1Department of Informatics, Universitas Teknokrat Indonesia, Indonesia
2Department of Computer Science, Institut Pertanian Bogor, Indonesia
BibTex Citation Data :
@article{JTSISKOM13657, author = {Andi Nurkholis and Imas Sukaesih Sitanggang}, title = {Optimalisasi model prediksi kesesuaian lahan kelapa sawit menggunakan algoritme pohon keputusan spasial}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {8}, number = {3}, year = {2020}, keywords = {land suitability; palm oil; spatial decision tree; spatial join relation}, abstract = {Land suitability evaluation has a vital role in land use planning aimed to increase food production effectiveness. Palm oil is a leading and strategic commodity for Indonesian people, which is predicted consumption will exceed production in the future. This study aims to evaluate palm oil land suitability using a spatial decision tree algorithm that is conventional decision tree modification for spatial data classification with adding spatial join relation. The spatial dataset consists of eight explanatory layers (soil nature and characteristics), and a target layer (palm oil land suitability) in Bogor District, Indonesia. This study produced three models, where the best model was obtained based on optimizing accuracy (98.18 %) and modeling time (1.291 seconds). The best model has 23 rules, soil texture as the root node, two variables (drainage and cation exchange capacity) are uninvolved, with land suitability visualization obtains percentage S2 (29.94 %), S3 (53.16 %), N (16.57 %), and water body (0.33 %).}, issn = {2338-0403}, pages = {192--200} doi = {10.14710/jtsiskom.2020.13657}, url = {https://jtsiskom.undip.ac.id/article/view/13657} }
Refworks Citation Data :
Note: This article has supplementary file(s).
Article Metrics:
Last update:
Visualization of agroecological suitability of peatland to pineapple productivity in Kampar District with Fuzzy approach
Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm
Prediction Model for Soybean Land Suitability Using C5.0 Algorithm
Implementation of Fuzzy C-Means and Self-Organizing Map for Data Clustering of Palm Oil
Human Resource Intelligent Scheduling Algorithm Based on Decision Tree Algorithm
Improvement Design of Functional Modules of College English Network Teaching System Based on Decision Tree Algorithm
Last update: 2025-04-24 05:07:55
Starting from 2021, the author(s) whose article is published in the JTSiskom journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JTSiskom, the author(s) agree with this policy. No special document approval is required.
The author(s) guarantee that:
The author(s) retain all rights to the published work, such as (but not limited to) the following rights:
Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JTSiskom will not be held responsible for anything arising because of the writer's internal dispute. JTSiskom will only communicate with correspondence authors.
Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JTSiskom allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JTSiskom to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.
