Certainty Factor-based Expert System for Meat Classification within an Enterprise Resource Planning Framework

Authors

  • Adhi Kusnadi Department of Computer Science IPB University and Department of Informatics Universitas Multimedia Nusantara, Indonesia
  • Yandra Arkeman Department of Agricultural Industrial Technology IPB University
  • Khaswar Syamsu Department of Agricultural Industrial Technology IPB University
  • Sony Hartono Wijaya Department of Computer Science IPB University

DOI:

https://doi.org/10.26555/jiteki.v9i3.26443

Keywords:

Certainty Factor (CF), ERP, Expert System, Halal Beef, Halal Products

Abstract

The demand for halal products in the Islamic context continues to be high, requiring adherence to halal and haram laws in consuming food and beverages. However, individuals face the challenge of distinguishing between haram meat and permissible halal meat. This study aims to answer these challenges by designing an expert system application within the ERP framework to increase the usability functionality of the system that can differentiate between beef, pork, or a mixture of both based on the physical characteristics of the meat. The aim is to determine halal products permissible for consumption by Muslims. The research methodology includes a data collection process that involves taking 30 meat samples from various sources, and the criteria used to classify the meat will be determined based on an analysis of the physical characteristics of the meat. System administrators use expert systems to ensure proper treatment of meat during administration processes, including separating halal beef from pork and implementing different inventory procedures. The Certainty Factor (CF) inference engine deals with uncertainty even though the expert system's accuracy level is relatively good with several rules. However, these results must be studied further because the plan relies on expert opinion. Therefore, it is necessary to set the correct CF value for accurate height classification. The CF inference engine facilitates reasoned conclusions in meat classification. Functional testing confirms the smooth running of the system, validating its reliability and performance. In addition, the expert system accuracy assessment produces a commendable accuracy rate of 90%. In addition, the expert system works powerfully on various meat samples, accurately classifying meat types with high precision. This study explicitly highlights the expert system's design for meat classification in determining halal products by using the Expert System Certainty Factor. In conclusion, this expert system provides an efficient and reliable approach to classifying meat and supports the production and consumption of Halal products according to Islamic principles.

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Published

2023-07-20

Issue

Section

Articles