Case-Based Reasoning Diagnosis Penyakit Pada Tanaman Kelapa Sawit

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Published May 13, 2024
https://doi.org/10.32722/multinetics.v10i1.6602
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Vol. 10 No. 1 (2024): MULTINETICS May (2024)

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Tursina .
Universitas Tanjungpura
Nella Yuliarni
a:1:{s:5:"en_US";s:23:"Universitas Tanjungpura";}
Rina Septiriana
Universitas Tanjungpura

Abstract

Oil palm cultivation is one of the main agricultural commodities in Indonesia, as oil palm plants have a relatively high economic value. However, oil palm cultivation is vulnerable to disease attacks that can threaten productivity. The lack of knowledge among farmers in managing plantations and addressing these problems can exacerbate damage to plants due to disease attacks. To address this issue, this research will develop an application using a Case-Based Reasoning (CBR) approach to diagnose diseases in oil palm plants with the nearest neighbor calculation algorithm. CBR will solve new problems by reusing knowledge to solve old problems that are similar and already have solutions. The nearest neighbor algorithm is useful for calculating similarity between new cases and old cases. The data for the research involves 7 types of diseases, 20 symptoms, and 345 cases of disease attacks on oil palm plants. The application testing uses 25 test cases and 320 case bases. From the test results, the application shows an accuracy rate of 31.00, with the highest similarity value reaching 0.98. In this research, it can be seen that the generated similarity values are high but the accuracy value is low. This can be caused by several factors, such as the data of the cases, feature representation, and insufficient diagnosis, which result in high similarity values due to the assignment of values to attribute categories and attribute value proximity.

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How to Cite
., T., Yuliarni, N., & Septiriana, R. (2024). Case-Based Reasoning Diagnosis Penyakit Pada Tanaman Kelapa Sawit . MULTINETICS , 10(1), 10–20. https://doi.org/10.32722/multinetics.v10i1.6602

References

  1. Juliardi, S. E., & Fachrudin, H. T. (2022). Penilaian Perkebunan Kelapa Sawit. Merdeka Kreasi Group.
  2. Fauzi, Y., Widyastuti, Y. E., Satyawibawa, I., & Paeru, R. H. (2012). Kelapa sawit. Penebar Swadaya Grup.
  3. Rasywir, E., Sinaga, R., & Pratama, Y. (2020). Evaluasi pembangunan sistem pakar penyakit tanaman sawit dengan metode deep neural network (DNN). Jurnal media informatika budidarma, 4(4), 1206-1215.
  4. Kamus Besar Bahasa Indonesia (KBBI), “Penyakit”. [Online]. Available : https://kbbi.kemdikbud.go.id/entri/penyakit. [Accessed, 17 April 2023].
  5. Sihaloho, T. P., Tarigan, W., Siallagan, S., & Simbolon, F. H. (2022). Model Case Based Reasoning Dalam Mendiagnosa Penyakit Kelapa Sawit. Jurnal Mnemonic, 5(2), 178-183.
  6. Riyanto, I. R. (2021). Diagnosa Penyakit Hepatitis Menggunakan Metode Sorensen Coefficient. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 8(3), 1307-1321.
  7. Pal, S. K., Dillon, T. S., & Yeung, D. S. (Eds.). (2012). Soft computing in case based reasoning. Springer Science & Business Media.
  8. López, B. (2022). Case-based reasoning: a concise introduction. Springer Nature.
  9. Warman, I. (2019, March). Implementation of case-based reasoning and nearest neighbor similarity for peanut disease diagnosis. In Journal of Physics: Conference Series (Vol. 1196, No. 1, p. 012053). IOP Publishing.
  10. Kusrini & Emha Taufiq Luthfi. (2009). Algoritma Data Mining. Yogyakarta : C.V Andi Offset.
  11. Sulistiani, H., Darwanto, I., & Ahmad, I. (2020). Penerapan Metode Case Based Reasoning dan K-Nearest Neighbor untuk Diagnosa Penyakit dan Hama pada Tanaman Karet. JEPIN (Jurnal Edukasi Dan Penelitian Informatika), 6(1), 23-28.
  12. Aldo, D., Nur, Y. S. R., Lanyak, A. C. F., Hulqi, F. Y. A., & Hikmah, R. N. (2022). Penerapan Metode Case Base Reasoning Dalam Diagnosa Penyakit dan Hama pada Tanaman Hortikultura. Building of Informatics, Technology and Science (BITS), 4(2), 1111-1122.
  13. Adawiyah, R. (2018). Case based reasoning diagnosis hama dan Penyakit Tanaman Nilam. INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi, 2(1), 57-67.
  14. Ikhsan, Z., Yaherwandi, Y., Efendi, S., Rezki, D., Umami, I. M., & Suhendra, D. (2020). Pemberdayaan masyarakat Nagari Silago Kabupaten Dharmasraya melalui teknologi budidaya tanaman kelapa sawit. Jurnal Hilirisasi IPTEKS, 3(1), 10-19.
  15. Merawati, N. L. P., & Hartati, S. (2018). Sistem rekomendasi topik skripsi menggunakan metode case based reasoning. Jurnal Ilmiah Teknologi Infomasi Terapan, 4(3).
  16. Vedayoko, L. G., Sugiharti, E., & Muslim, M. A. (2017). Expert system diagnosis of bowel disease using case based reasoning with nearest neighbor algorithm. Scientific Journal of Informatics, 4(2), 134-142.
  17. Simon, H., Simon, M., & Ajayi, I. H. (2019). Artificial intelligence model for control of plants diseases using case-based reasoning with nearest neighbor technique. Int J Eng Sci Comput, 9(9), 23780-23784.
  18. Warman, I. (2019, March). Implementation of case-based reasoning and nearest neighbor similarity for peanut disease diagnosis. In Journal of Physics: Conference Series (Vol. 1196, No. 1, p. 012053). IOP Publishing.
  19. Zhai, Z., Martínez, J. F., Martínez, N. L., & Díaz, V. H. (2020). Applying case-based reasoning and a learning-based adaptation strategy to irrigation scheduling in grape farming. Computers and Electronics in Agriculture, 178, 105741.
  20. Smiti, A., & Nssibi, M. (2020). Case Based Reasoning Framework for COVID-19 Diagnosis. Ingénierie des Systèmes d Inf., 25(4), 469-474.