Decision Support System for Plantation Land Recommendations in Mandailing Natal Regency Using The TOPSIS Method

Lidya Rosnita, Bustami Bustami, Dini Kairiyah Samosir, Hafizh Al Kausar Aidilof

Abstract


Plantations have great potential to be developed because they are a source of income for the community, farmers and PTPN in the area because Indonesia has the largest plantation land in the world. Mandailing Natal Regency is the district with the largest area in North Sumatra province, but Mandailing Natal has not been able to surpass crop production from plantation land in North Sumatra. The method for determining plantation land to produce good harvests is the TOPSIS algorithm for recommending plantation land. In this research, recommendations were made for plantation land with the aim of finding out what land is suitable in each subdistrict in Mandailing Natal Regency. The data method for this plantation land was taken at the Mandailing Natal Central Statistics Agency with the variables used, namely land area, area height, topography and rainfall. In implementing The decision support system using the TOPSIS algorithm on plantation land in Mandailing Natal Regency, the Recommendation results based on the type of plantation land in each sub-district are as follows: Rubber Plantation Land is highly recommended in Siabu District with a preference value of 0.56938682730811 and highly not recommended in Penyabungan District with preference value 0.33537499293319. Then, Palm Oil Plantation Land is highly recommended in Batang Natal sub-district with a preference value of 0.53467087652891 and not highly recommended in Tambangan sub-district with a preference value of 0.33181406496882. And the last one is Cocoa Farm which is highly recommended in Tambangan District with a preference value of 0.62855110465075 and highly not recommended in East Panyabungan District with a preference value of 0.25592982445435.

Keywords


Plantation; Recommendations; TOPSIS

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References


Abidin, N. A. S. Z., Avila, R. D., Hermatyar, A., & Rismayani, R. (2022). Comparison of K-Means and K-Medoids Algorithms for Grouping Cocoa Production Areas. In JUTISI (Jurnal Teknik Informatika dan Sistem Informasi). 8(2), 383-391. https://doi.org/10.28932/jutisi.v8i2.4897

Fadlisyah, F., Rosnita, L., & Pane, M. W. A. (2023). Decision Support System in Determining High School Student Specialization Using MOORA Method. In JICS (Journal of Informatics and Computer Science), 9(1), 65-70. https://doi.org/10.33143/jics.v9i1.2945

Fajriana, F. (2021). Analysis of the K-Medoids Algorithm in The Capture Fisheries Production Clustering System of North Aceh Regency. In JEPIN (Jurnal Edukasi dan Penelitian Informatika), 7(2), 263–269. https://dx.doi.org/10.26418/jp.v7i2.47795

Harahap, L. M., Fuadi, W., Rosnita, L., Darnila, E., & Meiyanti, R. (2022). Clustering of Featured Vegetables Using The K-Means Algorithm. In JUTISI (Jurnal Teknik Informatika dan Sistem Informasi), 8(3), 567-579. https://doi.org/10.28932/jutisi.v8i3.5277

Karlina, L., & Nurdiawan, O. (2023). Application of K-Medoids in Classifying The Distribution of Critical Land in West Java Based on District/City. In JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 527–532. https://doi.org/10.36040/jati.v7i1.6348

Khomsatun, K., Ikhsan, D., Ali, M., & Kursini, K. (2020). Decision Support System for Selecting Planting Areas in Wonosobo Regency Using K-Means Clustering and TOPSIS. In Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 9(1), 55. https://doi.org/10.23887/janapati.v9i1.23073

Marlina, D., Lina, N., Fernando, A., & Ramadhan, A. (2018). Implementation of K-Medoids and K-Means Algorithm for Grouping of Defect in Children. In Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi. 4(2), 64. https://doi.org/10.24014/coreit.v4i2.4498

Maulida, R. (2018). Application of Data Mining in Grouping Tourist Visits to Top Tourist Attractions in DKI Jakarta Province Using K-Means. In JISKA (Jurnal Informatika Sunan Kalijaga). 2(3), 167. https://doi.org/10.14421/jiska.2018.23-06

Mubarok, A., Suherman, H. D., Ramdhani, Y., Topiq, S. (2019). Decision Support System for Credit Granting Feasibility Using The TOPSIS Method. In Jurnal Informatika. 6(1), 37-46. https://doi.org/10.31311/ji.v6i1.4739

Risawandi, R., Rosnita, L., & Putra, R. K. (2023). Decision Support System for Predicting The Graduation of Informatics Students Using the SAW Method. In Journal of Informatics and Computer Science (JICS). 9(1), 58–64. https://doi.org/10.33143/jics.v9i1.2944

Rosnita, L., Yunizar, Z., & Ananda, E. F. (2024). Implementation of Data Mining in Determining Drug Purchase Patterns Using the Apriori Method. In Journal Serambi Engineering (JSE). 9(3), 9459-9466. https://doi.org/10.32672/jse.v9i3.1679

Sadli, M., Fajriana, F., Fuadi, W., Ermatita, E. & Pahendra, I. (2018). Application of The K-Nearest Neighbors Model in Classifying Electricity Demand for Each Area in Lhokseumawe City. In Jurnal ECOTIPE. 5(2), 11-18. https://doi.org/10.33019/ecotipe.v5i2.64668

Sulistyawati, A. A. D., & Sadikin, M. (2021). Application of the K-Medoids Algorithm to Determine Customer Segmentation. In Sistemasi, 10(3), 516. https://doi.org/10.32520/stmsi.v10i3.1332

Wahyuni, E. G. (2017). Decision Support System for Employee Recruitment Using The TOPSIS Method. In Jurnal Sains, Teknologi Industri, 14(1), 108–116.

Wira, B., Budianto, A. E., & Wiguna, A. S. (2019). Implementation of K-Medoids Clustering Method to Identify Study Program Selection Patterns of New Students in 2018 at Kanjuruhan University Malang. In RAINSTEK : Jurnal Terapan Sains & Teknologi, 1(3), 53–68. https://doi.org/10.21067/jtst.v1i3.304




DOI: https://doi.org/10.30596/jcositte.v6i1.22387

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