Article Open Access

Design of a Fuzzy Mamdani Based Decision Support System for Fair Wage Determination of Palm Oil Workers

(1) * Zuheri Lubis Mail (Universitas Islam Negeri Sumatera Utara, Medan, 20235, Indonesia)
(2) Sriani Sriani Mail (Universitas Islam Negeri Sumatera Utara, Medan, 20235, Indonesia)
*Corresponding author

Abstract


Wage determination in palm-oil plantations often relies on subjective assessments, which may lead to inconsistencies, unfair practices, and disputes between workers and management. To address this issue, this study proposes the application of the Fuzzy Mamdani method as a decision support system for determining plantation workers’ wages. The main objective is to design and implement a fuzzy-based model that provides fair and transparent wage recommendations by integrating multiple worker performance criteria. The research was conducted at PT CSIL with a sample of 45 workers. Four key variables length of service, attendance, overtime, and work performance were formulated into linguistic variables with triangular membership functions. A fuzzy rule base, developed in collaboration with domain experts, was used to perform Mamdani inference with centroid defuzzification. Data were obtained from company records and validated using comparisons with the actual wage distribution determined by the payroll department. The results show that the system recommendations were consistent with the company’s wage decisions for 40 out of 45 workers, representing an agreement rate of 88.9%. This demonstrates that the Fuzzy Mamdani method can effectively reduce subjectivity and improve fairness in wage determination. In conclusion, the proposed system provides a reliable tool to support payroll decision-making in plantation settings. The implications suggest that broader adoption of fuzzy-based models can enhance transparency and worker satisfaction in agricultural wage systems.

Keywords


Fuzzy Mamdani; Decision Support System; Worker Wage; and Palm Oil

   

DOI

https://doi.org/10.33122/ejeset.v6i2.1059
      

Article metrics

Abstract views : 89 | PDF views : 13

   

Cite

   

Full Text

Download

References


Agusantia, D., & Juandi, D. (2022). Kemampuan Penalaran Analogi Matematis di Indonesia: Systematic Literature Review. Symmetry: Pasundan Journal of Research in Mathematics Learning and Education, 7(2), 222–231. https://doi.org/10.23969/symmetry.v7i2.6436

Ahmed, M., & Ahmed, A. (2023). Palm tree disease detection and classification using residual network and transfer learning of Inception ResNet. Plos One, 18(3), e0282250. https://doi.org/10.1371/journal.pone.0282250

Asrianda, A., Aidilof, H. A. K., & Pangestu, Y. (2021). Machine Learning for Detection of Palm Oil Leaf Disease Visually using Convolutional Neural Network Algorithm. Journal of Informatics and Telecommunication Engineering, 4(2), 286–293. https://doi.org/10.31289/jite.v4i2.4185

Ardhana, V. Y. P., Sampetoding, E. A. M., Kumoro, D. T., & Alamsyah, N. (2022). Model berbasis Fuzzy Tsukamoto untuk perhitungan besaran gaji dosen pada perguruan tinggi swasta. Jurnal Sistem Komputasi dan Informatika (JSON), 3(3), 311–318. https://doi.org/10.30865/json.v3i3.3856

Akyuz, E. (2021). Application of Fuzzy FMEA to perform an extensive risk analysis in maritime transportation engineering. International Journal of Maritime Engineering, 159(A1). https://doi.org/10.5750/ijme.v159iA1.1013

Fajriyati, S., & Yusuf, M. (2025). The Bibliometric Analysis of Fraud Reporting: Scientific Publications from 2019 to 2024. TOFEDU: The Future of Education Journal, 4(1), 7–19. https://doi.org/10.61445/tofedu.v4i1.365

Fakharudin, P. R. A. F., & Avianto, D. (2024). Fuzzy Mamdani untuk Pemerataan Gaji Karyawan. SISTEMASI: Jurnal Sistem Informasi, 13(1), 305. https://doi.org/10.32520/stmsi.v13i1.3621

