STEMMINDO: A WEB-BASED INDONESIAN STEMMING ENGINE USING ENHANCED CONFIX STRIPPING - Novi Prisma Yunita; Helmi Roichatul Jannah (Jurnal Riset Informatika, 2026)
Stemming is an essential preprocessing stage in Natural Language Processing (NLP), particularly for Indonesian, which has complex affixation patterns. Most Indonesian stemming implementations are provided as programming libraries, making them less accessible for beginners, educators, and non-programmer researchers. This study presents Stemmindo, a lightweight web-based Indonesian root word search application implementing the Enhanced Confix Stripping (ECS) algorithm using the Laravel framework. Unlike conventional stemming libraries, the system provides a real-time and modular interface that enables users to explore Indonesian morphological processing without writing program code. The novelty of this research lies in the implementation of ECS within an accessible web-based educational tool. Evaluation was conducted using affixation pattern testing, rule-based testing, and real-text evaluation. Testing on 20 affixation patterns achieved 90% accuracy, while evaluation on 100 words representing 33 derived prefix rules achieved 94% accuracy. After applying failure-handling strategies through exception lists and rule-level accommodations, the accuracy increased to 98%. Real-text evaluation was conducted using 1,742 words collected from Indonesian educational web content. After preprocessing and filtering, 564 unique words were evaluated, of which 366 stemming results were successfully matched with the corpus, while the remaining cases mainly consisted of named entities, noisy input, ambiguous forms, overstemming, and understemming. These findings indicate that the proposed system performs effectively for common Indonesian morphological patterns while remaining practical for educational and experimental NLP usage. Future work includes improving reduplication handling, expanding lexical resources, and enhancing accessibility features.