Application of Forward Chaining Method, Certainty Factor, and Bayes Theorem for Cattle Disease

Fajar Rahardika Bahari Putra (1), Abdul Fadlil (2), Rusydi Umar (3)
(1) Informatics Engineering Study Program, Muhammadiyah University of Sorong, Southwest Papua, 98416, Indonesia
(2) Electrical Engineering Study Program, University Ahmad Dahlan, Yogyakarta, 55166, Indonesia
(3) Informatics Engineering Study Program, University Ahmad Dahlan, Yogyakarta, 55166, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Putra, Fajar Rahardika Bahari, et al. “Application of Forward Chaining Method, Certainty Factor, and Bayes Theorem for Cattle Disease”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 1, Feb. 2024, pp. 365-74, doi:10.18517/ijaseit.14.1.18912.
Indonesia is a country that has many natural resources, especially mammals. The Papua and West Papua regions are large provinces with abundant natural resources and tremendous livestock potential. The availability of natural resources in the form of live cattle provides a great opportunity to develop animal husbandry in West Papua province. This research was conducted to create a new expert system with a knowledge base to solve the problems that occur and be useful for the community, especially cattle breeders. The current problem is the delay and lack of medical personnel in diagnosing cattle diseases, the distance that must be traveled, which is still very difficult to travel, and the lack of understanding of farmers in early handling when implications indicate animals. So, the Certainty Factor Method and Bayes Theorem with Forward-Chaining search are used to handle current problems. From the results of manual calculations, Certainty Factor Forward Chaining search is a method that has an uncertainty value of 99.84% for 3-day fever compared to Bayes Theorem Forward Chaining search with a value of 50% for worms, 50% for 3-day fever and 50% for nail rot, if applied then Certainty Factor Forward Chaining search is the most appropriate. Likewise, updating the knowledge base must be done from time to time. So that in the future, it can be compared with other methods and Android-based to facilitate current breeders.

Purwaningsih and D. Nurhayati, “Alternatif Penggunaan Obat Cacing Herbal dari Biji Buah Pinang untuk Ternak Sapi di Distrik Prafi Kabupaten Manokwari,” Aksiologiya J. Pengabdi. Kpd. Masy., vol. 6, no. 3, pp. 407–415, 2022, doi: 10.30651/aks.v6i3.5526.

J. H. Priyanka and N. Parveen, “Online employment portal architecture based on expert system,” Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 3, pp. 1731–1735, 2022, doi:10.11591/ijeecs.v25.i3.pp1731-1735.

M. Abd Ulkareem Naser and S. Mohammed Hasen, “Design an expert system for students graduation projects in Iraq universities: Basrah University,” Int. J. Electr. Comput. Eng., vol. 11, no. 1, pp. 602–610, 2021, doi: 10.11591/ijece.v11i1.pp602-610.

K. B. Dasari and N. Devarakonda, “Detection of different DDoS attacks using machine learning classification Algorithms,” Ing. des Syst. d’Information, vol. 26, no. 5, pp. 461–468, 2021, doi:10.18280/isi.260505.

B. Srikanth, A. Naresh Kumar, and P. Sridhar, “Four Circuit Transmission Line Location for Inter Circuit Faults Using Fuzzy Expert System,” J. Eur. des Syst. Autom., vol. 55, no. 2, pp. 273–280, 2022, doi: 10.18280/jesa.550216.

M. Y. Thanoun, M. T. Yaseen, and A. M. Aleesa, “Development of Intelligent Parkinson Disease Detection System Based on Machine Learning Techniques Using Speech Signal,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 1, pp. 388–392, 2021, doi: 10.18517/ijaseit.11.1.12202.

B. M. Nema, Y. M. Mohialden, N. M. Hussien, and N. A. Hussein, “COVID-19 knowledge-based system for diagnosis in Iraq using IoT environment,” Indones. J. Electr. Eng. Comput. Sci., vol. 21, no. 1, pp. 328–337, 2021, doi: 10.11591/ijeecs.v21.i1.pp328-337.

R. Ponnala, M. Chakravarthy, and S. V. N. L. Lalitha, “Dynamic state power system fault monitoring and protection with phasor measurements and fuzzy based expert system,” Bull. Electr. Eng. Informatics, vol. 11, no. 1, pp. 103–110, 2022, doi:10.11591/eei.v11i1.3585.

