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All Journal TEKNIK INFORMATIKA JURNAL INSTEK (Informatika Sains dan Teknologi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Ilmiah Sinus SENSITEK Respati Journal of Computer System and Informatics (JoSYC) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Teknimedia: Teknologi Informasi dan Multimedia International Journal Artificial Intelligent and Informatics Jurnal Permata Indonesia Jurnal Rekayasa elektrika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Ferry Wahyu Wibowo
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology Nursing Public Health

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Journal : Journal of Computer System and Informatics (JoSYC)

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Knowledge-Based Decision Support System for Determining Types of Agricultural Crops According to Soil Conditions Wibowo, Ferry Wahyu; Sunyoto, Andi; Setiaji, Bayu; Wihayati, Wihayati
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6254

Abstract

Selecting the right crop for a particular land condition is one of the significant challenges in the agricultural sector. Each crop type has specific needs related to environmental factors such as soil type, pH, humidity, rainfall, and temperature. Mistakes in determining the appropriate crop type can result in decreased production, wastage of resources, and losses for farmers. This paper aims to determine the best model for use as a knowledge base to choose suitable plants for soil conditions. Machine learning algorithms were used to identify patterns of relationships between land conditions and the success of certain crop types to assist in selecting suitable crops and then made a knowledge-based decision support system. Algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) have been applied to solve this problem. In this paper, 30 experiments were conducted to test the stability of the model in determining suitable crops based on land conditions. The results of the experiments showed that the Support Vector Machine (SVM) has a more stable performance than other algorithms, with accuracy values of mean and standard deviation of 1 and 0, respectively.
Co-Authors Akhyari, Muhammad Wafa Aldy A Kulakat Ali Asgar Zainal Abidin Andi Sunyoto Arief Setyanto Arif Fajar Solikin Azis Wahyudi Bayu Setiaji Chavid Syukri Fatoni Darno - Ema Utami Fatoni, Chavid Syukri KUSRINI Kusrini - Kusrini Kusrini Kusrini Kusrini Kusrini, Kusrini Mohammad Diqi Prastowo, Wahit Desta Pratama, Rendy Bagus Rachmawati Oktaria Mardiyanto Sari, Yunita Sartika Solikin, Arif Fajar Suyoto, Andi Widhiarta, Widhiarta Wihayati, Wihayati