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All Journal Jurnal Computer Science and Information Technology (CoSciTech) Journal of Software Engineering and Information System (SEIS)
Karan
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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OPTIMASI KNN DENGAN PSO UNTUK KLASIFIKASI KASUS HUKUM DI AUSTRALIA MENGGUNAKAN N-GRAM Karan; M Alidin; Rafi Fadilla
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 1 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i1.8644

Abstract

This study aims to improve the accuracy of legal case classification in Australia by integrating the K-Nearest Neighbors (KNN) algorithm optimized using Particle Swarm Optimization (PSO) and N-Gram-based text representation. The dataset consists of 15,263 legal documents collected from the Federal Court of Australia (FCA) with an 80:20 data split for training and testing. The classification process is carried out by applying TF-IDF weighting and a combination of N-Gram (unigrams, bigrams, trigrams) to enrich the data representation. The PSO optimization results show an optimal K value of 9, with a testing accuracy reaching 96%. The evaluation of the model performance shows a precision value of 0.95, a recall of 0.96, and an F1-Score of 0.94. These results indicate that the combination of KNN, PSO, and N-Gram is able to significantly improve the performance of legal document classification, especially in the Cited case category. However, the weakness of the model in the Not Cited category indicates the need to develop a method to handle data imbalance in order to improve model generalization.
Penerapan K-Nearest Neighbors untuk Klasifikasi Kasus Hukum di Pengadilan Federal Australia: Penerapan K-Nearest Neighbors untuk Klasifikasi Kasus Hukum di Pengadilan Federal Australia Karan; Rafi Fadilla; M Alidin; Taslim
Computer Science and Information Technology Vol 6 No 1 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

With the development of information technology, especially in the legal field, legal case analysis can now be done more efficiently through the application of machine learning. This study aims to classify legal cases based on the status of Cited and Uncited using the K-Nearest Neighbor (KNN) algorithm. The classification process includes text preprocessing stages, word weighting using the TF-IDF method, and testing the KNN algorithm with various values ​​of the parameter k. The research data was taken from the Federal Court of Australia (FCA) covering legal cases from 2006–2009, with three data sharing scenarios: 90:10, 80:20, and 70:30. The evaluation model was carried out using a confusion matrix with metrics of accuracy, precision, recall, and F1-score. The best results were obtained in the 80:20 scenario with a value of k = 3, resulting in an accuracy of 96.36%, a precision of 96.80%, a recall of 99.49%, and an F1-score of 98.13%. With these results, the KNN algorithm is proven to be effective in supporting the automatic legal document classification process.
Co-Authors M Alidin Rafi Fadilla Taslim