Comparing text classification algorithms with n-grams for mediation prediction

https://doi.org/10.22146/ijccs.93929

Retzi Y. Lewu(1), Kusrini Kusrini(2*), Ainul Yaqin(3)

(1) 
(2) (Scopus ID : 36057015500); The College of Information Management and Computer Science AMIKOM Yogyakarta
(3) Universitas AMIKOM Yogyakarta
(*) Corresponding Author

Abstract


Tingkat keberhasilan mediasi perkara perdata di pengadilan negeri dari tahun ke tahun sangat rendah dan menyebabkan penumpukan perkara yang harus ditangani dengan persidangan. Sementara itu, pendaftaran perkara baru dengan klasifikasi perkara serupa terus bermunculan dan wajib dimediasi. Penelitian ini dilakukan dengan memanfaatkan data mediasi perkara terdahulu sebagai dataset untuk memprediksi hasil mediasi perkara baru. Ketika n-gram digunakan pada dataset yang telah di-preprocessing, hanya ditemukan nilai pada unigram (n=1). Pada penerapan model menggunakan algoritma machine learning, dihasilkan akurasi yang sama sebesar 0.6875 pada Algoritma Naïve Bayes, Logistic Regression dan Support Vector Machine (SVM), sedangkan algoritma Decision tree menghasilkan akurasi paling rendah sebesar 0,375. Rendahnya nilai dikarenakan Decision Tree lebih cenderung overfit untuk digunakan dengan teks berbahasa Indonesia. Pola kalimat formal pada dokumen mediasi berbahasa Indonesia tidak memenuhi unsur – unsur kata majemuk, imbuhan, variasi susunan kata, dan semantik leksikal. Untuk penelitian selanjutnya direkomendasikan penggunaan algoritma klasifikasi lain, pemanfaataannya pada dokumen – dokumen lain seperti putusan pengadilan, penentuan rangking mediator berdasarkan keberhasilan mediasi serta implementasi model pada aplikasi e-mediasi yang terintegrasi dengan sistem informasi manajemen perkara

Keywords


Algoritma Klasifikasi Teks, N-gram, Prediksi hasil mediasi

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DOI: https://doi.org/10.22146/ijccs.93929

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