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Abstrak :

Pertumbuhan penduduk yang terjadi di D.I Yogyakarta meningkatkan jumlah limbah yang dibuang sehingga mempengaruhi kualitas air sungai. Selain itu, pertumbuhan populasi juga berkontribusi pada perubahan penutup lahan di daerah aliran sungai yang kemudian akan berdampak pada kualitas air sungai. Tujuan penelitan ini adalah untuk menganalisis variabilitas spasial dan temporal kualitas air pada Sungai Code di Provinsi D.I Yogyakarta, selain itu mengetahui tren perubahan penutup lahan di Daerah Tangkapan Air (DTA) Sungai Code serta hubungannya dengan perubahan kualitas air sungai, dan mengidentifikasi serta menganalisis parameter kualitas air dominan yang  mempengaruhi variabilitas kualitas air Sungai Code secara keseluruhan. Data yang di analisis adalah data sekunder yang berasal dari Pemerintah Provinsi D.I Yogyakarta yang terbatas pada periode pemantauan 2011-2017. Metodologi yang dilakukan adalah analisis deskriptif untuk menganalisis variasi spasial dan temporal kualitas air secara visual, kemudian melakukan klasifikasi penutup lahan untuk Daerah Tangkapan Air (DTA) Sungai Code dengan software ArcGIS 10.3 serta menganalisis hubungan perubahannya dengan perubahan kualitas air Sungai Code, dan menganalisis variabilitas data kualitas air dengan Cluster analysis (CA) & Principal Component Analysis (PCA) menggunakan software IBM SPSS. Hasil penelitian menunjukan terdapat parameter yang konsentrasinya mediannya memiliki tren positif untuk variasi spasial dari hulu ke hilir, diantaranya: suhu, TDS, BOD, COD, Nitrat, Nitrit, Seng, Tembaga, Timbal, Bakteri koli tinja, selain itu terdapat juga parameter yang konsentrasi mediannya memiliki tren positif untuk variasi temporal dari tahun 2011-2017, diantaranya: pH, TDS, Nitrat, Nitrit, Deterjen, Seng, Warna. Terdapat juga parameter-parameter yang perlu diperhatikan karena konsentrasinya sebagian besar sudah melebihi baku mutu kelas 3 dan/atau kelas 4 yang ditetapkan dalam Pergub DIY No. 20 Tahun 2008 tentang Baku Mutu Air, diantaranya: BOD, Nitrit, Tembaga, Bakteri koli tinja. Hasil korelasi penutup lahan dan kualitas air mendapatkan bahwa penutup lahan vegetasi alami dapat memberikan dampak positif terhadap perubahan kualitas air Sungai Code, sedangkan penutup lahan agrikultur dan area bangunan cenderung memberikan dampak negatif terhadap perubahan kualitas air Sungai Code. Untuk CA, paramater kualitas air Sungai Code berhasil dikelompokan menjadi 4 cluster, diantaranya: kelompok pencemar nuterien, kelompok pencemar logam, kelompok pencemar materi organik dan kelompok pencemar grey water. Analisis komponen utama (PCA) menghasilkan empat komponen utama (PC1, PC2, PC3, PC4) yang mempunyai variansi secara berturut-turut 22,815%, 17,631%, 16,016%, 12,806% dan menjelaskan total 69,268% variabilitas data kualitas air Sungai Code. PC1 diinterpretasikan sebagai pencemar materi organik Sungai Code. PC2 diinterpretasikan sebagai pencemar nutrien dan kontaminasi tinja pada Sungai Code yang dapat berasal dari limbah dan air limpasan agrikultur. PC3 diinterpretasikan sebagai pencemar logam Sungai Code yang dapat berasal dari air limpasan urban dan limbah domestik. PC4 dapat diinterpretasikan sebagai dampak pencemaran grey water pada Sungai Code.

