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All Journal Jurnal Teknik Informatika (JUTIF)
Bambang Widoyono
Informatics, Universitas Sebelas Maret, Indonesia
Computer Science & IT

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Aspect-Based Sentiment Analysis of Access by KAI Application Reviews Using IndoBERT for Multi-Label Classification Tasks Hilda Nur Alfiana; Afrizal Doewes; Bambang Widoyono
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5402

Abstract

Ratings and reviews on mobile applications provide valuable insights into user experience and satisfaction with app features and services. However, ratings are subjective and often inconsistent with the content of the reviews. Therefore, a more in-depth analysis of the review content is necessary to identify evaluation points accurately. This study aims to evaluate the performance of IndoBERT in Aspect-Based Sentiment Analysis (ABSA) on Access by KAI application reviews. Data were collected by scraping user reviews from the Google Play Store, then annotated using a hybrid labeling approach. The resulting dataset was used to fine-tune the IndoBERT model across three ABSA tasks: aspect classification, sentiment classification for each aspect, and joint aspect-sentiment classification. We also benchmarked the model against baseline models to demonstrate its effectiveness. The results show that IndoBERT achieved the best performance across all tasks, specifically aspect classification (accuracy 0.928, F1-score 0.785), sentiment classification (accuracy 0.928, F1-score 0.752), and joint aspect-sentiment classification (accuracy 0.962, F1-score 0.549). Overall, IndoBERT successfully outperformed SVM and XGBoost with TF-IDF, BiLSTM with pre-trained IndoBERT embeddings, mBERT, and XLM-R. This study contributes a new dataset that provides resources for further research and development in Indonesian Natural Language Processing (NLP). These findings also highlight the advantages of a monolingual model trained specifically on Indonesian-language data.
Optimizing E-commerce Personalization through Hybrid Decision Tree–Nearest Neighbor Recommendation Integration Akhmad Syaifuddin; Ristu Saptono; Arif Rohmadi; Bambang Widoyono; Brilyan Hendrasuryawan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5418

Abstract

Single-method recommendation systems face critical limitations: content-based filtering suffers from overspecialization while collaborative filtering struggles with data sparsity and cold-start problems. This research introduces an innovative hybrid recommendation framework that synthesizes Content-Based Filtering (CBF) utilizing Decision Trees with Collaborative Filtering (CF) employing Nearest Neighbor algorithms. Our approach addresses the inherent limitations of singular recommendation methodologies by integrating product attribute analysis with collective user behavior patterns. We conducted comprehensive evaluations using a shopping behavior dataset comprising 3,900 consumer records with diverse demographic and product interaction data. Our findings reveal that an asymmetric hybrid configuration—weighted at 70% for CBF and 30% for CF—achieves optimal performance with a Root Mean Square Error (RMSE) of 0.7422. The system incorporates an interactive user interface that facilitates a natural shopping experience: browsing available items, receiving personalized recommendations, and providing explicit feedback on suggested products. Through feature importance analysis, we identified key product attributes that significantly influence recommendation quality, including size variations and specific color preferences. The hybrid approach demonstrates 42% greater category diversity and 37% more recommendation diversity compared to pure content-based filtering, while maintaining superior accuracy metrics. Our research contributes to understanding optimal hybrid architectures and provides practical insights for implementing effective personalization strategies in real-world e-commerce environments.
Co-Authors Afrizal Doewes Akhmad Syaifuddin Arif Rohmadi Brilyan Hendrasuryawan Hilda Nur Alfiana Ristu Saptono