Expert System With Certainty Factor For Early Diagnosis Of Red Chili Peppers Diseases

Authors

  • Fahrul Agus Computer Science Dept. Faculty of Computer Science and Information Technology
  • Hernanda Eka Wulandari Computer Science Dept. Faculty of Computer Science and Information Technology Kampus Gunung Kelua, Samarinda, 75123 Kalimantan Timur Indonesia
  • Indah Fitri Astuti Computer Science Dept. Faculty of Computer Science and Information Technology Kampus Gunung Kelua, Samarinda, 75123 Kalimantan Timur Indonesia

DOI:

https://doi.org/10.33633/jais.v2i2.1455

Abstract

Red chili peppers (Capsicum annuum L.) plants classified as fruit and vegetables spices (herbs), that almost all people consume it for everyday purposes. Not only used for household consumption, but also used in various industries as a raw material. Utilization as raw materials in various industry makes red chili as vegetable crops of high economic value and has a great prospect. But its diseases are complex enough for causing farmers hard to diagnose and resulted in reduced production levels. One solution to this problem is the establishment of an expert system that can help farmers to diagnose major diseases of red pepper plant in a practical and accurate. The purpose of this research is to make the application of expert systems for diagnosing diseases of red chili. This application uses forward chaining to diagnose disease and calculate the value of possibilities with Certainty Factor. This method is to prove whether a fact that certainly would not have been shaped or metrics that are typically used in an expert system. This method is suitable for expert systems to diagnose something uncertain. Certainty Factor expressed confidence in an event or factual hypothesis based on evidence or expert judgment. The results of research is in the form of an expert system application that can diagnose diseases of red pepper plants with enough accuracy and can help in overcoming the problem of crop failure. Based on the data taken from tests performed in this study, the system can diagnose red chili plant disease with high accuracy.

Author Biography

Fahrul Agus, Computer Science Dept. Faculty of Computer Science and Information Technology

-------Computer Science Dept. Faculty of Computer Science and Information TechnologyKampus Gunung Kelua, Samarinda, 75123 Kalimantan Timur Indonesia

References

Cahyono, B. 2014. Rahasia Budidaya Cabai Merah Besar & Keriting Secara Organik dan Anorganik. Jakarta: Pustaka Mina.

Cakrabawa. 2013. Buletin Konsumsi Pangan. Jakarta Selatan: Pusat Data dan Sistem Informasi Pertanian.

Ali, M,A. 2015. Pemodelan Sistem Pakar Diagnosa Penyakit Tanaman Cabai Menggunakan Metode AHP-SAW. Malang: Universitas Brawijaya.

Tuswanto, 2013. Sistem Pakar Untuk Mendiagnosa Hama dan Penyakit Tanaman Bawang Merah Menggunakan Certainty Factor. Universitas Ahmad Dahlan.

Andri Saputra, H.A.T., 1978. Sistem Pakar Kerusakan Mesin Jahit Dengan Metode Certainty Factor Berbasis Android. Journal Of Applied Intelligent System, Vol.1, No. 1, Februari 2016 pp.36–47.

Kusumadewi, S. 2003. Artificial Intelegence. Yogyakarta: Andi.

Aziz, F. 1994. Belajar sendiri Pemrograman Sistem Pakar, Jakarta: Elex Media Komputindo.

Turban, 2005. Dessicion Support System and Intelligent System. Yogyakarta: Andi.

Arhami, M. 2005. Konsep Dasar Sistem Pakar, Yogyakarta: Andi.

Kusrini. 2008. Sistem Pakar Teori Dan Aplikasi. Yogyakarta: Andi.

Alex, S. 2015. Usaha Tani Cabai Kiat Jitu Bertanam Cabai di Segala Musim. Yogyakarta: Pustaka Baru Press.

Surachman. E, 2007. Hama Tanaman. Yogyakarta: Penerbit Kanisius.

Suryanto. W, 2010. Hama dan Penyakit Tanaman Pangan, Hortikultura, dan Perkebunan. Yogyakarta: Penerbit Kanisius

Irwanto. 2005. Perancangan Object Oriented Software dengan UML. Yogyakarta: Andi.

Nugroho, A. 2009, Rekayasa Perangkat Lunak menggunakan UML dan Java, Andi Offset, Yogyakarta: Andi.

Fowler, M. 2004. UML. Distilled.Edisi ke-3. Terjemahan Tim Penerjemah Penerbit Andi. Yogyakarta: Andi.

Downloads

Published

2018-08-03