Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal AL-ULUM: JURNAL SAINS DAN TEKNOLOGI
Avi Rudianita Indah Dg Widya
Politeknik Statistika STIS
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Energy

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
SPAN-LEVEL ASPECT-BASED SENTIMENT TRIPLET ANALYSIS IN GOVERNMENT APPLICATION REVIEWS Feza Raffa Arnanda; Lya Hulliyyatus Suadaa; Avi Rudianita Indah Dg Widya; Setia Irham Pramana
AL ULUM: JURNAL SAINS DAN TEKNOLOGI Vol 12, No 1 (2026)
Publisher : UPT Publication and Journal Management, Islamic University of Kalimantan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/jst.v12i1.22767

Abstract

The government is enhancing digital public services through mobile applications in line with the Electronic-Based Government System (SPBE) 2018–2025 vision. To support continuous innovation, the Ministry of Administrative and Bureaucratic Reform (Kemenpan-RB) organize the Public Service Innovation Competition (KIPP). Understanding user complaints is essential, and aspect-based sentiment analysis, particularly Span-Level Aspect Sentiment Triplet Extraction (Span-ASTE), was applied to analyze government app reviews. A domain-specific dataset was developed with a Cohen’s Kappa of 0.817, indicating strong annotation reliability. IndoBERT-large achieved the highest F1-score of 0.76, while IndoBERT-lite-base provided an efficient alternative with an F1-score of 0.727. An aspect categorization model reached 0.86 accuracy. These models aim to improve public services, strengthen SPBE implementation, and enhance Indonesia’s E-Government Development Index ranking.
Co-Authors Feza Raffa Arnanda Setia Irham Pramana Suadaa, Lya Hulliyyatus