Analysis of “Halal” Word in Social Media Using Text Mining and Word Networking

Abstract

The increasing of Islam news in internet makes some perceptions for many people in the world. One of them is about "Halal" concepts as the Islam standard for muslim belief of God. Halal product is the product that created based on Islamic standard law. In every product, it is necessary to ensure its safety by conducting halal certification from the Indonesian Ulama Council (MUI). Then, based on the explanation, this research aims to analysis what internet user say about halal by using social media Twitter. This research uses text mining, word networking and word networking matrix to understanding the use of halal word. This research shows that "halal" has a large network and having relationship with many of word such as: food, meat, certification, restaurant, slaughter, animal, and industry. Then, from the network shows that "food" is the biggest word matrix in the "halal" networking. It word define degree centrality 75, betwenness centrality 20.001,38, and closeness centrality 0.473064 which mean that "halal" word have strong relationship with "food".

Understanding Through the Click: How Social Media Guides or Misleads the Issue of Halal Products

“…The concept of Halal extends beyond food and includes various other products and services, such as finance, cosmetics, and pharmaceuticals, where adherence to Islamic principles guides their production and use (Hasanah & Agustiningsih, 2019). However, many people have a misguided understanding of halal as associated with the prohibition to eat particular meat and beverages, whereas the meaning of halal itself has a detailed meaning, where the upstream to downstream process must be correct and fulfill the halal requirement (Kurniawati & Cakravastia, 2023).…”

6'ya 6 Tahta Üzeri̇nde at Kaplama Problemi̇ni̇ Çözmek İçi̇n Deneti̇msi̇z Maki̇ne Öğrenme Algori̇tmasi

“…Every knight and their place on the board is explicitly shown in Figure 3. The graph analyses provide important information to extract information from data presented in the form of networks such as computational networks [28][29][30], social networks [31][32][33][34], biological networks [35][36][37], word networks [38][39][40], infection networks [41,42]. One of the many analysis methods is clustering which divides the whole network into smaller relational clusters of the nodes.…”