Analisis Spasial pada Model Perekonomian Sumatera Bagian Selatan

This study tries to use the Spatial concept by analyzing the observed spatial patterns and spatial autocorrelation, as well as evaluating the spatial modeling of each region in 60 districts/cities in five Southern Sumatra Provinces. This research used Geoda. Geoda will then provide a spatial description of the condition of the percentage of GRDP presented in the Moran I statistics, LISA and LISA Clusterd Map in 2015-2019. The results of this study are expected to show the spatial relationship of GRDP between 60 regencies/cities in five provinces in Sumbagsel and be able to indicate how the spatial relationship is in the clustered pattern of regions with the same characteristics. Furthermore, the LISA Cluster map is expected to describe the grouping of GRDP in 11 regions. The SAR model was chosen to analyze cases of spatial linkage. This study will further provide an economic analysis of how the percentage of the population and GRDP influence, In addition, this study will examine how the influence of the Development Index and poverty on GRDP. Therefore, this research will be one of the studies that has the latest updates because it uses two approaches; spatial approach and economic approach presented in the results of the discussion and discussion

Pola Pertumbuhan Ekonomi Daerah Otonomi Baru (DOB) Berdasarkan Tipologi Klassen di Provinsi Lampung

The abstract is written in English. The abstract contains the research objectives. The importance of regional expansion is used as one of the reasons to encourage the acceleration of economic growth in a region. In the macro analysis, the level of economic growth achieved by a country/region is measured by the development of real national income achieved by an area. Therefore, the author wants to see how the structure of economic growth in the new autonomous region after the enactment of regional expansion. So the question raised through this research is how the pattern and structure of economic growth in each DOB so that it becomes the input for the DOB concerned to increase economic growth in the area. Therefore, the purpose of this study has the main objective of finding patterns and structures of economic growth in each DOB

Daya Saing dan Pertumbuhan Ekonomi Negara Berkembang ASEAN

The purpose of this study is to look at the condition of the country's competitiveness and its influence on ASEAN economic growth. The data used consists of panel data consisting of time series data for 2009 - 2019 and a cross section of five ASEAN countries with the highest level of competitiveness. The variables used are economic growth, competitiveness, labor participation, and foreign direct investment. The analysis tool used is panel data regression, the Fixed Effect Model (FEM). The results show that competitiveness, labor participation, and foreign direct investment have a positive and significant effect on economic growth in the five developing ASEAN countries.   Keywords: ASEAN, Competitiveness, Economic Growth, and Fixed Effect Model (FEM)

Analisis Dampak Penanaman Modal Asing Dan Tenaga Kerja Asing Tiongkok terhadap Perekonomian Indonesia

This study will discuss the influence of the influx of foreign investment and Chinese foreign workers on the Indonesian economy, where cooperation between the two countries uses a turnkey project scheme. This study uses secondary data with time-series data types and is sourced from the Central Statistics Agency, the Investment Coordinating Board, and the Ministry of Manpower for the 2010-2019 period. The method used in this research is quantitative and statistical descriptive using multiple linear regression or OLS (Ordinary Least Square). The study results show a positive influence of Chinese foreign investment on the Indonesian economy and Chinese foreign workers who positively impact the Indonesian economy. Although both are below 1 percent, the percentage of Chinese foreign workers' influence on the Indonesian economy is greater than that of Chinese foreign investment

Pengaruh GDP Per Kapita, dan Konsumsi Energi Terhadap Emisi CO2 di Indonesia

This study aims to analyze the effect of GDP Per Capita and Energy Consumption on Carbon Dioxide Emissions in Indonesia in the short and long term. This study uses the Error Correction Model (ECM) method. The ECM method is used to find out how the long-term and short-term influances of the variables that affect carbon dioxide emissions. In addition, this study uses time series data in the form of annual from 1992 to 2021. The results show that the variables GDP per capita and Energy Consumption have a positive and significant effect on carbon dioxide emission in the long and short term

PENDAMPINGAN UMKM MEMPEROLEH SERTIFIKAT HALAL DI PROVINSI LAMPUNG

Dengan berakhirnya pandemi Covid-19, diharapkan sektor UMKM pulih kembali. Produk halal sangat penting dalam masyarakat saat ini. Bersamaan dengan itu, Badan Penyelenggara Jaminan Produk Halal (BPJPH) Kementerian Agama RI mengeluarkan peraturan bahwa semua produk UMKM harus bersertifikat halal pada tahun 2024. Sertifikasi halal sangat penting untuk memastikan konsumen merasa aman dan tidak melanggar aturan Islam. .Pengetahuan dan pemahaman UKM tentang sertifikasi halal dan nomor induk usaha (NIB) masih relatif minim, ditunjukkan dengan masih rendahnya jumlah UKM yang memiliki sertifikasi halal. Dalam rangka meningkatkan jumlah usaha UMKM, kegiatan pengabdian yang ditujukan kepada 60 UMKM di sekitar Universitas Lampung ini bertujuan untuk memfasilitasi sosialisasi langsung dan sertifikasi halal UMKM. Proses otorisasi sertifikasi halal saat ini menggunakan sistem online yang didahului dengan pemahaman halal dan sosialisasi NIB sehingga memudahkan dan membantu UKM dalam mendapatkan sertifikasi halal. Dari kegiatan ini diharapkan UKM di Provinsi Lampung dapat berkembang lebih baik dan lebih cepat. Kegiatan pengabdian ini bekerja sama dengan Pusat Halal Cendekiawan Muslim Kementerian Agama dan organisasi wanita Pemberdayaan Perempuan UMKM (PPUMI) Lampung

