Penerapan Internet of Things (IoT) dengan Pendekatan Metode Inverse Distance Weight (IDW)
Keywords:
Internet of Things, Inverse Distance Weight
Abstract
Penerapan Internet of Things (IoT) dengan Pendekatan Metode Inverse Distance Weight (IDW) merupakan sebuah upaya inovatif dalam menggabungkan teknologi IoT dengan analisis spasial yang memanfaatkan metode IDW. IoT adalah konsep di mana berbagai perangkat fisik dihubungkan ke internet untuk mengumpulkan dan berbagi data secara real-time, sementara IDW adalah teknik analisis spasial yang digunakan untuk menginterpolasi data berdasarkan jarak dan pola spasial. Penerapan kombinasi antara IoT dan IDW memiliki berbagai potensi aplikasi yang sangat luas, termasuk pemantauan lingkungan, manajemen sumber daya alam, prediksi cuaca lokal, atau bahkan perencanaan kota cerdas. Dengan memanfaatkan data real-time yang diperoleh melalui IoT dan kemampuan IDW untuk memahami pola spasial, inovasi ini dapat membantu pengambil keputusan dalam berbagai sektor untuk membuat keputusan yang lebih tepat dan efisien.
Dalam buku ini, studi kasus yang akan diuraikan adalah tentang pemanfaatan teknologi IoT dan metode IDW untuk memperkirakan tingkat pH air di sumur warga. Buku ini akan menjelaskan cara merancang IoT dan cara metode IDW menganalisis data pH air. Penjelasan mencakup berbagai aspek, termasuk arsitektur, teknik pengambilan data, rumus, dan program yang digunakan. Melalui penerapan Internet of Things dengan Pendekatan Metode Inverse Distance Weight (IDW), kita memasuki era di mana teknologi dan analisis spasial berkolaborasi untuk menghadirkan solusi yang lebih cerdas dan terarah dalam mengatasi berbagai tantangan di dunia nyata. Dengan memanfaatkan data dan teknologi ini, kita dapat meningkatkan pemahaman kita tentang lingkungan sekitar dan membuat keputusan yang lebih baik dalam berbagai aspek kehidupan kita.
-edisi Pertama – Purwokerto: UHB Press, 2023
viii + 83 hlm, 1 Jil: 23 cm
ISBN: In Process
References
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Handiani, D. N., & Heriati, A. (2020). Analisis Sebaran Parameter Kualitas Air dan Indeks Pencemaran di Perairan Teluk Parepare-Sulawesi Selatan. Jurnal Ilmu Lingkungan, 18(2), 272–282. https://doi.org/10.14710/jil.18.2.272-282
Hermawan, A. P., Kim, D. S., & Lee, J. M. (2020). Sensor Failure Recovery using Multi Look-back LSTM Algorithm in Industrial Internet of Things. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2020-Septe, 1363–1366. https://doi.org/10.1109/ETFA46521.2020.9212123
https://ppiig.ulm.ac.id/wp-content/uploads/2019/02/How-Inverse-Distance-Weighted-Works.pdf
Inas Fikri, N., Louis Nathaniel, V., Syahrul Gunawan, M., & Abuzairi, T. (2021). Design of Real-Time Aquarium Monitoring System for Endemic Fish on the Smartphone. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 7(2), 269. https://doi.org/10.26555/jiteki.v7i2.21137
Juliyanto, M. A., Sulistiyowati, I., Ahfas, A., Juliyanto, M. A., Sulistiyowati, I., & Ahfas, A. (2023). Design of Turbine Aerator with Remote Control and Internet of Things (IoT)-Based Water pH Monitoring "Design of Turbine Aerator with Remote Control and Internet of Things-Based Water pH Monitoring Design of Turbine Aerator with Remote Control and Internet. Buletin Ilmiah Sarjana Teknik Elektro, 5(1), 156–166. https://doi.org/10.12928/biste.v5i1.7863
Khouni, I., Louhichi, G., & Ghrabi, A. (2021). Environmental Technology & Innovation Use of GIS based Inverse Distance Weighted interpolation to assess surface water quality : Case of Wadi El Bey , Tunisia. 24.