I. G. A. K. Pamungkas, T. Ahmad, and R. M. Ijtihadie, “Analysis of Autoencoder Compression Performance in Intrusion Detection System,” Int. J. Saf. Secur. Eng., vol. 12, no. 3, pp. 395–401, 2022, doi: 10.18280/ijsse.120314.

F. Ahmed, Fatema-Tuj-Johora, R. J. Chakma, S. Hossain, and D. Sarma, “A Combined Belief Rule based Expert System to Predict Coronary Artery Disease,” Proc. 5th Int. Conf. Inven. Comput. Technol. ICICT 2020, pp. 252–257, 2020, doi:10.1109/ICICT48043.2020.9112540.

A. Sarazin et al., “Expert system dedicated to condition-based maintenance based on a knowledge graph approach: Application to an aeronautic system,” Expert Syst. Appl., vol. 186, no. August 2020, p. 115767, Dec. 2021, doi: 10.1016/j.eswa.2021.115767.

A. Saibene, M. Assale, and M. Giltri, “Expert systems: Definitions, advantages and issues in medical field applications,” Expert Syst. Appl., vol. 177, no. November 2020, p. 114900, 2021, doi:10.1016/j.eswa.2021.114900.

A. Kopczynski, “Hybrid Expert System for Computer-Aided Design of Ship Thruster Subsystems,” IEEE Access, vol. 8, pp. 57024–57035, 2020, doi: 10.1109/access.2020.2982264.

W. J. Hussain, A. A. Akkar, and H. A. Rasheed, “Comparison of Robust and Bayesian Methods for Estimating the Burr Type XII Distribution,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 5, pp. 1835–1838, 2020, doi: 10.18517/ijaseit.10.5.12990.

T. Sajana, M. Syamala, L. Phaneendra Maguluri, and C. Usha Kumari, “A hybrid approach for classification of infectious diseases,” Mater. Today Proc., no. xxxx, 2021, doi: 10.1016/j.matpr.2020.11.727.

F. Mijaswari and S. Sulindawaty, “Computer Troubleshooting Expert System Damage Certainty Factor Method Using Web Based,” J. Comput. Networks, Archit. High Perform. Comput., vol. 2, no. 2, pp. 171–176, 2020, doi: 10.47709/cnapc.v2i2.386.

B. Walek and V. Fojtik, “A hybrid recommender system for recommending relevant movies using an expert system,” Expert Syst. Appl., vol. 158, p. 113452, 2020, doi: 10.1016/j.eswa.2020.113452.

S. Hossain, D. Sarma, R. J. Chakma, W. Alam, M. M. Hoque, and I. H. Sarker, A rule-based expert system to assess coronary artery disease under uncertainty, vol. 1235 CCIS. Springer Singapore, 2020. doi: 10.1007/978-981-15-6648-6_12.

W. Zhou et al., “A Hybrid Expert System for Individualized Quantification of Electrical Status Epilepticus During Sleep Using Biogeography-Based Optimization,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 30, pp. 1920–1930, 2022, doi:10.1109/tnsre.2022.3186942.

C. Jiang, W. Fan, N. Yu, and E. Liu, “Spatial modeling of gully head erosion on the Loess Plateau using a certainty factor and random forest model,” Sci. Total Environ., vol. 783, p. 147040, 2021, doi:10.1016/j.scitotenv.2021.147040.

A. G. Devi et al., “An Improved CHI2 Feature Selection Based a Two-Stage Prediction of Comorbid Cancer Patient Survivability,” Rev. d’Intelligence Artif., vol. 37, no. 1, pp. 83–92, 2023, doi:10.18280/ria.370111.

S. H. Alizadeh, A. Hediehloo, and N. S. Harzevili, “Multi independent latent component extension of naive Bayes classifier,” Knowledge-Based Syst., vol. 213, p. 106646, 2021, doi:10.1016/j.knosys.2020.106646.

J. D. Rudie et al., “Subspecialty-level deep gray matter differential diagnoses with deep learning and bayesian networks on clinical brain mri: A pilot study,” Radiol. Artif. Intell., vol. 2, no. 5, pp. 1–13, 2020, doi: 10.1148/ryai.2020190146.

