Identifikasi Kemiripan Teks Menggunakan Class Indexing Based dan Cosine Similarity Untuk Klasifikasi Dokumen Pengaduan

Abstract

Report handling on "LAPOR!" systemdepends on the system administrator who manually reads every incoming report [3]. Read manually can lead to errors<br />in handling complaints [4] if the data flow is very large and grows rapidly it can take at least three days and sensitive to inconsistencies [3]. In this study, the authors propose a model that can measure and identify the similarity of document reports computerized that can identify the similarity between the Query (Incoming) with Document (Archive). In this study, the authors employed term weighting scheme Class-Based Indexing, and Cosine Similarity to analyze document similarities. CoSimTFIDF, CoSimTFICF and CoSimTFIDFICF values are defined as feature sets for the text classification process using the KNearest<br /><br />Neighbor (K-NN) method. The optimum result<br />evaluation with preprocessing employ Stemming and the best<br />result of all features is 75% training data ratio and 25% test<br />data on the CoSimTFIDF feature that is 84%. Value k = 5<br />has a high accuracy of 84.12%

Implementasi Algoritma Text Mining dan Cosine Similarity untuk Desain Sistem Aspirasi Publik Berbasis Mobile

“…Kajian terkait membahas aspirasi masyarakat, namun fokusnya hanya pada sistem pelaporan yang menggunakan metode Long Term Memory [13]. Kemudian penelitian yang mengukur dan mengidentifikasi kesamaan dokumen laporan yang menggunakan skema term Class-Based Indexing dengan Cosine similarity [14]. Selanjutnya penelitian yang mengimplementasikan aplikasi LAPOR pelayanan public pada pemeritah kota [15] [16].…”

Literature Review Klasifikasi Data Menggunakan Metode Cosine Similarity dan Artificial Neural Network

“…Pada fitur Cosine Similarity berbasis terms weighting TF-IDF memiliki presentase sebesar 84%. Kemudian fitur CoSim-TFICF memiliki 40% rasio data latih dan data uji sebesar 60% [12].…”

Penerapan Information Retrieval Menggunakan Pemodelan Topik Pada Deskripsi Portal Multimedia

“…Semakin mirip 2 buah objek maka nilai kemiripan mendekati nilai 1. Sedangkan cosine similarity merupakan perhitungan kesamaan berdasarkan sudut cosine diantara 2 buah vektor yaitu vektor query dan vektor dokumen [9].…”