K-MEANS CLUSTERING FOR GROUPING INDONESIA UNDERDEVELOPED REGIONS IN 2020 BASED ON POVERTY INDICATORS

Poverty is still a problem in Indonesia, especially in underdeveloped areas. Underdeveloped areas are areas where the region and its people are less developed than other regions on a national scale. The classification of disadvantaged areas is determined by the president in the Presidential Regulation of the Republic of Indonesia Number 63 of 2020 concerning the Determination of Underdeveloped Regions of 2020-2024. Various policies need to be set by the government to overcome poverty in underdeveloped areas. Program planning strategies may be different for each region. Therefore, in order to achieve an optimal implementation of poverty alleviation programs, it is necessary to group the districts covered in underdeveloped areas in Indonesia based on poverty indicators. The data used is macro data from the characteristics of each region in disadvantaged areas obtained from regional publications in the figures for each district. From the results of the analysis of k means clustering formed three groups with different characteristics in each cluster. In cluster one, the focus of government policies is on employment and sanitation aspects, cluster two is on health, education, and employment aspects, cluster three is on all aspects because cluster three is the area with the highest percentage of poor people compared to the other two clusters. The high percentage of poor people is also followed by other poor aspects.

An Integrated Analysis of GWR Models and Spatial Econometric Global Models to Decompose the Driving Forces of the Township Consumption Development in Gansu, China

The investigation of township consumption patterns has become highly significant in order to emphasize the importance of township consumption patterns in economic development and policy formulation. To attain township consumption development in underdeveloped areas is a significant way to meet the general criterion of “rich life” under China’s Rural Revitalization strategy. The primary objective of this study is to evaluate the driving forces that contribute to the development of township consumption in underdeveloped areas such as Gansu Province, China, and then scientifically design and implement a strategy for township consumption development in Gansu, all of which are related to the broader interests of rural revitalization. The study used 1233 township data of Gansu Province, China. The study integrated geographically weighted regression (GWR) and a spatial econometric global (SEG) model for data analysis and interpretation. The integration of these two models can comprehensively capture both spatial heterogeneity and spatial independence concurrently. First, we conducted integrated analyses of GWR and SEG models using consistent settings of spatial weight matrix elements, with GWR focusing on spatial heterogeneity and SEG models on spatial spillover. Second, the permanent resident population, the number of financial institution outlets, the types of townships, and the characteristics of townships had a substantial significant effect on the development of township consumption in Gansu, China. In addition, the ratio of residents with access to basic medical insurance was found to be negatively significant. The revitalization strategy for township consumption in Gansu Province, China should prioritize increasing the permanent resident population of townships, accelerating the development of township urbanization, accelerating the construction of township consumption infrastructures, and strengthening financial support from township financial institutions.

An Empirical Study on Dynamic Evolution of Industrial Structure and Green Economic Growth—Based on Data from China’s Underdeveloped Areas

From the experiences of developed countries or areas, advanced industrial structure is an effective way to promote economic transformation and high-quality growth. This paper uses the economic development data of seven underdeveloped provinces in China in 10 years to study the relationship between industrial structure upgrading, industrial structure rationalization and green economic growth. The result shows: (1) The relationship between the upgrading of industrial structure and green total factor productivity (GTFP) is a non-linear relationship that is difficult to fit. (2) There are two turning points in the relationship curve between industrial structure upgrading and green total factor productivity (these can be called “rationalization points”). (3) The “rationalization points” are affected by the rationalization of the industrial structure. (4) The “rationalization point” divides the relationship curve into three intervals. Within the threshold range [0.661, 0.673] of the rationalization of the industrial structure, the upgrading of the industrial structure promotes the increase of green total factor productivity, while outside the range, the upgrading of the industrial structure inhibits the increase of green total factor productivity. Therefore, industrial development in underdeveloped areas should first implement rationalization of industrial structure. After the rational adjustment of the industrial structure, we will then develop a high-level industrial structure to improve the green TFP.

Pengaruh Penyaluran Dana Desa pada Indikator Perekonomian Kabupaten Daerah Tertinggal di Indonesia

ABSTRACT The different characteristics in each region in Indonesia make the pattern of economic activity, infrastructure development and human resources in each region are not similar. Thus have implications to inequality problem. There are regions have growth but also there are regions that have growth slowly or can be called underdeveloped areas. To overcome this problem, the central government directs development activities with the main focus on villages and underdeveloped areas. Underdeveloped areas are considered to be lagging behind in various economic and development aspects. The Village Law has mandated the central government to distribute the Village Fund. This study aims to determine the effect of Village Fund distribution on two economic indicators in 122 underdeveloped areas. The analytical method that used in this research is a simultaneous equation model consist of two structural equations. The results showed that the distribution of village funds had a good effect on two economic indicators of underdeveloped areas. Hopefully, the government in underdeveloped areas can use the village fund well to improve the economy in their area. ABSTRAK Perbedaaan karakteristik di setiap wilayah di Indonesia mengakibatkan pola pembangunan ekonomi infrastruktur dan sumber daya manusia di tiap daerah menjadi tidak seragam. Hal tersebut berimplikasi pada munculnya masalah ketimpangan. Ada daerah yang maju lebih cepat dan ada juga daerah yang tumbuh lebih lambat atau bisa dikatakan sebagai daerah tertinggal. Untuk mengatasi permasalahan tersebut, pemerintah pusat mengarahkan kegiatan pembangunan di daerah dengan fokus utama desa dan daerah tertinggal. Daerah tertinggal dianggap masih tertinggal di berbagai aspek ekonomi dan pembangunan. Undang-Undang Desa memberikan mandate kepada pemerintah untuk menyalurkan Dana Desa. Penelitian ini bertujuan untuk mengetahui pengaruh penyaluran Dana Desa terhadap dua indikator perekonomian pada 122 kabupaten daerah tertinggal. Metode analisis yang digunakan adalah model persamaan simultan yang terdiri dari dua persamaan struktural. Hasil penelitian menunjukkan bahwa penyaluran dana desa memberikan pengaruh yang baik bagi dua indikator perekonomian daerah tertinggal. Dengan demikian pemerintah di kabupaten daerah tertingga dapat mengoptimalkan penggunaan Dana Desa yang telah disalurkan guna peningkatan perekonomian.

Classification of Underdeveloped Areas in Indonesia Using the SVM and k-NN Algorithms

The determination or classification of underdeveloped areas essentially consists of classifying several observations taking into account existing indicators. The classification method used is K-Nearest Neighbor (k-NN) and Support Vector Machines (SVM). This study aims to analyze the accuracy of the classification between SVM and k-NN algorithms in the classification of underdeveloped areas in Indonesia. The data source used in this study is secondary data obtained from the Central Bureau of Statistics (BPS). The data used are 514 districs and municipalities of Indonesia. After analysis, the conclusion is that there are 122 districs and municipalities that are left behind out of a total of 514 districs and municipalities in Indonesia. The most underdeveloped areas are on the island of Papua, followed by the areas of the islands of Bali and Nusa Tenggara, and Sulawesi. Based on the results of the classification of underdeveloped areas using the method SVM with the kernel RBF has the best results with the parameters C = 1 and γ = 0.05 while the results of the classification of underdeveloped areas using the method k-NN obtains the best results with k = 15 Based on the results of classification of underdeveloped areas using the SVM and the k-NN method, including the level of classification is very good. The two methods compared have the same precision value of 92.2% and can be used to determine the classification of underdeveloped areas. Keywords: classification, machine learning, supervised learning, underdeveloped areas.