Analisis Kesiapan Modernisasi Daerah Irigasi Kedung Putri pada Tingkat Sekunder Menggunakan Metode K-Medoids Clustering

https://doi.org/10.22146/agritech.41006

Ansita Gupitakingkin Pradipta(1*), Anditya Sridamar Pratyasta(2), Sigit Supadmo Arif(3)

(1) Departemen Teknik Pertanian dan Biosistem, Fakultas Teknologi Pertanian, Universitas Gadjah Mada, Jl. Flora No. 1, Bulaksumur, Yogyakarta 55281
(2) Departemen Teknik Pertanian dan Biosistem, Fakultas Teknologi Pertanian, Universitas Gadjah Mada, Jl. Flora No. 1, Bulaksumur, Yogyakarta 55281
(3) Departemen Teknik Pertanian dan Biosistem, Fakultas Teknologi Pertanian, Universitas Gadjah Mada, Jl. Flora No. 1, Bulaksumur, Yogyakarta 55281
(*) Corresponding Author

Abstract


Preparation for the modernization of the Kedung Putri Irrigation System (DI Kedung Putri) required a comprehensive assessment of the irrigation pillars, one of which was at the secondary level. To facilitate the assessment and development plan, a clustering was carried out using the k-medoids method, that used a representative data (called medoid) as the cluster center. Then, the decision making was conducted by using the Analytic Hierarchy Process (AHP) method. Performance assessment of 21 secondary channels was stated as the readiness index of irrigation modernization (IKMI). The assessment result showed that 9,52% included in good criteria, 71,43% included in fair criteria, and 19,05% included in poor criteria. Based on these results that DI Kedung Putri was not ready yet to be modernized. For this reason, it was necessary to conduct the system improvement in groups, namely by grouping based on similarities (clustering). The used method was k-medoids clustering using Rapid Miner 9.0 software. The clustering result showed that the optimal cluster number were 4 clusters, with the Davies Bouldin Index (DBI) value -1,959. The members of the 0, 1, 2 and 3 cluster were 6, 6, 8 and 1 secondary channels, respectively. Furthermore, the priority scale in clusters development was needed based on the performance of irrigation pillars on secondary channels. The results of AHP analysis showed that the order of priority development starts from cluster 0, followed by cluster 2, 1, and 3. The recommendations for the development of secondary channels incorporated in cluster, such as increasing water supply, routine infrastructure maintenance, technical assistance, and public campaigns in irrigation management. The secondary channel incorporated in cluster 3 had good performance on all pillars, so it only needed to maintain the existing operation and maintenance patterns.

Keywords


Analytic hierarchy process; k-medoids clustering; rapid miner; readiness index of irrigation modernization



References

Adiana, B. E., Soesanti, I., & Permanasari, A. E. (2018). Analisis Segmentasi Pelanggan Menggunakan Kombinasi RFM Model dan Teknik Clustering. JUTEI, 2(1), 23–32. https://doi.org/10.21460/jutei.2017.21.76.

Anonim. (2011). Pedoman Modernisasi Irigasi. Direktorat Irigasi dan Rawa Jakarta.

Bhat, A. (2014). K-Medoids Clustering Using Partitioning Around Medoids For Performing Face Recognition. International Journal of Soft Computing, Mathematics and Control, 3(3), 1–12. https://doi.org/10.14810/ijscmc.2014.3301.

Haas, R., & Meixner, O. (2005). An Illustrated Guide to The Analytic Hierarchy Process. Vienna: University of Natural Resources and Applied Life Science.

Han, J., Kamber, M., & Pei, J. (2001). Data Mining – Consepts and Techniques. USA: Morgan Kaufman.

Hardiani, T., Selo, S., & Hartanto, R. (2017). Segmentasi Nasabah Tabungan Menggunakan Model RFM (Recency , Frequency , Monetary) dan K-Means Pada Lembaga Keuangan Mikro. In Seminar Nasional Teknologi Informasi dan Komunikasi Terapan.

Jain, Y. K., & Bhandare, S. K. (2011). Min Max Normalization Based Data Perturbation Method for Privacy Protection. International Journal of Computer & Communication Technology, 2(VIII), 45–50.

Kaur, N. K., Kaur, U., & Singh, D. (2014). K-Medoid Clustering Algorithm- A Review. International Journal of Computer Application and Technology, 1(1), 42–45.

Mulyadi, Soekarno, I., & Winskayati. (2014). Analisis Pilar Modernisasi Irigasi dengan Pendekatan Analytical Hierarchy Process ( AHP ) pada Daerah Irigasi Barugbug - Jawa Barat. Jurnal Teoretis Dan Terapan Bidang Rekayasa Sipil, 21(3), 213–220.

Murtiningrum, Masithoh, R. E., & Jatmiko, M. W. (2007). Optimalisasi Penggunaan Pompa Dalam Sistem Irigasi Dengan Metode Analytical Hierarchy Process di Daerah Irigasi Pacal Kabupaten Bojonegoro. Agritech, 27(2), 48–58. https://doi.org/10.22146/agritech.9493

Nugraha, R. F. (2018). Pengelompokan dan Pemetaan Perkumpulan Petani Pemakai Air (P3A) Daerah Irigasi Kedung Serayu Berdasarkan Nilai Indeks Kesiapan Modernisasi Irigasi (IKMI) dengan Analisis Fuzzy C-Means (FCM). Universitas Gadah Mada.

Pramesti, D. F., Furqon, M. T., & Dewi, C. (2017). Implementasi Metode K-Medoids Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan / Lahan Berdasarkan Persebaran Titik Panas ( Hotspot ). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 1(9), 723–732.

Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal Services Sciences, 1(1). https://doi.org/10.1504/IJSSci.2008.01759

Soni, K. G., & Patel, A. (2017). Comparative Analysis of K-means and K-medoids Algorithm on IRIS Data. International Journal of Computational Intelligence Research, 13(5), 899–906.



DOI: https://doi.org/10.22146/agritech.41006

Article Metrics

Abstract views : 3651 | views : 3969

Refbacks

  • There are currently no refbacks.




Copyright (c) 2019 Ansita Gupitakingkin Pradipta, Anditya Sridamar Pratyasta, Sigit Supadmo Arif

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

agriTECH has been Indexed by:


agriTECH (print ISSN 0216-0455; online ISSN 2527-3825) is published by Faculty of Agricultural Technology, Universitas Gadjah Mada in colaboration with Indonesian Association of Food Technologies.


website statisticsView My Stats