Spatial Autoregressive Model and Spatial Patterns of Poverty In Lampung Province

Many research in economics only focus on the independence of a region while neglecting the effects of space and the interaction that occurs between mutually adjacent areas. The purpose of this study is to measure the multidimensional poverty concept in 15 districts/cities in the province of Lampung in 2015-2019. Spatial analysis such as moran i statistics, LISA clustered map, and lisa signification are used to analyze spatial patterns and spatial autocorrelation. Spatial modeling with spatial autoregressive model, geoda and geographical information systems are used as explanatory spatial data and spatial modeling. The results show that the percentage of poor people between districts/cities in Lampung Province have positive Moran's I values, there is a clustered pattern in 2015-2019, Moran scatter plot depicts 4 quadrants, LISA Cluster map indicates high-high and low-low areas, and LISA map has 4 significant areas. Spatial regression results show that per capita expenditure for nonfood has a negative effect, per capita expenditure for food has a positive effect, population growth rate has a positive effect, household clean water has a positive effect, life expectancy has a negative effect, mean years of schooling has a negative effect, and simultaneously the independent variables have a significant influence on the percentage of poor people. Poverty in Lampung Province is spatially related to each other between regions, the findings suggest that the variables used affect spatially. The implication of this result is one of the basis for inter-regional policies in the interests of multi-dimensional poverty alleviation between regions.Keywords: Poverty, Spatial analysis, Spatial Autoregressive Model (SAR

Analisis Pengaruh Sektor Pertaian Terhadap PDRB Sektor Pertanian di Indonesia Tahun 2015-2021

The agricultural sector in Indonesia is an important sector in the national economy, but its role in the formation of GRDP in most regions is starting to decline. This was followed by a decrease in the area of ​​harvested land. The purpose of this study is to see the agricultural sector which acts as the basis sector in each province in Indonesia, and to determine the effect of domestic investment in the agricultural sector, the area of ​​paddy fields, and the agricultural sector's workforce on the GRDP of the agricultural sector. The data in this study were obtained from BPS and the Investment Coordinating Board (BKPM). This study uses panel data analysis with the selected model Fixed Effect Models (FEM) with the Eviews 10 application. This study also uses the Location Quotient (LQ) method to see the role of the base sector. The results of this study indicate that the agricultural sector is the basis sector in the provinces of Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Kep. Bangka Belitung, Central Java, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, North Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, West Sulawesi, Maluku and North Maluku. This study also found that the variable PMDN in the agricultural sector had a significant positive effect, the variable paddy field area had a significant negative effect and the agricultural sector labor variable had no significant effect on the GRDP of the agricultural sector in Indonesia

Analisis Pengaruh Sektor Pertanian terhadap PDRB Sektor Pertanian di Indonesia Tahun 2015-2021

The agricultural sector in Indonesia is an important sector in the national economy, but its role in the formation of GRDP in most regions is starting to decline. This was followed by a decrease in the area of ​​harvested land. The purpose of this study is to see the agricultural sector which acts as the basis sector in each province in Indonesia, and to determine the effect of domestic investment in the agricultural sector, the area of ​​paddy fields, and the agricultural sector's workforce on the GRDP of the agricultural sector. The data in this study were obtained from BPS and the Investment Coordinating Board (BKPM). This study uses panel data analysis with the selected model Fixed Effect Models (FEM) with the Eviews 10 application. This study also uses the Location Quotient (LQ) method to see the role of the base sector. The results of this study indicate that the agricultural sector is the basis sector in the provinces of Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Kep. Bangka Belitung, Central Java, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, North Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, West Sulawesi, Maluku and North Maluku. This study also found that the variable PMDN in the agricultural sector had a significant positive effect, the variable paddy field area had a significant negative effect and the agricultural sector labor variable had no significant effect on the GRDP of the agricultural sector in Indonesia

The Influence of Determinats on CO2 Emission in Indonesia for a Decade

This study aims to see the effect of GDP per capita, income inequality, and population on CO2 emissions in Indonesia from 1990-2021. This research uses a descriptive quantitative method. The data used is secondary data, in the form of annual data for 32 years. The analytical method used is the error correction model (ECM) to see the short and long-term effects between the independent variable and the dependent variable. The results of this study indicate that GDP per capita has a positive and significant effect on Indonesia, both in the short term and in the long term. The income inequality variable has a positive and insignificant effect on CO2 emissions in Indonesia in the short term. Meanwhile, in the long term, income inequality has a negative and insignificant effect on CO2 emissions in Indonesia. The population variable has an insignificant negative effect on CO2 emissions in Indonesia in the short term. However, in the long term, the population significantly affects CO2 emissions in Indonesia