Development of Rule-Based Feature Extraction in Multi-label Text Classification

Abstract

Hadith is the second main guidelines after the Holy Quran in the Islamic religion, which was revealed through the Messenger of Allah. Today, Hadith can classified by more than one class such as advice class, prohibited, and information to facilitate readers of Hadith in filtering the appropriate classes for each Hadith of Rasulullah SAW. In the course of research, there are many kinds of data involved in a text classification study. Therefore, special handling that fit with the characteristics of certain data is required. This study investigates the handling of multi-label data-Hadith Bukhari in Indonesian translation-focusing on feature extraction, feature weighted, and preprocessing methods. This study uses a rule-based feature extraction combined with several types of preprocessing along with three types of feature-weighted methods: TF-IDF, Word2vec, and Word2vec weighted with TF-IDF, the five preprocessing stages in this research: Case Folding, Tokenization, Remove Punctuation, Stopword Removal, and Stemming. From the 13 experiments conducted in this study consist of 2000 hadiths, it was found that the best performance for multi-label classification of Hadith data produced by the combination of the proposed rule-based feature extraction, Word2vec feature weighted method, and without using Stemming and Stopword Removal in the preprocessing phase. The Hamming Loss value obtained from this combination was 0.0623. The results show that our rule-based feature extraction method better than baseline method.

Improving Chi-Square Feature Selection using a Bernoulli Model for Multi-label Classification of Indonesian-Translated Hadith

“…Several studies on multilabel classification have been conducted; however, only a few such as those of Bakar et al [10], Mediamer et al [11], and Kabi et al [12] focused on the multilabel classification of Hadith data. Liu et al [13] conducted multilabel classification using a correlation function; this was effective for overfitted and noisy data.…”

Klasifikasi Topik Multi Label pada Hadis Shahih Bukhari Menggunakan K-Nearest Neighbor dan Latent Semantic Analysis

“…Peelitian ini menghasilkan F1-Score sebesar 65.275% pada single label dan 88.42% pada multi label data. Penelitian ketiga [5] melakukan klasifikasi hadis multi label dengan menggunakan Rule-based pada ekstraksi fitur untuk mengurangi dimensional yang tinggi dan menghasilkan Hamming Loss sebesar 0.0623.…”

“…Rule-Based digunakan sebagai metode pemilihan fitur yang dapat meningkatkan efisiensi dari sistem yang dibangun [5]. Rule-based dibangun dengan cara mengamati pola kalimat dari kata dan susunan kata dari banyak data Hadis sebagai representasi dari seluruh hadis berdasarkan penelitian [5]. Aturan-aturan yang dimiliki untuk mendefinisikan label anjuran terdapat pada Tabel [10].…”

Klasifikasi Multi Label pada Hadis Bukhari Terjemahan Bahasa Indonesia Menggunakan Mutual Information dan k-Nearest Neighbor

“…Masing-masing hadis memiliki beberapa jenis ajaran yang dapat diterapkan dalam kehidupan umat muslim sehari-hari. Ajaran yang dimaksud seperti anjuran, informasi, hingga larangan-larangan [3]. Namun terdapat kendala dalam mempelajari hadis untuk diterapkan isi dan maknanya dikehidupan sehari-hari apabila umat Islam kesulitan dalam menggolongkan hadis ke dalam anjuran, larangan, dan informasi maupun gabungan dari kedua diantara ketiga jenis ajaran tersebut.…”