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Population growth that occurred in D.I Yogyakarta increased the amount of waste discharged so that it affected river water quality. In addition, population growth also contributes to changes in land cover in watersheds which will then have an impact on river water quality. The purpose of this research is to analyze the spatial and temporal variability of water quality in the River Code, in addition to knowing trends in land cover changes in the Code watershed and its relationship to changes in river water quality, and analyzing dominant water quality parameters that affect Code River water quality variability as a whole. This research using secondary data from the Provinsi D.I Yogyakarta, which limited to the 2011-2017 monitoring period. The methodology used is descriptive analysis to analyze spatial and temporal variations in water quality, then classify land cover in Code watershed with ArcGIS 10.3 software and analyze the relationship of changes with changes in Code River water quality, analyze variability of water quality data with Cluster analysis (CA) & Principal Component Analysis (PCA) using IBM SPSS software. The results showed there are parameters whose median concentration has a positive trend for spatial variations from upstream to downstream, including: temperature, TDS, BOD, COD, Nitrate, Nitrite, Zinc, Copper, Lead, Fecal Coli Bacteria. There are also concentration parameters the median has a positive trend for temporal variations from 2011-2017, including: pH, TDS, Nitrate, Nitrite, Detergent, Zinc, Color. There are also parameters that need to be considered because most of the concentrations have exceeded the grade 3 and / or grade 4 water quality standards stipulated in Pergub DIY No. 20 Tahun 2008, including: BOD, Nitrite, Copper, fecal coli bacteria. The correlation results between land cover and water quality found that natural vegetation land cover can have a positive impact on changes in Code River water quality, while agricultural and building areas land cover tend to have a negative impact on changes in Code River water quality. For CA, the Code River water quality parameters were grouped into 4 clusters, including: the nutrient polluter group, the metal polluter group, the organic material polluter group and the gray water polluter group. Principal component analysis (PCA) produced four main components (PC1, PC2, PC3, PC4) which had variances respectively 22.815%, 17.631%, 16.016%, 12.806% and explained a total of 69.268% variability of Code River water quality data. PC1 is interpreted as organic matter pollutans in Code River. PC2 is interpreted as nutrients pollutants and fecal contamination in the Code River which may come from agricultural waste and runoff. PC3 is interpreted as a Code River metal pollutant that may come from urban runoff water and domestic waste. PC4 may interpreted as the impact of gray water pollution on the Code River.

Abstrak :
Cluster analysis is a mult ivariate analysis technique used to classify objects such that the objects in a cluster are very similar and the objects in different clusters are quite different. This study will discuss the non-hierarchical clustering methods. The methods are C-Means Cluster and Fuzzy C-Means Cluster. These methods are suitable for large data and continuous variables. This study would also present the application of the methods on the case of village grouping according to the underdevelopment status in two regions of level II (Kota Metro and Kabupaten Lampung Timur) in Lampung Province. The unit of observations in this study are 257 villages in Kota Metro (22 villages) and Kabupaten Lampung Timur in Lampung Province obtained from the Village Potential Statistics (Podes - Potensi Desa) 2008. The results show that the optimal cluster in Kota Lampung data is 4, with a minimum value of the Fukuyama-Sugeno validity index is at -45.4649. As for the data of Kabupaten Lampung Timur, theoptimumnumber ofclustersis13,with aminimum value of the Fukuyama-Sugeno validity index is at 196.9629.

Abstrak :
Kota Pekalongan terletak di pesisir utara Pulau Jawa dan sering mengalami genangan pasang air laut (rob). Pada wilayah yang tergenang rob tersebut dilakukan analisis kerentanan wilayah sebagai dasar mitigasi bencana. Analisis kerentanan wilayah dalam penelitian ini berdasarkan tingkat bahaya dan kondisi sosial-ekonomi. Analisis kerentanan wilayah yang digunakan pada penelitian ini adalah analisis cluster metode K-means dimana unit analisisnya adalah batas administratif RW. Kerentanan wilayah sedang di daerah penelitian mendominasi daerah penelitian dengan kondisi bahaya rob tinggi dan kerentanan sosialekonomi rendah. Kerentanan wilayah terhadap banjir rob di Kota Pekalongan cenderung lebih tinggi yang berada di dekat dengan laut dan sungai. Hal ini dikarenakan laut dan sungai sebagai jalur utama masuknya air laut dan kondisisosial ekonominya pun rendah. ......Pekalongan city is located at the north coast of Java and often had coastal inundation. Vulnerable analaysis were conducted on the inundated areas as a base for disaster mitigation proses. Vulnerability analyses in the research area are based on the level of prone areas and socio-economic conditions. The vulnerability analysis method used in this research is K-means cluster method with RW boundary as units analysis. The results of the analysis that the vulnerability of the region is dominated area of research with high rob hazardous conditions and low socio-economic vulnerability. Vulnerability to flooding areas in Pekalongan rob tends to be higher near the sea and rivers. This is because the seas and rivers as the main line entry of sea water and socio-economic conditions were low.