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Factors to Improve Fishery Household Welfare: Empirical Analysis of Indonesia Wicaksono, Bayu Rhamadani; Yuniarti, Yuniarti; Paramartha, Dede Yoga
Economics and Finance in Indonesia Vol. 67, No. 1
Publisher : UI Scholars Hub

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Abstract

This study attempts to verify the linkage between the characteristics of fishers and the welfare of fishery household in Indonesia, which is explained by the surplus obtained by fishers. Based on the empirical results using multiple linear regression analysis, variables with significant impacts on improving the welfare of fishery household in Indonesia in both marine and inland open water fisheries are fishing gear, number of fishers, number of crew, salary, province, age, gender, education level, processed storage, transportation, and market target. Furthermore, the characteristics of fishers are divided into similarities and differences. Observed from the similarities, the main fishers play a prominent role to fulfill the daily needs of their families. Observed from the differences, fishers in marine fisheries prefer to use a boat with an inboard motor, prepare more funds, and require more crew members because they usually catch fish on long trips. On the other hand, fishers in inland open water fisheries prefer to use a boat without an inboard motor, prepare less funds, and require less crew members because they usually catch fish on short trips. The government needs to formulate effective, efficient, and targeted policies for the welfare of fishers. The findings suggest several policy recommendations related to the improvement of fishery household welfare in Indonesia, such as soft loan in the form of People Business Credit (KUR), storage facilities for a better supply chain, and revitalization of fish auction sites.
Tourism Resilience Process During Pandemic with Big Data Insight Paramartha, Dede Yoga; Deli, Nensi Fitria; Fitriyani, Ana Lailatul; Pramana, Setia
Jurnal Ikatan Sarjana Ekonomi Indonesia Vol 10 No 3 (2021): December
Publisher : Jurnal Ekonomi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52813/jei.v10i3.184

Abstract

Tourism, which is one of the pillars of the Indonesian economy, has experienced a shock due to the COVID-19 pandemic. This study aims to identify tourism resilience and its relation to the Indonesian economy during the pandemic. Subsequent to this, this study also investigates the competitiveness of tourism in responding to government policies regarding the five national tourism priorities. Descriptive analysis of data sourced from big data is used to support the analysis of tourism resilience in terms of accommodation and accessibility. In addition, Principal Component Analysis is used to build the tourism competitiveness measure of the five priority tourism destinations. The results showed that big data proxy indicators related to tourism generally show recovery signals in the new normal period, even though it hasn’t returned to its pre-pandemic condition and slightly decreased in early 2021. The improvement in this sector was mostly driven by domestic tourist. In terms of the economy, the added value of tourism has decreased considerably during 2020. In addition, based on the measure of tourism competitiveness, Central Java and North Sumatra are provinces that have good support systems for priority tourist destinations in their respective regions.
Development of Automated Environmental Data Collection System and Environment Statistics Dashboard Paramartha, Dede Yoga; Fitriyani, Ana Lailatul; Pramana, Setia
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p314-325

Abstract

Environmental data such as pollutants, temperature, and humidity are data that have a role in the agricultural sector in predicting rainfall conditions. In fact, pollutant data is common to be used as a proxy to see the density of industry and transportation. With this need, it is necessary to have automated data from outside websites that are able to provide data faster than satellite confirmation. Data sourced from IQair, can be used as a benchmark or confirmative data for weather and environmental statistics in Indonesia. Data is taken by scraping method on the website. Scraping is done on the API available on the website. Scraping is divided into 2 stages, the first is to determine the location in Indonesia, the second is to collect statistics such as temperature, humidity, and pollutant data (AQI). The module used in python is the scrapy module, where the crawling is effective starting from May 2020. The data is recorded every three hours for all regions of Indonesia and directly displayed by the Power BI-based dashboard. We also illustrated that AQI data can be used as a proxy for socio-economic activity and also as an indicator in monitoring green growth in Indonesia.