Penerapan Algoritma K-means dan Metode Elbow Untuk Clustering Tingkat Pencemaran Sampah Plastik pada Kabupaten/Kota di Seluruh Indonesia

Candra Darmawan (Universitas Papua, Indonesia)
Yesaya Setiyawan (Universitas Papua, Indonesia)
Rahmat Ady Prasetyo (Universitas Papua, Indonesia)
Sitti Khodijah Qurrota’Ayyun (Universitas Papua, Indonesia)

G-Tech : Jurnal Teknologi Terapan
Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024

Plastic waste is the most widely disposed of by humans, be it individuals, shops, and large companies. For this reason, data collection is needed in regions throughout Indonesia. From the data, it can be grouped what areas have a high, average, and low percentage of plastic waste. This can be done with data mining methods with one of the functions as clustering. The suitable algorithm to apply is K.means. In 2019, out of 242 districts/cities, there are 82 districts/cities cluster 0, 17 districts/cities cluster 1, and 143 districts/cities cluster 2. In 2020, out of 248 districts/cities, there are 73 districts/cities cluster 0, 25 districts/cities cluster 1, and 150 districts/cities cluster 2. In 2021, out of 236 districts/cities, there are 65 districts/cities cluster 0, 24 districts/cities cluster 1, and 147 districts/cities cluster 2. In 2022, there are 86 districts/cities cluster 0, 34 districts/cities cluster 1, and 191 districts/cities cluster 2.