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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE JSiI (Jurnal Sistem Informasi) Jurnal Ilmu Komputer dan Desain Komunikasi Visual Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Teknik Informatika Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Ilmiah Teknik Informatika dan Komunikasi MDP Student Conference Computing Insight: Journal of Computer Science
Tri Luhur Indayanti Sugata
Universitas Pembangunan Nasional Veteran Jawa Timur
Aerospace Engineering Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Enhancing Aspect-Based Sentiment Analysis in Imbalanced Multilabel Datasets using Resampling and Classifiers for Digital Signature Applications Narendra, Efriza Cahya; Arifiyanti, Amalia Anjani; Sugata, Tri Luhur Indayanti
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 2 (2025): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i2.3023

Abstract

Amid the growing demand for digital identity solutions, applications like Privy, VIDA, and Xignature offer integrated digital signature and e-stamp services, generating extensive user feedback on platforms like Google Play Store and App Store. Extracting meaningful insights from thousands of reviews is challenging, necessitating effective sentiment analysis. Aspect-Based Sentiment Analysis (ABSA) enables detailed sentiment evaluation by linking user feedback to specific aspects and sentiments. However, ABSA faces challenges with imbalanced datasets where label distributions are uneven. This study explores the application of three resampling techniques, including MLROS, MLSMOTE, and REMEDIAL, to address this issue in multilabel classification. Using multilabel classifiers, including Binary Relevance, Label Powerset, and Classifier Chains, the study systematically evaluates their performance. Results reveal that resampling significantly enhances outcomes, with MLROS and Classifier Chains under a 70:30 split achieving the best performance, reducing Hamming Loss to 0.0401 or 95% accuracy. This marks a 34.2% improvement over baseline models without resampling or classifiers. The model generalizes well to unseen data with minimal overfitting, as indicated by validation results. These results underscore the importance of imbalanced data resampling and multilabel classification techniques in advancing ABSA, offering valuable insights for improving sentiment analysis in real-world applications.
Co-Authors Abdul Rezha Efrat Najaf Agung Brastama Putra Amalia Anjani Arifiyanti Annisa Lusyani Zahra Audy Fitri Ariani Fariz Iqbal Ramadhani Mukhlis Narendra, Efriza Cahya Paloma Ransi Prasasti Karunia Farista Ananto Rafika Rahmawati Rafika Rahmawati Rafika Rahmawati Satibi, Iswanda Fauzan Sembilu, Nambi Setiawan, Moch Rezeki Thomas Aquino Berno Doduk Tri Puspa Rinjeni Ubaidillah Fahmi, Rohmat Virdha Rahma Aulia Wahyuni, Eka Dyar