Bali is one of the hearts of tourism in Indonesia. The existence of the Covid-19 pandemic has made this tourist paradise also affected the wheels of the economy. Based on this, this study aims to determine the density clustering of one of the economic supporters in Bali, namely hospitality. The study began with the quadrant method and Ripley's K-Function to measure the distribution pattern of hospitality. From the results of the two methods, the distribution pattern of hotels in Bali is more towards clusters than random or regular distribution. If the point distribution pattern is more towards the cluster, it is continued with the Density-Based Spatial Clustering of Application Noise (DBSCAN) algorithm to form spatial clustering. In the DBSCAN algorithm, a combination of parameters, namely minimum points (MinPts) and epsilon (Eps), is carried out with evaluation using the silhouette average width value. From the results of the DBSCAN algorithm, the clustering results show that the distribution of hotels in Bali forms clusters and tends to approach the surrounding tourist attractions, such as near the beach, city market, and mountainous areas. It can help policymakers if they want to prioritize economic recovery after the Covid-19 pandemic.
Java Island is one of the areas that is very fertile and densely populated, but on the other hand, Java Island is also one of the areas that is most frequently hit by natural disasters, one of which is Klaten Regency. Natural disaster itself is an event that threatens and disrupts human life caused by nature. Some of the natural disasters that often occur simultaneously in Klaten Regency are floods, landslides, and hurricanes. These three disasters usually occur during the rainy season. This of course makes the government need to take action by seeing the large chance of a disaster occurring in order to optimize disaster management. Then research will be carried out that aims to determine the chances of natural disasters occurring in the next few years. Forecasting will be carried out using the Markov chain method, with this method the probability value of the future period can be estimated using the current period probability value based on the characteristics of the past period. So that the value of the steady state chance of floods and landslides in period 36 (December 2023) and hurricanes in period 15 (March 2022) with the chances of a disaster are 34.21%, 15.38%, and 73.53%, respectively.Received August 31, 2021Revised October 27, 2021Accepted November 11, 2021
The economy is a benchmark to determine the extent of the development of a country. Indonesia, which is now a developing country, is ranked 5th as the poorest country in Southeast Asia. Of course, the government must pay attention because until now, poverty has become one of Indonesia's main problems. Ending poverty everywhere and in all its forms is goal 01 of the Sustainable Development Goals (SDGs) program. One of the efforts that can be done is by planning as part of the implementation of the target, namely eliminating poverty and appropriate social protection for all levels of society so that the SDGs are achieved. Therefore, it is important to do a spatial analysis by making a model of poverty estimation in Indonesia and grouping to identify areas in Indonesia that have the highest poverty mission. The clustering method used in this grouping is Self Organizing Map (SOM). In this study, Spatial Autoregressive (SAR) analysis was used to create a predictive model. This is because poverty is very likely to have a spatial influence or be influenced by location to other areas in the vicinity. The results of the SAR model that can be formed are . Furthermore, the region with the highest mission is grouped using the Self Organizing Map (SOM) clustering based on variables that significantly affect the amount of poverty in Indonesia. From the results of the analysis obtained four clusters, each of which has its characteristics to classify 34 provinces in Indonesia. The clusters formed include cluster 1 consisting of 17 provinces, cluster 2 consisting of 9 provinces, cluster 3 consisting of 1 province, and cluster 4 consisting of 7 provinces.
Poverty is an essential issue for every country, including Indonesia. Poverty can be caused by the scarcity of basic necessities or the difficulty of accessing education and employment. In 2019 Papua Province became the province with the highest poverty percentage at 27.53%. Seeing this, the district groupings formed in describing poverty conditions in Papua Province are based on similar characteristics using the variables Percentage of Poor Population, Gross Regional Domestic Product, Open Unemployment Rate, Life Expectancy, Literacy Rate, and Population Working in the Agricultural Sector using K-medoids clustering algorithm. The results of this study indicate that the optimal number of clusters to describe poverty conditions in Papua Province is 4 clusters with a variance of 0.012, where the first cluster consists of 10 districts, the second cluster consists of 5 districts, the third cluster consists of 12 districts, and the fourth cluster consists of 2 districts.
COVID-19 merupakan virus yang menyerang sistem pernapasan dengan penularan yang sangat cepat yang sudah memasuki Indonesia sejak Maret 2020. Daerah dengan jumlah kasus positif terbanyak di Indonesia adalah Pulau Jawa, di mana Pulau Jawa sendiri sekaligus merupakan pulau dengan penduduk terbanyak. Sehingga pemerintah perlu memfokuskan penanganan COVID-19 untuk menekan kasus positif pada daerah yang memiliki peluang terbesar untuk naiknya kasus positif. Berangkat dari hal tersebut dilakukan penelitian ini untuk mengetahui gambaran umum kasus positif COVID-19 di Pulau Jawa dan mengetahui Provinsi yang memiliki peluang naik kasus COVID-19 tertinggi menggunakan analisis Rantai Markov. Rantai Markov merupakan metode yang dapat memperkirakan nilai probabilitas periode yang akan datang menggunakan nilai probabilitas periode saat ini berdasarkan sifat-sifat periode lalu. Didapatkan nilai peluang naik stasioner kasus COVID-19 pada Provinsi Jawa Barat dan Jawa Tengah sebesar 61,54% (Januari 2023), Provinsi Banten sebesar 65,75% (September 2022), DIY 59,82% (Mei 2022), Jakarta 46% (Agustus 2022), dan Jawa Timur sebesar 48,81% (April 2023). Sehingga dapat disimpulkan bahwa peluang naik kasus COVID-19 tertinggi yaitu pada Provinsi Jawa Barat dan Jawa Tengah yang akan stasioner pada bulan Januari 2022, untuk itu pemerintah diharapkan perlu memfokuskan perhatian kepada ketiga provinsi tersebut. Selain itu, masyarakat sekitar juga harus menjaga protokol kesehatan sebagai salah satu upaya untuk menekan peluang kasus positif COVID-19.
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