S. Wang, J. Ren, and R. Bai, “A semi-supervised adaptive discriminative discretization method improving discrimination power of regularized naive Bayes,” Expert Syst. Appl., vol. 225, no. April, p. 120094, 2023, doi: 10.1016/j.eswa.2023.120094.

G. Lanzaro and M. Andrade, “A fuzzy expert system for setting Brazilian highway speed limits,” Int. J. Transp. Sci. Technol., vol. 12, no. 2, pp. 505–524, 2023, doi: 10.1016/j.ijtst.2022.05.003.

X. Zhou, H. Du, Y. Sun, H. Ren, P. Cui, and Z. Ma, “A new framework integrating reinforcement learning, a rule-based expert system, and decision tree analysis to improve building energy flexibility,” J. Build. Eng., vol. 71, no. April, p. 106536, 2023, doi:10.1016/j.jobe.2023.106536.

F. Inusah, Y. M. Missah, U. Najim, and F. Twum, “Integrating expert system in managing basic education: A survey in Ghana,” Int. J. Inf. Manag. Data Insights, vol. 3, no. 1, p. 100166, 2023, doi:10.1016/j.jjimei.2023.100166.

S. Chatterjee, D. Dey, S. Munshi, and S. Gorai, “Dermatological expert system implementing the ABCD rule of dermoscopy for skin disease identification,” Expert Syst. Appl., vol. 167, p. 114204, 2021, doi: 10.1016/j.eswa.2020.114204.

F. R. B. Putra, A. Fadlil, and R. Umar, “Analisis Metode Forward Chaining Pada Sistem Pakar Diagnosa Penyakit Hewan Sapi Berbasis Android,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 1034–1044, 2021, doi:10.30645/j-sakti.v5i2.398.

Syed Naseer Ahmed, M. Bhargava, and S. S. K V, “Material selection using knowledge-based expert system for racing bicycle forks,” Intell. Syst. with Appl., vol. 19, no. May, p. 200257, 2023, doi:10.1016/j.iswa.2023.200257.

Y. Cao, Z. J. Zhou, C. H. Hu, S. W. Tang, and J. Wang, “A new approximate belief rule base expert system for complex system modelling,” Decis. Support Syst., vol. 150, no. July 2020, p. 113558, 2021, doi: 10.1016/j.dss.2021.113558.

R. Ul Islam, M. S. Hossain, and K. Andersson, “A deep learning inspired belief rule-based expert system,” IEEE Access, vol. 8, pp. 190637–190651, 2020, doi: 10.1109/access.2020.3031438.

A. Aziz, B. W. Setyawan, and K. Saddhono, “Using expert system application to diagnose online game addiction in junior high school students: Case study in five big city in Indonesia,” Ing. des Syst. d’Information, vol. 26, no. 5, pp. 445–452, 2021, doi:10.18280/isi.260503.

R. Yera, A. A. Alzahrani, L. Martínez, and R. M. Rodríguez, “A Systematic Review on Food Recommender Systems for Diabetic Patients,” Int. J. Environ. Res. Public Health, vol. 20, no. 5, 2023, doi:10.3390/ijerph20054248.

A. Satria, A. Naufal Yulianra, M. Az Zahrah, and M. S. Anggreainy, “Application of the Certainty Factor and Forward Chaining Methods to a Cat Disease Expert System,” IEEE Xplore, vol. 6, no. 2, pp. 83–88, 2022, doi: 10.1109/aidas56890.2022.9918803.

J. Straub, “Expert system gradient descent style training: Development of a defensible artificial intelligence technique,” Knowledge-Based Syst., vol. 228, p. 107275, 2021, doi: 10.1016/j.knosys.2021.107275.

M. R. Mufid, A. Basofi, S. Mawaddah, K. Khotimah, and N. Fuad, “Risk diagnosis and mitigation system of covid-19 using expert system and web scraping,” IES 2020 - Int. Electron. Symp. Role Auton. Intell. Syst. Hum. Life Comf., pp. 577–583, 2020, doi:10.1109/IES50839.2020.9231619.

