K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters
The Special Region of Yogyakarta (DIY) has rivers that cross rural and urban areas that are still used by the community and industry. However, cases of river water pollution in DIY are a major issue in 2021. It is very important to classify rivers according to class so that further analysis and acti...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | Indonesian |
| Published: |
Universitas Muhammadiyah Purwokerto
2023-05-01
|
| Series: | Jurnal Informatika |
| Subjects: | |
| Online Access: | https://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/16986 |
| _version_ | 1797831022669201408 |
|---|---|
| author | M. Andang Novianta Syafrudin Syafrudin Budi Warsito |
| author_facet | M. Andang Novianta Syafrudin Syafrudin Budi Warsito |
| author_sort | M. Andang Novianta |
| collection | DOAJ |
| description | The Special Region of Yogyakarta (DIY) has rivers that cross rural and urban areas that are still used by the community and industry. However, cases of river water pollution in DIY are a major issue in 2021. It is very important to classify rivers according to class so that further analysis and action can be carried out. This study conducted a grouping analysis of rivers in DIY based on water quality parameters such as Total Suspended Solid (TSS), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Phosphate, Fecal Coli, and Total Coliform. The grouping method uses the K-means algorithm. The data source is secondary data from the DIY Provincial Environment and Forestry Service. The data is in the form of 56 river samples observed in November 2020. The description of the data shows that the average of the 56 river water samples is 24.95 for TSS, 8.84 for DO, 4.33 for BOD5, 20.36 for COD, 0 .54 for Phosphate, 22.820 for Fecal Coli, and 59.210 for Total Coliform. The results of grouping with k=6 are the best compared to k = 2, 3, 4, 5, 7, and 8. The number of members in this grouping is n1 = 14, n2 = 1, n3 = 1, n4 = 5, n5 = 18, and n6 = 17. The cluster that has the highest average TSS, BOD, and COD values is the 3rd cluster (Rivers in Bantul and Sleman Regencies). The cluster that has the highest DO value is the 6th cluster (Rivers in Bantul Regency). The cluster that has the highest average Phosphate value is the 2nd cluster (Rivers in Bantul, Sleman, and Gunungkidul Regencies). The cluster that has the highest average Fecal Coli and Total Coliform values are the 4th cluster (Rivers in Bantul Regency, Yogyakarta City, and Sleman Regency). |
| first_indexed | 2024-04-09T13:46:21Z |
| format | Article |
| id | doaj.art-cba171c0f42a4317a1623fabf1691bee |
| institution | Directory Open Access Journal |
| issn | 2086-9398 2579-8901 |
| language | Indonesian |
| last_indexed | 2024-04-09T13:46:21Z |
| publishDate | 2023-05-01 |
| publisher | Universitas Muhammadiyah Purwokerto |
| record_format | Article |
| series | Jurnal Informatika |
| spelling | doaj.art-cba171c0f42a4317a1623fabf1691bee2023-05-09T04:33:03ZindUniversitas Muhammadiyah PurwokertoJurnal Informatika2086-93982579-89012023-05-0111115516310.30595/juita.v11i1.169865282K-Means Clustering for Grouping Rivers in DIY based on Water Quality ParametersM. Andang Novianta0Syafrudin Syafrudin1Budi Warsito2<p style="text-align: justify;" align="center"><em>Institut Sains & Teknologi AKPRIND Yogyakarta</em></p><span lang="EN-AU">Diponegoro University</span><span></span>Diponegoro UniversityThe Special Region of Yogyakarta (DIY) has rivers that cross rural and urban areas that are still used by the community and industry. However, cases of river water pollution in DIY are a major issue in 2021. It is very important to classify rivers according to class so that further analysis and action can be carried out. This study conducted a grouping analysis of rivers in DIY based on water quality parameters such as Total Suspended Solid (TSS), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Phosphate, Fecal Coli, and Total Coliform. The grouping method uses the K-means algorithm. The data source is secondary data from the DIY Provincial Environment and Forestry Service. The data is in the form of 56 river samples observed in November 2020. The description of the data shows that the average of the 56 river water samples is 24.95 for TSS, 8.84 for DO, 4.33 for BOD5, 20.36 for COD, 0 .54 for Phosphate, 22.820 for Fecal Coli, and 59.210 for Total Coliform. The results of grouping with k=6 are the best compared to k = 2, 3, 4, 5, 7, and 8. The number of members in this grouping is n1 = 14, n2 = 1, n3 = 1, n4 = 5, n5 = 18, and n6 = 17. The cluster that has the highest average TSS, BOD, and COD values is the 3rd cluster (Rivers in Bantul and Sleman Regencies). The cluster that has the highest DO value is the 6th cluster (Rivers in Bantul Regency). The cluster that has the highest average Phosphate value is the 2nd cluster (Rivers in Bantul, Sleman, and Gunungkidul Regencies). The cluster that has the highest average Fecal Coli and Total Coliform values are the 4th cluster (Rivers in Bantul Regency, Yogyakarta City, and Sleman Regency).https://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/16986k-means clusteringrivers classifyingwater quality |
| spellingShingle | M. Andang Novianta Syafrudin Syafrudin Budi Warsito K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters Jurnal Informatika k-means clustering rivers classifying water quality |
| title | K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters |
| title_full | K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters |
| title_fullStr | K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters |
| title_full_unstemmed | K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters |
| title_short | K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters |
| title_sort | k means clustering for grouping rivers in diy based on water quality parameters |
| topic | k-means clustering rivers classifying water quality |
| url | https://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/16986 |
| work_keys_str_mv | AT mandangnovianta kmeansclusteringforgroupingriversindiybasedonwaterqualityparameters AT syafrudinsyafrudin kmeansclusteringforgroupingriversindiybasedonwaterqualityparameters AT budiwarsito kmeansclusteringforgroupingriversindiybasedonwaterqualityparameters |