Application Of Expert System In Determining Diseases In Potato Plants

Authors

  • Ali Ikhwan Embedded, Networks and Advanced Computing (ENAC) Research Cluster, School of Computer and Communication Engineering, Perlis, Malaysia
  • Nur Ahmadi Bi Rahmani Faculty Of Islamic Economics and Business, Universitas Islam Negeri Sumatera Utara, Medan, Sumatera Utara, Indonesia
  • Moustafa H. Aly Department of Electronics and Communications Engineering, Arab Academy for Science, Technology and Maritime Transport, Egypt
  • Nuri Aslami Faculty Of Islamic Economics and Business, Universitas Islam Negeri Sumatera Utara, Medan, Sumatera Utara, Indonesia
  • Muhammad Dedi Irawan Faculty of Science and Technology, Universitas Islam Negeri Sumatera Utara, Medan, Sumatera Utara, Indonesia
  • Imam Ahmad Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia

DOI:

https://doi.org/10.24002/ijis.v7i2.10213

Abstract

This research aims to develop an expert system in diagnosing diseases in potato plants using the Case Based Reasoning (CBR) method approach combined with the K-Nearest Neighbor (K-NN) algorithm. The system is designed to help farmers identify the type of disease based on the symptoms that appear, as well as provide relevant solutions to increase crop productivity. In previous research, the CBR method showed a limited accuracy rate of 74% because it only relied on one algorithm. Through the application of two methods in data analysis, namely CBR and K-NN, this study succeeded in increasing the diagnosis accuracy to be higher than the previous approach of 80%. The system is implemented in the form of a web-based application that is easily accessible by farmers. The results show that the integration of these two methods provides more optimal, effective, and accurate results in detecting potato plant diseases based on symptom data. The findings are expected to contribute significantly to the development of agricultural technology, especially in improving the harvest success of potato farmers in Indonesia.

References

[1] I. Singh, A. Jaiswal, and N. Sachdeva, “Comparative Analysis of Deep Learning Models for Potato Leaf Disease Detection,” in 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2024, pp. 421–425. doi: 10.1109/Confluence60223.2024.10463314.

[2] L. Suciningtyas, R. Alfatikarani, M. B. F. Alan, F. M. Maimunir, and H. P. A. Tjahyaningtijas, “Activation Function Comparison on Potato Leaf Disease Classification Performance,” in 2023 6th International Conference on Vocational Education and Electrical Engineering: Integrating Scalable Digital Connectivity, Intelligence Systems, and Green Technology for Education and Sustainable Community Development, ICVEE 2023 - Proceeding, 2023. doi: 10.1109/ICVEE59738.2023.10348283.

[3] R. Maurya, R. Burget, R. Shaurya, M. Kiac, and M. K. Dutta, “Multi-Head Attention-Based Transfer Learning Approach for Porato Disease Detection,” in International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, 2023. doi: 10.1109/ICUMT61075.2023.10333272.

[4] H. Bommala, N. J. Babu, P. Srikanth, S. K. R. Mallidi, T. S. R. Sai, and R. Mounika, “Detecting Diseases in Potato Leaves using Deep Learning and Machine Learning Approaches: A Review,” in Proceedings of the 4th International Conference on Smart Electronics and Communication, ICOSEC 2023, 2023. doi: 10.1109/ICOSEC58147.2023.10276029.

[5] A. Singha, M. S. Hossain Moon, and S. Roy Dipta, “An End-to-End Deep Learning Method for Potato Blight Disease Classification Using CNN,” in 2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023, 2023. doi: 10.1109/ICCINS58907.2023.10450112.

[6] N. Moawad, H. Zaki, T. A. El Moniem Essa, and M. Said, “Detection of Potato Tuber Diseases Using Machine Learning Models,” in 2023 International Conference on Artificial Intelligence Science and Applications in Industry and Society, CAISAIS 2023, 2023. doi: 10.1109/CAISAIS59399.2023.10269994.

[7] A. Setiawan, Soeheri, E. Panggabean, M. A. Elhias, F. Ikorasaki, and B. Riski, “Efficiency of Bayes Theorem in Detecting Early Symptoms of Avian Diseases,” in 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, 2019. doi: 10.1109/CITSM.2018.8674273.

[8] M. S. Ummah, “No 主観的健康感を中心とした在宅高齢者における 健康関連指標に関する共分散構造分析Title,” Sustain., vol. 11, no. 1, pp. 1–14, 2019, [Online]. Available: http://scioteca.caf.com/bitstream/handle/123456789/1091/RED2017-Eng-8ene.pdf?sequence=12&isAllowed=y%0Ahttp://dx.doi.org/10.1016/j.regsciurbeco.2008.06.005%0Ahttps://www.researchgate.net/publication/305320484_SISTEM_PEMBETUNGAN_TERPUSAT_STRATEGI_MELESTARI

[9] S. Sahu, R. Kumar, M. S. Pathan, J. Shafi, Y. Kumar, and M. F. Ijaz, “Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3168161.

[10] A. I. Akolzin, P. G. Korolev, and A. Y. Kurochkin, “Development of the Data Interaction Structure for the Production Expert System Focused on Support of Measurement Channels Design,” in Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021, 2021. doi: 10.1109/ElConRus51938.2021.9396445.

[11] Dini Syahfitri, B. Serasi Ginting, and A. Sihombing, “Expert System To Diagnose Pregnancy Diseases In Women Using Naive Bayes Method,” J. Inf. Syst. Technol. Res., vol. 1, no. 2, pp. 98–102, May 2022, doi: 10.55537/jistr.v1i2.148.

