CLUSTERING DATA KOMPOSISI SAMPAH SELURUH INDONESIA MENGGUNAKAN METODE K-MEANS
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
IRSYAM, MUH ALIF
Luqman, Luqman
Karmila, sely
Luqman, Luqman
Karmila, sely
Subject
Teknik Informatika
Datestamp
2022-09-20 03:04:16
Abstract :
Waste has always been a problem in big cities in Indonesia. The volume of waste is increasing but the Final Disposal Site (TPA) is limited, of course, it becomes a problem if it is not handled carefully. So at this time the use of waste management can be used as an alternative as a primary energy source for power plants. Information technology has a very important role in the effort to achieve the target of New and Renewable Energy (EBT) that has been set by the government, especially processing the data generated during the energy supply chain process so that an efficient and optimal WTE model will be obtained in achieving the EBT target. In this case, the authors take advantage of working with supervisors and the Waste to Energy Lab of the PLN Institute of Technology to be able to make this happen by converting waste into New and Renewable Energy (EBT). The author obtained data on the composition of municipal waste throughout Indonesia and then clustered the data using the K-Means method. The results of clustering using this method are in the form of 3 clusters with the final result of the calculation as many as 101 cities for the first cluster, 163 cities for the second cluster, and 23 cities for the third cluster.
Waste has always been a problem in big cities in Indonesia. The volume of waste is increasing but the Final Disposal Site (TPA) is limited, of course, it becomes a problem if it is not handled carefully. So at this time the use of waste management can be used as an alternative as a primary energy source for power plants. Information technology has a very important role in the effort to achieve the target of New and Renewable Energy (EBT) that has been set by the government, especially processing the data generated during the energy supply chain process so that an efficient and optimal WTE model will be obtained in achieving the EBT target. In this case, the authors take advantage of working with supervisors and the Waste to Energy Lab of the PLN Institute of Technology to be able to make this happen by converting waste into New and Renewable Energy (EBT). The author obtained data on the composition of municipal waste throughout Indonesia and then clustered the data using the K-Means method. The results of clustering using this method are in the form of 3 clusters with the final result of the calculation as many as 101 cities for the first cluster, 163 cities for the second cluster, and 23 cities for the third cluster.
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