https://journal.uir.ac.id/index.php/ITJRD <p style="text-align: justify;"><span id="result_box" class="" lang="en"><strong>IT Journal Research and Development (ITJRD)</strong> is a <span class="">scientific journal</span> that was built by the Engineering Department of Informatics, <span class="">Riau Islamic University</span> <span class="">to</span> <span class="">provide</span> a means <span class="">for academics</span> <span class="">and</span> <span class="">researchers to</span> <span class="">publish papers</span> <span class="">and scientific works</span> <span class="">in the</span> <span class="">field of</span> <span class="">Information Technology</span>. <span class="">The scope of</span> this journal covers <span class="">research</span> in the field of informatics engineering, <span class="">computer science</span>, computer networks<span class="">, information systems</span>, <span class="">graphic design</span>, <span class="">image</span> and multimedia management. ITJRD is based journal OJS (Open Journal System) and has been indexed by <strong>Science and Technology Index (SINTA), BASE (Bielefeld Academic Search Engine), Google Scholar, Index Copernicus International (ICI), Indonesian Publication Index (IPI), Cosmos Impact Factor and a CrossRef Member</strong>. An indexing by other organizations is being <span class="">done</span> <span class="">in the nearest future</span><span class="">.</span></span></p> <p style="text-align: justify;"><span class="" lang="en"><span class=""><strong>ACCREDITED by Ministry of Research, Technology, and Higher Education of the Republic of Indonesia,&nbsp;No.14/E/KPT/2019, Mei 10, 2019</strong></span></span></p> UIR PRESS en-US IT Journal Research and Development 2528-4061 <!--p><span class="tlid-translation translation" lang="id"><span class="" title="">Ini adalah jurnal akses terbuka yang berarti bahwa semua konten tersedia secara gratis tanpa biaya kepada pengguna atau lembaganya.</span> <span class="" title="">Hak cipta dalam teks artikel individu (termasuk artikel penelitian, artikel opini, dan abstrak) adalah milik penulis masing-masing, tunduk pada lisensi Creative Commons CC-BY-SA yang diberikan kepada semua orang lain.</span> <span class="" title="">ITJRD memungkinkan penulis untuk memegang hak cipta tanpa batasan dan memungkinkan penulis untuk mempertahankan hak penerbitan tanpa batasan.</span></span></p--> <p>This is an open access journal which means that all content is freely available without charge to the user or his/her institution. The copyright in the text of individual articles (including research articles, opinion articles, and abstracts) is the property of their respective authors, subject to a Creative Commons CC-BY-SA licence granted to all others.&nbsp;ITJRD allows the author(s) to hold the copyright without restrictions and allows the author to retain publishing rights without restrictions.</p> <p>&nbsp;</p> https://journal.uir.ac.id/index.php/ITJRD/article/view/13595 <p>The allocated area for soybean cultivation has been gradually decreasing, leading to a decline in both production and productivity. Consequently, the current level of soybean production and productivity falls short of meeting the demand within the community. One potential solution to augment soybean output and efficiency involves allocating specific parcels of land for soybean cultivation. It is essential to conduct land evaluations tailored to soybean cultivation, accounting for the land's inherent potential, in order to optimize land utilization. Thus, a comprehensive system is required to assess land suitability, particularly for soybean cultivation, and employ the results of this classification as recommendations for land allocation. This research employess combination the Classification and Regression Tree (CART) method and the Artificial Bee Colony (ABC) algorithm to classify suitable land for soybean cultivation. CART is used for classification and ABC is utilized for feature selection to identify the most relevant attributes in case of the algorithm improvement. Through a series of iterative experiments involving 5, 10, 25, 50, 75, and 100 iterations, the best attribute was determined following three attempts at each iteration. The Confusion Matrix test yielded an accuracy rate of 94.22% for the CART method in the second experiment, while the combined use of the best ABC and CART combination resulted in an accuracy rate of 97.11%. Therefore, it can be concluded that the integration of the artificial bee colony (ABC) algorithm with the classification and regression tree (CART) method outperforms the sole use of the CART method in terms of accuracy.</p> Rusdi Efendi Mochammad Yusa Stefani Tasya Hallatu Copyright (c) 2024 Rusdi Efendi, Mochammad Yusa, Stefani Tasya Hallatu https://creativecommons.org/licenses/by-sa/4.0 2024-06-13 2024-06-13 9 1 1 13 10.25299/itjrd.2024.13595