Performance Index Model of Raw Water Infrastructure

Sri U. Sudiarti, Ussy Andawayanti, Lily M. Limantara, Hari Siswoyo

Abstract


This research intends to build a performance index model of raw water infrastructure mathematically by considering technical, non-technical, and environmental aspects. The research location is in Lombok and the Sumbawa Islands. Data is collected by field surveys and questionnaires that are distributed to 160 respondents related to raw water infrastructure in 21 locations. The methodology consists of Partial Least Squares (PLS) and Generalized Reduced Gradient (GRG). The results show that technical, non-technical, and environmental aspects have a significant influence on the performance index of raw water infrastructure. The structural analysis shows that the technical, non-technical, and environmental variables have a positive and significant influence on the performance index. The performance index of raw water infrastructure is successful enough to be developed and tested by using field data and GRG. The evaluation result shows that the model gives an accurate estimation of raw water infrastructure performance in Nusa Tenggara Barat province. The performance index model for raw water infrastructure is as follows: 0.521 IKTK + 0.305 IKNT + 0.174 IK Liwith the sum of square residual (SSR) is 83.21, the root mean square error (RMSE) is 0.44, the mean square error (MSE) is 3.97, and the accuracy level is 95.25%. This research provides the development of an evaluation method for raw water infrastructure performance and a valuable outlook for policymakers in managing and maintaining raw water infrastructure to support sustainable water resources in the future. Considering some aspects of this, it is hoped the efforts to increase the quality of raw water infrastructure can be more directed and effective, contributing to increasing society's prosperity and a sustainable environment in the region.

 

Doi: 10.28991/CEJ-2024-010-06-014

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Keywords


Performance Index; Variable; Technic; Non-Technic; Environment.

References


Bina Operasi dan Pemeliharaan, Direktorat Jenderal Sumber Daya Air Kementerian Pekerjaan Umum dan Perumahan Rakyat, 2022. Petunjuk Teknis Penilaian Kinerja Jaringan Air Baku.

Raihan, A., Pereira, J. J., Begum, R. A., & Rasiah, R. (2023). The economic impact of water supply disruption from the Selangor River, Malaysia. Blue-Green Systems, 5(2), 102–120. doi:10.2166/bgs.2023.031.

Molinos-Senante, M., Maziotis, A., Sala-Garrido, R., & Mocholi-Arce, M. (2023). Assesing the influence of environmental variables on the performance of water companies: An efficiency analysis tree approach. Expert Systems with Applications, 212, 118844. doi:10.1016/j.eswa.2022.118844.

Ibrahim, A., Ismail, A., Juahir, H., Iliyasu, A. B., Wailare, B. T., Mukhtar, M., & Aminu, H. (2023). Water quality modelling using principal component analysis and artificial neural network. Marine Pollution Bulletin, 187. doi:10.1016/j.marpolbul.2022.114493.

Odwori, E. O. (2022). Adapting Strategies for Water Supply Management to Climate Change in Nzoia River Basin, Kenya. Asian Journal of Environment & Ecology, 24–52. doi:10.9734/ajee/2022/v18i130304.

Yousefi, P., Shabani, S., Mohammadi, H., & Naser, G. (2017). Gene Expression Programing in Long Term Water Demand Forecasts Using Wavelet Decomposition. Procedia Engineering, 186, 544–550. doi:10.1016/j.proeng.2017.03.268.

Nasution, A., Helard, D., & Indah, S. (2021). Kajian Kinerja Pengelolaan Sistem Penyediaan Air Minum (SPAM) di Kabupaten Solok dan Kota Solok Berbasis Buku Kinerja Badan Peningkatan Penyelenggaraan Sistem Penyediaan Air Minum. Cived, 8(3), 213. doi:10.24036/cived.v8i3.115792.

Suprayitno, M., Kusumastuti, D. I., & Wahono, E. P. (2021). Evaluasi kinerja PDAM Tirta Jasa di Kabupaten Lampung Selatan. REKAYASA: Jurnal Ilmiah Fakultas Teknik Universitas Lampung, 25(2), 36–41. doi:10.23960/rekrjits.v25i2.39.

Suprayogi, H., Bisri, M., Limantara, L. M., & Andawayanti, U. (2018). Service index modeling of urban drainage network. International Journal of GEOMATE, 15(50), 95–100. doi:10.21660/2018.50.04204.

Susilo, H., Purwantoro, D., & Rahadiansyah, S. (2021). Model Performance Index of Ground Water Irrigation Systems in the Karst Mountain Region: Case Study in Gunung Kidul Regency, Yogyakarta. IOP Conference Series: Earth and Environmental Science, 641(1), 012014. doi:10.1088/1755-1315/641/1/012014.

Noviadriana, D., Andawayanti, U., Juwono, P. T., & Sisinggih, D. (2019). Service index indicator of polder system with retention pond using PCA method. International Journal of Recent Technology and Engineering, 8(3), 7577–7583. doi:10.35940/ijrte.C6159.098319.

Kurniawan, T., Bisri, M., Juwono, P. T., Suhartanto, E., Tohari, A., & Riandasenya, S. A. R. (2022). Performance Index Model of River and Infrastructure. Journal of Hunan University Natural Sciences, 49(2), 111–122. doi:10.55463/issn.1674-2974.49.2.11.

Purwantoro, D., Limantara, L. M., Soetopo, W., & Solichin, M. (2020). Sabo Dam Infrastructure System Performance Index Model in Mount Merapi. Technology Reports of Kansai University, 62(10), 6151–6164.

Bakti, L. M., Juwono, P. T., Dermawan, V., Wijatmiko, I., Kurniawan, T., & Tohari, A. (2023). Irrigation Performance Index Model (Case Study in IPDMIP). Journal of Hunan University Natural Sciences, 50(4), 1-11. doi:10.55463/issn.1674-2974.50.4.1.

Liu, B., Mohandes, M., Nuha, H., Deriche, M., Fekri, F., & McClellan, J. H. (2022). A Multitone Model-Based Seismic Data Compression. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(2), 1030–1040. doi:10.1109/tsmc.2021.3077490.

Hair Jr., J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications, Thousand Oaks, United States.

J Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2), 220–221. doi:10.1080/1743727x.2015.1005806.

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. doi:10.2307/3151312.

Moore, D. S., McCabe, G. P., & Craig, B. A. (2009). Introduction to the Practice of Statistics. WH Freeman New York, United States.

David, G. K., Lawrence L.K., dan Keith, E. M. (1988). Applied regression analysis and other multivariable methods. Duxbury Press, Grove, United States.

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623. doi:10.7717/peerj-cs.623.


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DOI: 10.28991/CEJ-2024-010-06-014

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