Information System for Selecting the Best Oil Palm Seeds Based on Soil Quality and Climate Data Using the Topsis Method: Sistem Informasi Pemilihan Bibit Sawit Terbaik Berdasarkan Data Kualitas Tanah Dan klim Menggunakan Metode Topsis - Rizki Daufi Prastiwi; Wulan Andang Purnomo; Elinda Revita (Journal of Vocational Education and Information Technology (JVEIT), 2026)
Selecting oil palm seeds that suit soil quality and climate conditions is an important factor in increasing plantation productivity. However, the seed selection process is still largely based on experience, potentially resulting in decisions that are less objective and not in accordance with land characteristics. This study aims to develop a web-based decision support system to recommend the best oil palm seeds based on soil quality data and climate conditions using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. System development was carried out using the System Development Life Cycle (SDLC) method with a Waterfall model that includes problem identification, literature study, data collection, needs analysis, implementation of the TOPSIS method, application development using the CodeIgniter framework and MySQL database, and system testing. The TOPSIS method is implemented through the stages of decision matrix formation, normalization, criteria weighting, determining positive and negative ideal solutions, calculating the distance between each alternative, and determining preference values ​​to produce the best seed recommendations. The results showed that the system is able to process data on soil quality, rainfall, temperature, humidity, soil texture, and seed characteristics into objective seed recommendations based on TOPSIS preference values. System testing using the Blackbox Testing method showed that all application functions run according to functional requirements without any errors. The system developed is expected to help farmers and plantation managers in determining the oil palm seeds that are most suitable for land conditions more quickly, accurately, and objectively.