing user experience. Practically, this research suggests that designers and developers of visual platforms should prioritize efficient functionality while maintaining stimulating and identity-supporting elements to improve overall user appeal.
ROI-Based Shape-Prior Reconstruction for YOLOv8n-seg-Based Fetal Cerebellum Ultrasound Segmentation Yadi Utama; Erwin; Samsuryadi
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1669

Abstract

Fetal cerebellum segmentation in ultrasound images is important for quantitative analysis of fetal brain development, yet it remains challenging due to speckle noise, low contrast, acoustic artifacts, and unstable anatomical boundaries. This study proposes an ROI-Based Shape-Prior Reconstruction method as a post-processing refinement stage for YOLOv8n-seg fetal cerebellum segmentation. A total of 294 fetal ultrasound images with manually annotated binary cerebellum masks were used and divided into training, validation, and testing subsets using a 70:20:10 ratio. YOLOv8n-seg generated the initial segmentation masks, while the proposed ROI-based reconstruction stage refined the foreground region using a convolutional autoencoder trained on ROI-based binary cerebellum masks. Compared with raw YOLOv8n-seg, the proposed method improved DSC from 0.9282 to 0.9302 and IoU from 0.8671 to 0.8708. Boundary performance also improved, with HD95 decreasing from 15.06 to 14.18 and ASSD decreasing from 5.38 to 5.20. Although these improvements were modest and not statistically significant, the proposed method produced smoother boundaries and more morphologically consistent segmentation outputs in the visual evaluation. These results indicate that ROI-based shape-prior reconstruction can serve as a lightweight refinement stage for improving boundary consistency in fetal cerebellum ultrasound segmentation. However, external validation with larger datasets is still required to assess generalization.
Harnessing SVM for Sentiment Analysis: Insights from Gojek's Instagram Engagement Muhammad Juan Savero; Ali Ibrahim; Yadi Utama; Endang Lestari
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1041

Abstract

The development of digital technology has changed the transportation industry, including online services such as Gojek. Understanding customer sentiment is key in improving user experience and designing more effective business strategies. This research analyzes Gojek user sentiment on Instagram using Support Vector Machine (SVM). Data is obtained through web scraping, then processed through text cleaning, tokenization, common word removal, and stemming. Features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF) before being classified with SVM. The results showed that the SVM model achieved 70.82% accuracy in classifying user sentiment. Most positive comments highlight the convenience and efficiency of the service, while negative comments are more related to high tariffs, application constraints, and less responsive customer service. These findings provide insights for Gojek to improve marketing strategies, optimize customer service, and adjust fare policies based on user feedback. In addition, this analysis can help in predicting real-time customer satisfaction trends through sentiment monitoring on social media. As a development step, this research recommends further exploration with deep learning and Aspect-Based Sentiment Analysis (ABSA) to improve accuracy and understand the service aspects that have the most influence on customer satisfaction.
Taxpayer Classification Using K-Means Clustering to Support CRM Strategy Development: Case Study of Prabumulih City Samsat Bimmo Fathin Tammam; Ali Ibrahim; Dwi Rosa Indah; Ahmad Fali Oklilas; Yadi Utama
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1365

Abstract

Effective management of taxpayer data is crucial for enhancing compliance and optimizing regional revenue. This study addresses the limited use of data-driven taxpayer segmentation in local Samsat institutions by applying K-Means Clustering to support targeted Customer Relationship Management (CRM) strategies. A dataset of 3,999 motor vehicle taxpayer records from September 2025 was processed through feature selection, scaling, and clustering. The analysis identified three distinct taxpayer groups based on payment timeliness, compliance consistency, and vehicle age. Cluster validity was confirmed using the Davies-Bouldin Index, yielding a value of -41.327 for k = 3, supported by ANOVA for statistical significance. The findings highlight how clustering can reveal taxpayer behavior patterns, guiding personalized services and compliance programs. This study's novelty lies in integrating clustering outcomes with practical CRM strategies for public agencies, offering a data-driven approach to improve taxpayer engagement and regional revenue. However, the study is limited by its focus on a single-period dataset and vehicle-related attributes.
Co-Authors Ahmad Fali Oklilas Ali Ibrahim Bimmo Fathin Tammam Dwi Rosa Indah Endang Lestari Endang Lestari Ruskan Erwin Fathoni Fathoni Muhammad Juan Savero Samsuryadi Septhia Charenda Putri