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Journal : Science and Technology Indonesia

Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests Yulia Resti; Chandra Irsan; Muflika Amini; Irsyadi Yani; Rossi Passarella; Des Alwine Zayantii
Science and Technology Indonesia Vol. 7 No. 3 (2022): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3011.183 KB) | DOI: 10.26554/sti.2022.7.3.284-290

Abstract

Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.
Co-Authors ., Sutarno Abdul Wahid Sempurna Abdurahman Ade Iriani Sapitri Ade Murdiansyah Adiansyah Adiansyah Aditya Putra Perdana Prasetyo Ahmad Fadhil Ahmad Fali Oklilas Ahmad Heryanto Ahmad Rezqy FF Ahmad Rifai Ahmad Rifai Ahmad Rifai Ahmad Zarkasi Ambarwati, Ayu Annisa Darmawahyuni Arif Tumpal Leonardo Sianturi Atika Mailasari Ayu Ambarwati Bambang Tutuko Bangun Sudrajat Barzan Trio Putra Chandra Irsan Danny Matthew Saputra Dedy Kurniawan Deris Stiawan Des Alwine Zayantii Eka Fasilah Emaria Melati Erwin, Erwin Fatimah, Sayyidatina Febrina Hedy Anggraini Firdaus Firdaus Fri Murdiya Hani Alifia Mattjik Hendra Setiawan Holyness Nurdin Singadimedja Huda Ubaya Huda Ubaya Husnawati Husnawati Husnawati Husnawati Husnawati Husnawati Husnawati Iman Saladin B. Azhar Irsyadi Yani Izzati Millah Hanifah Kemahyanto Exaudi Kemahyanto Exaudi Muflika Amini Muhammad Ali Buchari Muhammad Fadli Muhammad Fadli Muhammad Naufal Rachmatullah Nabillah Selva Setiawan Nadya Lucyana Neni Frimayanti Osvari Arsalan Purwita Sari Purwita Sari Rahmad Fadli Isnanto Rahmat Fadli Isnanto Ranti Eftika Rendyansyah Reza Firsandaya Malik Rezqy FF, Ahmad Rian Rahmanda Putra Rido Zulfahmi Rifkie Primartha Roswitha Yemima Tiur Mediswati Rouzan Fiqri Abdullah Samsuryadi - Sarmayanta Sembiring Sayyidatina Fatimah Septa Rahmayuni Siti Nurmaini Sri Desy Siswanti Sri Desy Siswanti Sri Wahyuni Sutarno Sutarno - Sutarno . Sutarno Sutarno Sutarno Sutarno Sutarno Sutarno Sutarno, Sutarno Syahria Fardinelly Tarida Mathilda Tharisa Antya Perdani Titin Wahdania Tunnisa Wahyu Gunawan Winda Kurnia Yandi Prasetia Yandi Prasetia Yulia Resti Yusak Maryunianta Zahari Taha