[12] S. Sharma, D. Mehrotra, and N. B. Ben Saoud, “Imputation Techniques Analysis for Incomplete Medical Datasets in Case-Based Reasoning System,” in Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023, 2023. doi: 10.1109/Confluence56041.2023.10048893.

[13] B. A. Krisnamurti, Y. D. Prasetyo, and C. Kartiko, “Expert System of Land Suitability for Fruit Cultivation Using Case-Based Reasoning Method,” in 10th IEEE International Conference on Communication, Networks and Satellite, Comnetsat 2021 - Proceedings, 2021. doi: 10.1109/COMNETSAT53002.2021.9530782.

[14] W. T. Windi, Novriyenni, and Husnul Khair, “Application of Case Based Reasoning Method to Diagnose Rice Plant Diseases,” J. Artif. Intell. Eng. Appl., vol. 3, no. 1, pp. 498–503, 2023, doi: 10.59934/jaiea.v3i1.372.

[15] J. Ye, X. Cao, and Z. Guo, “Secure Marine Environment Communication: A Multiobject Authentication Protocol Based on Secret Sharing,” Int. J. Intell. Syst., vol. 2023, 2023, doi: 10.1155/2023/1814053.

[16] P. P. Vedanty, M. W. A. Kesiman, I. M. G. Sunarya, and I. G. A. A. D. Indradewi, “Identification of Leaf Diseases of Medicinal Plants Using K-Nearest Neighbor Based on Color, Texture, and Shape Features,” in 2023 10th International Conference on Advanced Informatics: Concept, Theory and Application (ICAICTA), IEEE, Oct. 2023, pp. 1–6. doi: 10.1109/ICAICTA59291.2023.10390034.

[17] A. J. Rozaqi and A. Sunyoto, “Identification of Disease in Potato Leaves Using Convolutional Neural Network (CNN) Algorithm,” in 2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020, 2020. doi: 10.1109/ICOIACT50329.2020.9332037.

[18] G. Dai, L. Hu, and J. Fan, “DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/6114061.

[19] M. V. Shewale and R. D. Daruwala, “High performance deep learning architecture for early detection and classification of plant leaf disease,” J. Agric. Food Res., vol. 14, 2023, doi: 10.1016/j.jafr.2023.100675.

[20] A. K. R. Abadio et al., “A pharmacophore-based virtual screening approach for the discovery of Trypanosoma cruzi GAPDH inhibitors (vol 5, pg 2019, 2013),” J Chem Inf Model, 2015, doi: Book_Doi 10.1002/9783527633326.

[21] S. Vaidyanathan et al., “A Novel 3-D Jerk System, Its Bifurcation Analysis, Electronic Circuit Design and a Cryptographic Application,” Electron., vol. 12, no. 13, 2023, doi: 10.3390/electronics12132818.

[22] S. H. P. Oo, N. D. Hung, and T. Theeramunkong, “Justifying Convolutional Neural Network with Argumentation for Explainability,” Inform., vol. 46, no. 9, 2022, doi: 10.31449/inf.v46i9.4359.

[23] P. Varshavskii, R. Alekhin, S. Polyakov, T. Blashonkov, and I. Mukhacheva, “Development of a Modular Case-Based Reasoning System for Data Analysis,” in Proceedings of the 2nd 2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2020, 2020. doi: 10.1109/REEPE49198.2020.9059242.

[24] Y. C. Tapidingan and D. Paseru, “Comparative Analysis of Classification Methods of KNN and Naïve Bayes to Determine Stress Level of Junior High School Students,” Indones. J. Inf. Syst., vol. 2, no. 2, pp. 80–89, Feb. 2020, doi: 10.24002/ijis.v2i2.3035.

[25] L. Ji, X. Zhang, Y. Zhao, and Z. Li, “Anomaly Detection of Dam Monitoring Data based on Improved Spectral Clustering,” J. Internet Technol., vol. 23, no. 4, 2022, doi: 10.53106/160792642022072304010.

[26] Y. Chi, Z. Yue, and Y. Lin, “Working Mode Recognition for SM4 Algorithm Based on Transformer,” Jisuanji Gongcheng/Computer Eng., vol. 49, no. 9, pp. 109 – 117, 2023, doi: 10.19678/j.issn.1000-3428.0065750.

[27] A. A. Sitorus, A. Ikhwan, and M. H. Aly, “Clothing Sales Prediction Information System Using Web-Based Double Exponential Smoothing Method,” Sci. J. Informatics, vol. 10, no. 3, pp. 371–387, 2023, doi: 10.15294/sji.v10i3.44919.

Downloads

Published

2025-02-28

How to Cite

Ikhwan, A., Bi Rahmani , N. A., H. Aly, M., Aslami, N., Dedi Irawan, M., & Ahmad, I. (2025). Application Of Expert System In Determining Diseases In Potato Plants. Indonesian Journal of Information Systems, 7(2), 194–203. https://doi.org/10.24002/ijis.v7i2.10213

Issue

Vol. 7 No. 2 (2025): February 2025

Section

Articles

License

Creative Commons License

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

Indonesian Journal of Information Systems as journal publisher holds copyright of papers published in this journal. Authors transfer the copyright of their journal by filling Copyright Transfer Form and send it to Indonesian Journal of Information Systems.

 
Creative Commons License
Indonesian Journal of Information Systems is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.