Krishna, S., & Tv, S. (2020). IoT based Water Parameter Monitoring System. Icces, 1299–1303.
Kshirsagar, R., Mudhalwadkar, R. P., & Kalaskar, S. (2019). Design and development of IoT based water quality measurement system. Proceedings of the International Conference on Trends in Electronics and Informatics, ICOEI 2019, Icoei, 1199–1202. https://doi.org/10.1109/ICOEI.2019.8862663
Lakshmikantha, V., Hiriyannagowda, A., Manjunath, A., Patted, A., Basavaiah, J., & Anthony, A. A. (2021). IoT based smart water quality monitoring system. Global Transitions Proceedings, 2(2), 181–186. https://doi.org/10.1016/j.gltp.2021.08.062
Li, Z. (2019). Integrating data-to-data correlation into inverse distance weighting. 14–17.
Lin, L., Yang, H., & Xu, X. (2022). Effects of Water Pollution on Human Health and Disease Heterogeneity: A Review. Frontiers in Environmental Science, 10(June). https://doi.org/10.3389/fenvs.2022.880246
Lyons, K. J., Ikonen, J., Hokajärvi, A. M., Räsänen, T., Pitkänen, T., Kauppinen, A., Kujala, K., Rossi, P. M., & Miettinen, I. T. (2023). Monitoring groundwater quality with real-time data, stable water isotopes, and microbial community analysis: A comparison with conventional methods. Science of the Total Environment, 864(December 2022). https://doi.org/10.1016/j.scitotenv.2022.161199
Nistor, M. M., Rahardjo, H., Satyanaga, A., Hao, K. Z., Xiaosheng, Q., & Sham, A. W. L. (2020). Investigation of groundwater table distribution using borehole piezometer data interpolation: Case study of Singapore. Engineering Geology, 271(August 2019), 105590. https://doi.org/10.1016/j.enggeo.2020.105590
Oguz, E. A., Depina, I., Myhre, B., Devoli, G., Rustad, H., & Thakur, V. (2022). IoT-based hydrological monitoring of water-induced landslides: a case study in central Norway. Bulletin of Engineering Geology and the Environment, 81(5). https://doi.org/10.1007/s10064-022-02721-z
Oppus, C., Guico, M. L., Monje, J. C., Domingo, M. A. L. G. A., Ngo, G., Retirado, M. G., & Kwong, J. C. (2020). Remote and Real-time Sensor System for Groundwater Level and Quality. 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020, 152–155. https://doi.org/10.1109/ECICE50847.2020.9301948
Perveen, S., & Amar-Ul-Haque. (2023). Drinking water quality monitoring, assessment and management in Pakistan: A review. Heliyon, 9(3), e13872. https://doi.org/10.1016/j.heliyon.2023.e13872
Prasetya, D. A., Nguyen, P. T., Faizullin, R., Iswanto, I., & Armay, F. (2020). Resolving the Shortest Path Problem using the Haversine Algorithm. 7(1), 62–64.
Qi, C., Huang, S., & Wang, X. (2020). Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN. 8. https://doi.org/10.1109/ACCESS.2020.3030878
Saini, J., Dutta, M., & Marques, G. (2020). Internet of Things Based Environment Monitoring and PM10Prediction for Smart Home. 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020, 3–7. https://doi.org/10.1109/3ICT51146.2020.9311996
Shukla, K., Kumar, P., Mann, G. S., & Khare, M. (2020). Mapping spatial distribution of particulate matter using Kriging and Inverse Distance Weighting at supersites of megacity Delhi. Sustainable Cities and Society, 54(November 2019), 101997. https://doi.org/10.1016/j.scs.2019.101997
Tahama, K., Baride, A., Gupta, G., Erram, V. C., & Baride, M. V. (2022). HydroResearch Spatial variation of sub-surface heterogenieties within the dyke swarm of Nandurbar region , Maharashtra , India , for groundwater exploration using Inverse Distance Weighted technique. HydroResearch, 5, 1–12. https://doi.org/10.1016/j.hydres.2021.12.001
Tsai, J. C., Leu, J. S., Prakosa, S. W., Hsiao, L. C., Huang, P. C., Yang, S. Y., & Huang, Y. T. (2021). Design and Implementation of an Internet of Healthcare Things System for Respiratory Diseases. Wireless Personal Communications, 117(2), 337–353. https://doi.org/10.1007/s11277-020-07871-5
Yang, W., Zhao, Y., Wang, D., Wu, H., Lin, A., & He, L. (2020). Using principal components analysis and idw interpolation to determine spatial and temporal changes of Surfacewater quality of Xin’Anjiang river in huangshan, china. International Journal of Environmental Research and Public Health, 17(8), 1–14. https://doi.org/10.3390/ijerph17082942
Zhang, J. (2022). Water Quality Substance Detection System Based on Internet of Things. Security and Communication Networks, 2022. https://doi.org/10.1155/2022/2815078
African, S., & Farm, A. (2021). South African Aquaculture Farm. 1–19.
