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All Journal International Journal of Electrical and Computer Engineering EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal TAM (Technology Acceptance Model) INOVTEK Polbeng - Seri Informatika Jurnal Pengabdian kepada Masyarakat Nusantara Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Proceeding Applied Business and Engineering Conference Bulletin of Network Engineer and Informatics (BUFNETS) ROUTERS: Jurnal Sistem dan Teknologi Informasi Jurnal Ilmiah ESAI Jurnal INFOTEL Jurnal Informatika: Jurnal Pengembangan IT Jurnal TAM (Technology Acceptance Model)
Imam Asrowardi
Politeknik Negeri Lampung
Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : bulletin of network engineer and informatics bufnets

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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 compared an English lexicon-based method (VADER) with a fine-tuned Indonesian RoBERTa model on 1,000 reviews of the DANA e-wallet application collected from Google Play. Following best practice for transformer models, reviews were classified primarily from their original (raw) text, and a preprocessed variant was used to assess the effect of text cleaning. On raw text, VADER labelled 903 reviews as neutral, 78 as positive, and 19 as negative, whereas Indonesian RoBERTa produced 494 positive, 385 negative, and 121 neutral, a positive-leaning distribution consistent with the sample's mean rating of 4.06. Inter-method agreement was negligible (Cohen's kappa = 0.008). Removing stopwords and numbers before classification changed 30.2% of the RoBERTa labels and flattened its distribution, confirming that such preprocessing degrades the contextual input transformers rely on, while leaving VADER almost unchanged. Because no human-annotated gold standard was used, the reported figures reflect inter-method agreement rather than absolute accuracy against human labels. The findings show that VADER is unsuitable for Indonesian reviews and that language-specific transformers should be applied to unprocessed text.
Design of an Edge-Intelligence Early-Warning System for Waste Odor Using Decision-Tree Classification-Rule Extraction on an ESP32 Microcontroller Agiska Ria Supriyatna; Imam Asrowardi; Eko Subyantoro; Dzakwan Akbar Perdana Wijaya; Naufaldi Favian Archi; Zahara Ummu Abdillah
Bulletin of Network Engineer and Informatics Vol. 4 No. 2 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) October 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/fd5dmk21

Abstract

Uncontrolled organic waste releases volatile gases such as ammonia, hydrogen sulfide, and assorted volatile organic compounds that signal active decomposition and create both nuisance and health risk. Conventional odor monitoring relies on human olfaction or on laboratory instruments that are subjective, intermittent, or too costly for continuous deployment. This study presents a low-cost early-warning system that performs odor classification entirely at the edge on an ESP32 microcontroller. A gas sensor array (electronic nose) comprising six metal-oxide MQ sensors (MQ2, MQ3, MQ4, MQ8, MQ9, and MQ135) was used to acquire 184 labelled samples evenly split between a normal class and an odor-warning class. A Decision Tree classifier was trained, and the resulting decision logic was distilled into a compact set of human-readable threshold rules suitable for execution on a constrained microcontroller. Principal component analysis confirmed that the two odor states form clearly separable clusters (the first two components explain 89.7% of the variance). The extracted rule set attained 100% accuracy, precision, recall, and F1-score on a held-out test set, matching heavier models (random forest, support-vector machine, k-nearest neighbours) while requiring only a few bytes of memory and a handful of comparison operations per inference. To probe real-world reliability, a Gaussian sensor-noise injection study and a single-sensor-failure study were conducted. A redundant three-sensor majority-vote rule preserved above 95% accuracy at a noise level of 50% of the feature standard deviation and remained near 100% even when any single sensor failed, whereas a single-sensor rule collapsed to 48.6% on failure of its governing sensor. The results demonstrate that edge intelligence based on extracted decision-tree rules offers an accurate, interpretable, fault-tolerant, and extremely lightweight solution for continuous on-site waste-odor early warning.
Co-Authors Ade Irma Rilyani Agiska Ria Supriyatna Bastaman Syah Bita Parga Zen Dzakwan Akbar Perdana Wijaya Eko Subyantoro Eko Subyantoro Eko Subyantoro Eko Win Kenali Gustika Barlina Mitra Halim Fathoni Henry Kurniawan Kukuh Puji Lestari, Kukuh Puji Lestari Kurniawan Saputra Muhammad Irfan Lutfi Nadia Nabiha Dziqra Naufaldi Favian Archi Norzaidi Haji Mohd Daud Nurul Qomariyah Putra, Septafiansyah Dwi Sari, Kadek Diana Sarono . Sentosa, Sucitra Aan Septafiansyah Dwi Putra Stephani Yolanda Subyantoro, Eko Subyantoro, Eko Supriyatna, Agiska Ria Tri Sandhika Jaya Tri Wahyudi sy Wayan Ardani Wijaya, Fidi Fran Yudi Setyawan Zahara Ummu Abdillah Zuriati, Zuriati