Ferdinandi, C. M., & Kiwonde, F. M. (2023). The influence of educational leadership on students’ academic performance in secondary schools: A case of Itilima district. Electronic Journal of Education, Social Economics and Technology, 4(1), 16–21. https://doi.org/10.33122/ejeset.v4i1.100

Gunawan, G., Zarlis, M., & Sihombing, P. (2024). Smart agriculture model in detecting oil palm plantation diseases using a convolution neural network. IAES International Journal of Artificial Intelligence (IJ-AI), 13(3), 3164–3171. https://doi.org/10.11591/ijai.v13.i3.pp3164-3171

Hak, F., Guimaraes, T., & Santos, M. (2022). Towards effective clinical decision support systems: A systematic review. PLoS ONE, 17(8 August), 1–19. https://doi.org/10.1371/journal.pone.0272846

Herpratiwi, H., Maftuh, M., Firdaus, W., Tohir, A., Daulay, M. I., & Rahim, R. (2022). Implementation and Analysis of Fuzzy Mamdani Logic Algorithm from Digital Platform and Electronic Resource. TEM Journal, 11(3), 1028–1033. https://doi.org/10.18421/TEM113-06

Hidayatullah, C., R, R. K., & Armansyah, A. (2024). Pencarian Rute Terpendek Dalam Pendistribusian Darah di Palang Merah Indonesia (PMI) dengan Algoritma Dijkstra. TIN: Terapan Informatika Nusantara, 4(11), 727–738. https://doi.org/10.47065/tin.v4i11.5028

Khairunnisa, N., Muhammad Faiz Assariy, Daffa Zulqisthi, Prasetya, M. R., Madania, K. F., Holy Nurani Rabbina, Oktavia, R., Raihana, S. H., Alzahra, A. V. A., & Chika Hayya Sabillah. (2025). Determining The Amount Of Tempeh Production Based On Demand And Inventory Data Using Fuzzy Logic Method (Case Study: “Murni” Soybean Tempe Msmes MADIUN). Journal of Applied Science, Technology & Humanities, 2(3), 402–415. https://doi.org/10.62535/7pbt8t33

Kurniadi, D., Nuraeni, F., & Jaelani, D. (2022). Implementasi logika Fuzzy Mamdani pada sistem prediksi calon penerima Program Keluarga Harapan. Jurnal Algoritma, 19(1), 160–171. https://doi.org/10.33364/algoritma/v.19-1.1016

Marsono, Nasyuha, A. H., Syahra, Y., & Mariami, I. (2024). Decision Support System for Selecting Online Teaching Methods Using the Fuzzy MCDM Algorithm. Sinkron: Jurnal dan Penelitian Teknik Informatika, 8(3), 1495–1504. https://doi.org/10.33395/sinkron.v8i3.13731

Muñoz-Valero, D., García-Hernández, L., & Herrera, F. (2025). A knowledge-driven fuzzy logic framework for supporting decision-making. Computers & Industrial Engineering, 193, 110755. https://doi.org/10.1016/j.cie.2024.110755

Murad, F. A., & Kurniawan, A. (2024). Design of a wireless fidelity network using the Open Shortest Path First protocol in the Computer and Network Engineering Research Room at Politeknik Negeri Jakarta. Electronic Journal of Education, Social Economics and Technology, 5. https://doi.org/10.33122/ejeset.v5i2.340

Pranata, E. H., Susanto, T., Rikendry, R., & Puspaningrum, A. S. (2023). Pengendalian Gerak Longitudinal Pesawat Fixed Wing FT-Explorer. Jurnal Teknik dan Sistem Komputer, 4(1), 20–28. https://doi.org/10.33365/jtikom.v4i1.3504

Pribadi, A., & Ade Kurniawan. (2022). Deteksi Penyakit Sawit Menggunakan Metode Deep Learning. Jurnal Sains dan Ilmu Terapan, 5(2), 72–76. https://doi.org/10.59061/jsit.v5i2.86

Ramadani, F. Q., Panggabean, M., & Laksono, D. A. (2024). Perancangan Aplikasi Pengaduan Masyarakat untuk Kantor Dinas Pekerjaan Umum dan Perumahan Rakyat Kabupaten Langkat Sumatera Utara. Masharif al-Syariah, 9(3), 1557–1565. https://doi.org/10.30651/jms.v9i3.23196