Agricultural, F. A. O., & Bulletin, S. (2023). for Rural Areas. Telecommunications Policy, 40(8), 755–773.
Almaviva, S., Artuso, F., Giardina, I., Lai, A., & Pasquo, A. (2022). Fast Detection of Different Water Contaminants by Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy. Sensors, 22(21). https://doi.org/10.3390/s22218338
Almetwally, S. A. H., Hassan, M. K., & Mourad, M. H. (2020). Real Time Internet of Things (IoT) Based Water Quality Management System. Procedia CIRP, 91(March 2023), 478–485. https://doi.org/10.1016/j.procir.2020.03.107
Barzegar, M., Blanks, S., Gharehdash, S., & Timms, W. (2023). Development of IOT-based low-cost MEMS pressure sensor for groundwater level monitoring. Measurement Science and Technology, 34(11), 115103. https://doi.org/10.1088/1361-6501/ace78f
Calderwood, A. J., Pauloo, R. A., Yoder, A. M., & Fogg, G. E. (2020). Low-cost, open source wireless sensor network for real-time, scalable groundwater monitoring. Water (Switzerland), 12(4), 1–17. https://doi.org/10.3390/W12041066
Celicourt, P., Gumiere, S. J., Lafond, J. A., Gumiere, T., Gallichand, J., & Rousseau, A. N. (2020). Automated Mapping of Water Table for Cranberry Subirrigation Management : Comparison of Three Spatial Interpolation Methods. 1–18. https://doi.org/10.3390/w12123322
Chen, C., Wu, Y., Zhang, J., & Chen, Y. (2022). IoT-Based Fish Farm Water Quality Monitoring System.
Chowdury, M. S. U., Emran, T. Bin, Ghosh, S., Pathak, A., Alam, M. M., Absar, N., Andersson, K., & Hossain, M. S. (2019). IoT based real-time river water quality monitoring system. Procedia Computer Science, 155, 161–168. https://doi.org/10.1016/j.procs.2019.08.025
Chuyen, T. D., Nguyen, D. D., Cuong, N. C., & Thong, V. V. (2023). Design and manufacture control system for water quality based on IoT technology for aquaculture in the Vietnam. Bulletin of Electrical Engineering and Informatics, 12(4), 1893–1900. https://doi.org/10.11591/eei.v12i4.5180
Delgado, A., Fernandez, A., Lozano, E., Miguel, D., León, F., Arteta, J., & Carbajal, C. (2021). Applying Grey Systems and Inverse Distance Weighted Method to Assess Water Quality from a River. 12(11), 614–623.
Dewana, B. R., Prasetyo, S. Y. J., & Hartomo, K. D. (2022). Comparison of IDW and Kriging Interpolation Methods Using Geoelectric Data to Determine the Depth of the Aquifer in Semarang, Indonesia. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 8(2), 215. https://doi.org/10.26555/jiteki.v8i2.23260
Dialaksito, F. M., & S, P. P. (2023). Design of Water PH Quality Monitoring System in PT SIER Industrial Area Based on Internet of Things at Waste Water Treatment Plant. 2(2), 7–22.