Y. Goita and M. Sidibe, “Towards a Comprehensive Expert System for Coronavirus Disease,” 2021 7th Int. Conf. Inf. Manag. ICIM 2021, pp. 18–23, 2021, doi: 10.1109/ICIM52229.2021.9417046.

Henderi, M. Maulana, H. L. H. S. Warnars, D. Setiyadi, and T. Qurrohman, “Model Decision Support System for Diagnosis COVID-19 Using Forward Chaining: A Case in Indonesia,” IEEE Xplore Int. Conf. Cyber IT Serv. Manag. CITSM 2020, pp. 6–9, 2020, doi:10.1109/citsm50537.2020.9268853.

S. Zhao, F. Blaabjerg, and H. Wang, “An overview of artificial intelligence applications for power electronics,” IEEE Trans. Power Electron., vol. 36, no. 4, pp. 4633–4658, 2021, doi:10.1109/tpel.2020.3024914.

R. M. Tawafak, G. Alfarsi, A. Romli, J. Jabbar, S. I. Malik, and A. Alsideiri, “A Review Paper on Student-Graduate Advisory Expert system,” IEEE Xplore Int. Conf. Comput. Inf. Technol. ICCIT 2020, vol. 01, no. ICCIT-1441, pp. 10–14, 2020, doi: 10.1109/iccit-144147971.2020.9213794.

J. Ha, M. Il Roh, K. S. Kim, and J. H. Kim, “Method for pipe routing using the expert system and the heuristic pathfinding algorithm in shipbuilding,” Int. J. Nav. Archit. Ocean Eng., vol. 15, p. 100533, 2023, doi: 10.1016/j.ijnaoe.2023.100533.

M. P. P. Pieroni, T. C. McAloone, Y. Borgianni, L. Maccioni, and D. C. A. Pigosso, “An expert system for circular economy business modelling: advising manufacturing companies in decoupling value creation from resource consumption,” Sustain. Prod. Consum., vol. 27, pp. 534–550, 2021, doi: 10.1016/j.spc.2021.01.023.

K. Saddhono, B. W. Setyawan, Y. M. Raharjo, and R. Devilito, “The diagnosis of online game addiction on Indonesian adolescent using certainty factor method,” Ing. des Syst. d’Information, vol. 25, no. 2, pp. 191–197, 2020, doi: 10.18280/isi.250206.

J. Yuan, S. Zhang, S. Wang, F. Wang, and L. Zhao, “Process abnormity identification by fuzzy logic rules and expert estimated thresholds derived certainty factor,” Chemom. Intell. Lab. Syst., vol. 209, no. December 2020, p. 104232, 2021, doi:10.1016/j.chemolab.2020.104232.

D. Susanto, A. Fadlil, and A. Yudhana, “Application of the Certainty Factor and Forward Chaining Methods to a Goat Disease Expert System,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 6, no. 2, 2020, doi: 10.23917/khif.v6i2.10867.

A. S. P. Natalia Anjela Sagat, “Sistem Pakar untuk Mendiagnosa Penyakit Tanaman Kopi Berbasis WEB,” JPTI J. Pendidik. dan Teknol. Indones., vol. 1, no. No 8 Agustus, pp. 25–32, 2021, doi:10.32767/jusikom.v4i1.423.

M. Ibtasam, “Accuracy Measurements and Decision Making by Naïve Bayes and Forward Chaining Method to Identify the Malnutrition Causes and Symptoms,” Sci. J. Informatics, vol. 8, no. 2, pp. 320–324, 2021, doi: 10.15294/sji.v8i2.29317.

G. Aguilera-Venegas, E. Roanes-Lozano, G. Rojo-Martínez, and J. L. Galán-García, “A proposal of a mixed diagnostic system based on decision trees and probabilistic experts rules,” J. Comput. Appl. Math., vol. 427, p. 115130, 2023, doi: 10.1016/

A. Seppewali, W. H. Mulyo, and R. Riswan, “Sistem Pakar Diagnosa Kerusakan Motor Suzuki Smash Titan 115 Cc Menggunakan Metode Forward Chaining,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 5, no. 1, pp. 13–20, 2023, doi: 10.47233/jteksis.v5i1.728.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).