Rasywir, E., Sinaga, R., & Pratama, Y. (2020). Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN). Paradigma - Jurnal Komputer dan Informatika, 22(2), 117–123. https://doi.org/10.31294/p.v22i2.8907

Samuel, S., Prilianti, K. R., Setiawan, H., & Mimboro, P. (2022). Metode Deteksi Pokok Pohon Secara Automatis pada Citra Perkebunan Sawit Menggunakan Model Convolutional Neural Network (CNN) pada Perangkat Lunak Sistem Informasi Geografis. Jurnal Teknologi Informasi dan Ilmu Komputer, 9(7), 1689–1698. https://doi.org/10.25126/jtiik.2022976772

Sapra, J., & Mathur, I. (2020). Effect of Dimensions of Workplace Spiritualism: Meaningful Work, Sense of Community, Organizational Values and Compassion with Reference to Job Satisfaction. Electronic Journal of Education, Social Economics and Technology, 1(1), 10–17. https://doi.org/10.33122/ejeset.v1i1.5

Sapuguh, I., Ahlina, N., Wahyudi, A., Setyawan, B., & Rosalinda, A. S. (2024). Development of fuzzy logic based student performance prediction system. Jurnal Teknik Informatika C.I.T Medicom, 15(6), 284–290. https://doi.org/10.35335/cit.Vol15.2024.714.pp284-290

Saputra, M. A., & Nurhaida, I. (2024). Signature originality verification using a deep learning approach. Electronic Journal of Education, Social Economics and Technology, 5(1), Article 310. https://doi.org/10.33122/ejeset.v5i1.310

Satia, G. A. W., Firmansyah, E., & Umami, A. (2022). Perancangan sistem identifikasi penyakit pada daun kelapa sawit (Elaeis guineensis Jacq.) dengan algoritma deep learning convolutional neural networks. Jurnal Ilmiah Pertanian, 19(1), 1–10. https://doi.org/10.31849/jip.v19i1.9556

Siti Wardah, Ahmad Syahdiyin, & Mohammad Amin. (2021). Peramalan Pengadaan Bahan Baku Kelapa Sawit Dengan Meggunakan Metode Fuzzy Time Series Di Pt X.Co. Juti Unisi, 5(1), 27–33. https://doi.org/10.32520/juti.v5i1.2930

Styorini, W., Putra, W. E., Khabzli, W., & Triyani, Y. (2022). Penerapan Deep Learning Pada Jenis Penyakit Tanaman Kelapa Sawit Menggunakan Algoritma Convolutional Neural Network. Jurnal Komputer Terapan, 8(2), 359–367. https://doi.org/10.35143/jkt.v8i2.5522

Sutisna, M. G., Yudono, M. A. S., Artiyasa, M., Narputo, P., & Jakfar, A. E. (2024). Sistem pendukung keputusan tingkat stres mahasiswa dengan Fuzzy Mamdani. RIGGS: Journal of Artificial Intelligence and Digital Business, 4(1), 403. https://doi.org/10.31004/riggs.v4i1.403

Wibowo, F., & Aryanto, D. (2025). Prototype model sistem pendukung keputusan berbasis Fuzzy Logic metode Mamdani untuk pemilihan lulusan terbaik di Universitas Muhammadiyah Purwokerto. JUITA: Jurnal Informatika, 3(3), 868. https://doi.org/10.30595/juita.v3i3.868

Wicaksono, W., Prilianti, K. R., Setiawan, H., & Mimboro, P. (2022). Perkebunan Kelapa Sawit Dengan Penginderaan Jauh. 03(November), 135–142. https://doi.org/10.26858/jessi.v3i2.38092

Wu, H., & Xu, Z. S. (2021). Fuzzy logic in decision support: Methods, applications and future trends. International Journal of Computers Communications & Control, 16(1), Article 4044. https://doi.org/10.15837/ijccc.2021.1.4044


Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Zuheri Lubis, Sriani

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0