Drage, J., & Kennedy, G. (2020). Building a Low-Cost, Internet-of-Things, Real-Time Groundwater Level Monitoring Network. Groundwater Monitoring and Remediation, 40(4), 67–73. https://doi.org/10.1111/gwmr.12408
Edition, T., & First, I. T. H. E. (2008). Guidelines for Drinking-water Quality. 1.
Forum, I., & Engineering, E. (2020). $ GHVLJQ RI KLJK OHYHO ZDWHU WDQN PRQLWRULQJ V \ VWHP EDVHG RQ , QWHUQHW RI WKLQJV. 769–774. https://doi.org/10.1109/IFEEA51475.2020.00163
Georgescu, P. L., Moldovanu, S., Iticescu, C., Calmuc, M., Calmuc, V., Topa, C., & Moraru, L. (2023). Assessing and forecasting water quality in the Danube River by using neural network approaches. Science of the Total Environment, 879(January), 162998. https://doi.org/10.1016/j.scitotenv.2023.162998
Gonzaga, B. A., Alves, D. L., Albuquerque, M. D. G., Espinoza, J. M. D. A., Almeida, L. P., & Weschenfelder, J. (2020). Development of a Low-cost Ultrasonic Sensor for Groundwater Monitoring in Coastal Environments: Validation using Field and Laboratory Observations. Journal of Coastal Research, 95(sp1), 1001–1005. https://doi.org/10.2112/SI95-195.1
Handiani, D. N., & Heriati, A. (2020). Analisis Sebaran Parameter Kualitas Air dan Indeks Pencemaran di Perairan Teluk Parepare-Sulawesi Selatan. Jurnal Ilmu Lingkungan, 18(2), 272–282. https://doi.org/10.14710/jil.18.2.272-282
Hermawan, A. P., Kim, D. S., & Lee, J. M. (2020). Sensor Failure Recovery using Multi Look-back LSTM Algorithm in Industrial Internet of Things. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2020-Septe, 1363–1366. https://doi.org/10.1109/ETFA46521.2020.9212123
https://ppiig.ulm.ac.id/wp-content/uploads/2019/02/How-Inverse-Distance-Weighted-Works.pdf
Inas Fikri, N., Louis Nathaniel, V., Syahrul Gunawan, M., & Abuzairi, T. (2021). Design of Real-Time Aquarium Monitoring System for Endemic Fish on the Smartphone. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 7(2), 269. https://doi.org/10.26555/jiteki.v7i2.21137
Juliyanto, M. A., Sulistiyowati, I., Ahfas, A., Juliyanto, M. A., Sulistiyowati, I., & Ahfas, A. (2023). Design of Turbine Aerator with Remote Control and Internet of Things (IoT)-Based Water pH Monitoring "Design of Turbine Aerator with Remote Control and Internet of Things-Based Water pH Monitoring Design of Turbine Aerator with Remote Control and Internet. Buletin Ilmiah Sarjana Teknik Elektro, 5(1), 156–166. https://doi.org/10.12928/biste.v5i1.7863
Khouni, I., Louhichi, G., & Ghrabi, A. (2021). Environmental Technology & Innovation Use of GIS based Inverse Distance Weighted interpolation to assess surface water quality : Case of Wadi El Bey , Tunisia. 24.
Krishna, S., & Tv, S. (2020). IoT based Water Parameter Monitoring System. Icces, 1299–1303.
Kshirsagar, R., Mudhalwadkar, R. P., & Kalaskar, S. (2019). Design and development of IoT based water quality measurement system. Proceedings of the International Conference on Trends in Electronics and Informatics, ICOEI 2019, Icoei, 1199–1202. https://doi.org/10.1109/ICOEI.2019.8862663
Lakshmikantha, V., Hiriyannagowda, A., Manjunath, A., Patted, A., Basavaiah, J., & Anthony, A. A. (2021). IoT based smart water quality monitoring system. Global Transitions Proceedings, 2(2), 181–186. https://doi.org/10.1016/j.gltp.2021.08.062
Li, Z. (2019). Integrating data-to-data correlation into inverse distance weighting. 14–17.
