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All Journal Jurnal Informatika dan Teknik Elektro Terapan Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC)
Narendra, Efriza Cahya
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Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

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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.
ANALISIS DESAIN APLIKASI JAHIT PAKAIAN CUSTOM ONLINE BERBASIS MOBILE Narendra, Efriza Cahya; Priyanto, Moh. Azzam; Putri, Dinda Adisty Yudianto; Wati, Seftin Fitri Ana; Fitri, Anindo Saka
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 1 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i1.2873

Abstract

Semakin ramai penggunaan teknologi yang membuat masyarakat merasa diberikan kemudahan. Masyarakat tidak perlu melakukan pemesanan secara real-time, melainkan dapat dengan mudah hanya perlu membuka aplikasi ini. Aplikasi ini juga menyediakan opsi pembuatan pakaian secara custom, prosedurnya sama dengan pemesanan reguler. Tujuannya untuk mengembangkan kreativitas pada customer yang akan membuat pakaian-pakaian unik. Metode penelitian yang digunakan dalam penelitian ini adalah observasi dan studi pustaka. Observasi ini dilakukan dengan mengamati secara langsung kondisi lapangan dan mengumpulkan data yang dibutuhkan. Sedangkan studi pustaka, peneliti mempelajari buku atau jurnal penelitian lain yang  relevan dengan penelitian ini untuk selanjutnya dijadikan sebagai referensi dan acuan dalam proses penyusunan penelitian. Kesimpulan yang dapat diambil adalah aplikasi ini dapat mempermudah dalam melakukan pemesanan jahitan secara online, dapat melakukan custom pakaian, melakukan revisi jahitan serta tidak membatasi kreativitas customer dalam mengupload model pakaian.
Co-Authors Amalia Anjani Arifiyanti Fitri, Anindo Saka Priyanto, Moh. Azzam Putri, Dinda Adisty Yudianto Tri Luhur Indayanti Sugata Wati, Seftin Fitri Ana