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INDONESIA
Bulletin of Network Engineer and Informatics (BUFNETS)
Published by GWEX NET PUBLISHER
ISSN : 29874858     EISSN : 29868017     DOI : https://doi.org/10.59688/bufnets
Core Subject : Science,
Computer Science & IT
The Journal invites original articles and is not simultaneously submitted to another journal or conference. Scopes: Information Technology: Software Engineering, Knowledge and Data Mining, Multimedia Technologies, Mobile Computing, Parallel/Distributed Computing, Computer Graphics, Virtual Reality, Data and Cyber Security. Computer Network: Management and Protocol Network, Telecommunication Systems, Wireless Communications, Fuzzy Sensor and Network, Internet of Things, Data Communication and Networking.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 72 Documents
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AURA: ADAPTIVE UI RECOVERY ARCHITECTURE FOR ANDROID TEST AUTOMATION M Ilham Yusuf Gumai; Suhendro Yusuf Irianto; RZ Abdul Aziz; Rahmalia Syahputri
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/738290

Abstract

User interface (UI) test automation on Android frequently breaks when developers rename element attributes during refactoring, rendering previously valid locators unresolvable and imposing significant maintenance overhead. Existing self-healing approaches predominantly target web DOM and lack post-action validation, risking false healing where a wrong element is silently accepted. This study introduces AURA, a runtime self-healing layer for Appium-WebdriverIO that chains five deterministic recovery strategies, a widget-family post-action validator, and an optional machine-learning reranker. A controlled benchmark comprising 490 refactoring scenarios across five synthetic Android applications and six mutator types demonstrates that AURA achieves a 99.39% correct action rate with only 0.61% false-healing rate, significantly outperforming the adapted Similo baseline (95.71% / 4.29%) at p < 0.0001 (McNemar exact test). External validation on six production Google Android applications (130 scenarios) confirms a 100% correct rate with a bounds-IoU enhanced validator. Cache learning reduces per-find latency by 95.1% from the second session onward.
Evaluating the Limitations of English Lexicon-Based Sentiment Analysis for Indonesian E-Wallet Reviews: A Comparison of VADER and Indonesian RoBERTa Imam asrowardi; Septafiansyah Dwi Putra; Nadia Nabiha Dziqra
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/738289

Abstract

Sentiment analysis of mobile application reviews supports service evaluation in the fast-growing Indonesian digital finance sector. This study examined whether an English lexicon-based method remains reliable for Indonesian-language reviews by comparing VADER with a fine-tuned Indonesian RoBERTa model. A total of 1,000 user reviews of the DANA e-wallet application were collected from Google Play and preprocessed through case folding, removal of numbers and punctuation, tokenization, and Indonesian stopword removal. Both methods classified each review as positive, neutral, or negative. VADER labelled 879 reviews as neutral, 102 as positive, and 19 as negative, whereas the Indonesian RoBERTa model produced a more balanced distribution of 362 negative, 327 positive, and 311 neutral reviews. The inter-method agreement, measured by Cohen's kappa, was only 0.027, indicating almost no agreement beyond chance. The results showed that VADER systematically assigned neutral labels because most Indonesian words were absent from its English lexicon, while the transformer model captured sentiment far more effectively. The findings demonstrated that language-specific transformer models are essential for sentiment analysis of Indonesian application reviews and that English lexicon-based tools are unsuitable for this task.

Page 8 of 8 | Total Record : 72

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