Lin, L., Yang, H., & Xu, X. (2022). Effects of Water Pollution on Human Health and Disease Heterogeneity: A Review. Frontiers in Environmental Science, 10(June). https://doi.org/10.3389/fenvs.2022.880246
Lyons, K. J., Ikonen, J., Hokajärvi, A. M., Räsänen, T., Pitkänen, T., Kauppinen, A., Kujala, K., Rossi, P. M., & Miettinen, I. T. (2023). Monitoring groundwater quality with real-time data, stable water isotopes, and microbial community analysis: A comparison with conventional methods. Science of the Total Environment, 864(December 2022). https://doi.org/10.1016/j.scitotenv.2022.161199
Nistor, M. M., Rahardjo, H., Satyanaga, A., Hao, K. Z., Xiaosheng, Q., & Sham, A. W. L. (2020). Investigation of groundwater table distribution using borehole piezometer data interpolation: Case study of Singapore. Engineering Geology, 271(August 2019), 105590. https://doi.org/10.1016/j.enggeo.2020.105590
Oguz, E. A., Depina, I., Myhre, B., Devoli, G., Rustad, H., & Thakur, V. (2022). IoT-based hydrological monitoring of water-induced landslides: a case study in central Norway. Bulletin of Engineering Geology and the Environment, 81(5). https://doi.org/10.1007/s10064-022-02721-z
Oppus, C., Guico, M. L., Monje, J. C., Domingo, M. A. L. G. A., Ngo, G., Retirado, M. G., & Kwong, J. C. (2020). Remote and Real-time Sensor System for Groundwater Level and Quality. 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020, 152–155. https://doi.org/10.1109/ECICE50847.2020.9301948
Perveen, S., & Amar-Ul-Haque. (2023). Drinking water quality monitoring, assessment and management in Pakistan: A review. Heliyon, 9(3), e13872. https://doi.org/10.1016/j.heliyon.2023.e13872
Prasetya, D. A., Nguyen, P. T., Faizullin, R., Iswanto, I., & Armay, F. (2020). Resolving the Shortest Path Problem using the Haversine Algorithm. 7(1), 62–64.
Qi, C., Huang, S., & Wang, X. (2020). Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN. 8. https://doi.org/10.1109/ACCESS.2020.3030878
Saini, J., Dutta, M., & Marques, G. (2020). Internet of Things Based Environment Monitoring and PM10Prediction for Smart Home. 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020, 3–7. https://doi.org/10.1109/3ICT51146.2020.9311996
Shukla, K., Kumar, P., Mann, G. S., & Khare, M. (2020). Mapping spatial distribution of particulate matter using Kriging and Inverse Distance Weighting at supersites of megacity Delhi. Sustainable Cities and Society, 54(November 2019), 101997. https://doi.org/10.1016/j.scs.2019.101997
Tahama, K., Baride, A., Gupta, G., Erram, V. C., & Baride, M. V. (2022). HydroResearch Spatial variation of sub-surface heterogenieties within the dyke swarm of Nandurbar region , Maharashtra , India , for groundwater exploration using Inverse Distance Weighted technique. HydroResearch, 5, 1–12. https://doi.org/10.1016/j.hydres.2021.12.001
Tsai, J. C., Leu, J. S., Prakosa, S. W., Hsiao, L. C., Huang, P. C., Yang, S. Y., & Huang, Y. T. (2021). Design and Implementation of an Internet of Healthcare Things System for Respiratory Diseases. Wireless Personal Communications, 117(2), 337–353. https://doi.org/10.1007/s11277-020-07871-5
Yang, W., Zhao, Y., Wang, D., Wu, H., Lin, A., & He, L. (2020). Using principal components analysis and idw interpolation to determine spatial and temporal changes of Surfacewater quality of Xin’Anjiang river in huangshan, china. International Journal of Environmental Research and Public Health, 17(8), 1–14. https://doi.org/10.3390/ijerph17082942
Zhang, J. (2022). Water Quality Substance Detection System Based on Internet of Things. Security and Communication Networks, 2022. https://doi.org/10.1155/2022/2815078
