Untitled

Abstract

In document retrieval, besides the suitability of query with search results, there is also a subjective user assessment that is expected to be a deciding factor in document ranking. This preference aspect is referred at the fiqh document searching. People tend to prefer on certain fiqh methodology without rejecting other fiqh methodologies. It is necessary to investigate preference factor in addition to the relevance factor in the document ranking. Therefore, this research proposed a method of term weighting based on preference to rank documents according to user preference. The proposed method is also combined with term weighting based on documents index and books index so it sees relevance and preference aspect. The proposed method is Inverse Preference Frequency with α value (IPFα). In this method, we calculate preference value by IPF term weighting. Then, the preference values of terms that is equal with the query are multiplied by α. IPFα combined with the existing weighting methods become TF.IDF.IBF.IPFα. Experiment of the proposed method uses dataset of several Arabic fiqh documents. Evaluation uses recall, precision, and f-measure calculations. Proposed term weighting method is obtained to rank the document in the right order according to user preference. It is shown from the result with recall value reach 75%, precision 100%, and f-measure 85.7% respectively.

Metode Pembobotan Hibrida untuk Ekstraksi Frasa Kunci Bahasa Arab

“…Fitur ini dapat dianggap sebagai pengganti dari fitur casing karena keduanya dapat mendeteksi kata-kata penting. Banyak metode yang dapat digunakan untuk menghitung seberapa penting kata-kata bedasarkan dokumen eksternal, seperti Inverse Document Frequency (IDF), Inverse Class Frequency (ICF) [13], Inverse Book Frequency (IBF) [14], dan Inverse Preference Frequency (IPF) [15]. Dalam penelitian ini digunakan pembobotan IDF.…”

Perangkingan Dokumen Berbahasa Arab berdasarkan Query dengan Metode Klasifikasi Naïve Bayes dan K-Nearest Neighbor

“…Penelitian sebelumnya yang membahas tentang perangkingan dokumen berbahasa inggris telah dilakukan, beberapa diantaranya adalah perangkingan berdasarkan susunan posisi kata dari query [3], perangkingan dengan pencocokan kata berbasis N-gram dan SVM [4], perangkingan dokumen berdasarkan konteks [5]. Sedangkan penelitian tentang dokumen berbahasa Arab yang telah dilakukan sebelumnya antara lain perangkingan informasi atau dokumen berdasarkan pembobotan kata [6], [7].…”