ABSTRAKMetode agglomerative hierarchical clustering merupakan metode analisis cluster yang bertujuan untuk mengelompokkan objek-objek berdasarkan karakteristik yang dimilikinya, yang dimulai dengan objek-objek individual sampai objek-objek tersebut bergabung menjadi satu cluster tunggal. Metode agglomerative hierarchical clustering terbagi menjadi beberapa algoritma, di antaranya metode single linkage, complete linkage, average linkage, dan ward. Penelitian ini membandingkan keempat metode dalam agglomerative hierarchical clustering dengan tujuan untuk mendapatkan solusi cluster terbaik dalam kasus pengklasifikasian kabupaten/kota di Provinsi Jawa Timur berdasarkan kualitas pelayanan Keluarga Berencana (KB).Hasil penelitian menunjukkan bahwa berdasarkan hasil uji validitas cluster, diketahui bahwa metode average linkage memberikan solusi cluster yang lebih baik bila dibandingkan dengan metode agglomerative hierarchical clustering lainnya (single linkage, complete linkage, dan ward). Solusi cluster pada metode average linkage menghasilkan 4 cluster dengan karakteristik yang berbeda. Cluster 1 memiliki karakteristik tingkat kualifikasi klinik KB dan tingkat kompetensi tenaga pelayanan KB "sangat rendah". Cluster 2 memiliki karakteristik tingkat kualifikasi klinik KB "cukup baik", dan tingkat kompetensi tenaga pelayanan KB "rendah". Cluster 3 memiliki karakteristik tingkat kualifikasi klinik KB "rendah" dan tingkat kompetensi tenaga pelayanan KB "sedang". Cluster 4 terdiri dari empat kabupaten dengan karakteristik tingkat kualifikasi klinik KB "sedang" dan tingkat kompetensi tenaga pelayanan KB "cukup baik". Kata kunci: analisis cluster, agglomerative hierarchical clustering, uji validitas cluster, Keluarga Berencana (KB)ABSTRACT Agglomerative hierarchical clustering methods is cluster analysis method whose primary purpose is to group objects based on its characteristics, it begins with the individual objects until the objects are fused into a single cluster. Agglomerative hierarchical clustering methods are divided into single linkage, complete linkage, average linkage, and ward. This research compared the four agglomerative hierarchical clustering methods in order to get the best cluster solution in the case of the classification of regencies/cities in East Java province based on the quality of "Keluarga Berencana" (KB) services.The results of this research showed that based on calculation of cophenetic correlation coefficient, the best cluster solution is produced by average linkage method. This method obtained four clusters with the different characteristics. Cluster 1 has an "extremely bad condition" on the qualification of KB clinics and the competence of KB service personnel. Cluster 2 has a "good condition" on the qualification of KB clinics and "bad condition" on the competence of KB service personnel. Cluster 3 has a "bad condition" on the qualification of KB clinics and "medium condition" on the competence of KB service personnel. Cluster 4 have a "medium condition" on the qualification of KB clinics and...
East Nusa Tenggara Province, according to the findings of 2013 Baseline Health Research and 2016 and 2017 Nutritional Status Surveys, was recorded as the province with the highest prevalence of stunting in Indonesia. Efforts should be made to formulate policies that are integrated with spatial aspects in order to reduce the prevalence of stunting. The LCR-GWR model approach is used by using locally compensated ridge, which were meant to adjusts to the effect of collinearity between predictor variables (i.e., the factors affecting the prevalence of stunting) in each area. Results of the analysis showed that factors affecting the prevalence of stunting in all districts/cities in East Nusa Tenggara Province are the percentage of children aged under five who were weighed ≥ 4 times, the percentage of children aged under five who receive complete basic immunization, the percentage of households consuming iodized salt, the percentage of households with decent source of drinking water and the real per capita expenditure. The analysis showed that LCR-GWR is able to produce a better model than the GWR model in overcoming local multicollinearity problems in stunting in East Nusa Tenggara Province, with lower RMSE value (0.0344) than the GWR RMSE model (3.8899).
The GSTAR and GSTARX models normally can only be formed from observed locations. The problem that sometimes occurs is that not all locations that want to be modeled have complete data as well as other locations. This study uses GSTAR and GSTARX modeling using SUR approach and combines them with the kriging interpolation technique for forecasting coffee berry borer attack in Probolinggo Regency. This modeling is called GSTAR-SUR Kriging and GSTARX-SUR Kriging. This study aims to determine the best model between GSTAR-SUR Kriging and GSTARX-SUR Kriging for forecasting coffee borer attack in an unobserved location. The result of this study shows that GSTAR-SUR Kriging and GSTARX-SUR Kriging models can be used for forecasting coffee berry borer attack in unobserved locations with high forecast accuracy shown by MAPE values <10%. In this study the GSTARX-SUR Kriging model (1,[1,12])(10,0,0) is the best model for forecasting boffee berry borer attacks in unobserved locations.
The research aimed to use Generalized Space Time Autoregressive (GSTAR) and GSTARX modeling with the Seemingly Unrelated Regression (SUR) approach and combine them with the Kriging interpolation technique in an unobserved location. The case study was coffee borer beetle forecasting in Probolinggo Regency, East Java, Indonesia, with Watupanjang Village as the unobserved location. The results show that GSTAR-SUR Kriging and GSTARX-SUR Kriging models can predict coffee borer beetle attacks in unobserved areas with high accuracy. It is indicated by the Mean Absolute Percentage Error (MAPE) values of less than 10%. The addition of exogenous variables (rainfall) into the model is proven to improve the accuracy of the model. The Root-Mean-Square Error (RMSE) value of the GSTARX-SUR Kriging model is smaller than the GSTAR-SUR Kriging model. The structure of the model produced from the research, GSTARX-SUR (1,[1,12])(10,0,0), can be used as a reference in modeling coffee borer beetle attacks in other regencies. Map of forecasting coffee borer beetle attack shows that the spread of coffee borer beetle attack is spatial clustering with the attack center located in the eastern region of Probolinggo Regency.
Environmental problems are very important issues for humanity to pay attention to. The existence of an environment with healthy, clean and beautiful conditions can provide many benefits for the survival of the community. This time, community service activities focus on environmental issues which are one of the life supports for the surrounding community in Palaan village. The main objective of this activity is to preserve and improve the reforestation process in Palaan village, Ngajum sub-district, Malang district. In addition, through this tree seed planting activity, it is hoped that it can increase the awareness of the village community on the importance of preserving the environment so that it is greener and healthier. In general, community service activities are carried out through three main stages starting from preparation, implementation and evaluation. This service activity involved students from the Faculty of Science and Technology (Saintek) Unira Malang. The process of planting seedlings is carried out in stages for 2 days. The results of the activity showed that 100 tree seedlings had been successfully planted in Palaan Village which had been designated as the planting location. Student participation showed high enthusiasm during the activity which was seen with 45 students being actively involved.
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