Fatma Sari Hutagalung
Universitas Muhammadiyah Sumatera Utara, Medan

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Perbandingan Algoritma MOORA dan Profile Matching pada Sistem Pemilihan Pupuk untuk Tanaman Porang Farid Akbar Siregar; Fatma Sari Hutagalung; Mhd Basri
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 1 (2023): September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6772

Abstract

Porang plants have long been utilized as a source of carbohydrates, fats, proteins, minerals, vitamins, and dietary fiber, which are exported as raw materials for various industries. In recent years, porang plants have become a highly profitable export commodity. One of the critical factors influencing porang plant production is fertilization. The timely, precise dosage, and correct method of fertilization determine the effectiveness of the fertilizer applied. MOORA considers various criteria in a balanced manner. Its ability to optimize the ratios between these criteria enables a more comprehensive selection of fertilizers. Profile Matching can be effective when specific criteria need to be emphasized over others. However, this method may not yield optimal decisions when several criteria carry significant weight. This research aims to determine the best type of fertilizer by applying a Decision Support System (DSS) and provides the benefit of assisting farmers in determining the most suitable fertilizer types for each phase of porang growth.In this study, a comparison was made between the MOORA and Profile Matching algorithms in the context of fertilizer selection for porang plants, using 7 fertilizer alternatives and 6 criteria types. Based on the research results, it can be concluded that both algorithms produce relatively similar outcomes, but the Profile Matching algorithm has a faster processing time compared to the MOORA algorithm in determining results. The contributions of this research include the development of a fertilizer selection system to help farmers optimize the growth and harvest of their crops. It also contributes to scientific literature and the comparison of algorithms, which can assist scientists and practitioners in selecting the most appropriate algorithms for similar problems in the future.