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Kristanto Setyo Utomo
Fungsi Neraca Wilayah dan Analisis Statistik, Badan Pusat Statistik Provinsi NTT
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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ANALISIS INPUT-OUTPUT PADA STRATEGI PEMULIHAN PEREKONOMIAN, PENYERAPAN TENAGA KERJA DAN PENINGKATAN PENDAPATAN DALAM MENGATASI DAMPAK COVID-19 DI PROVINSI NTT Kristanto Setyo Utomo
Jurnal Statistika Terapan (ISSN 2807-6214) Vol 1 No 2 (2021): Jurnal Statistika Terapan
Publisher : Badan Pusat Statistik Provinsi NTT

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Abstract

The Covid-19 pandemic in Indonesia since the beginning of 2020 has had a negative impact, especially on the economic sector. Various policies to limit mobilization and community activities in preventing the spread of Covid-19 have caused the economy of East Nusa Tenggara Province to enter a recession zone in 2020. The government has tried to encourage the process of National Economic Recovery (PEN) through various policies. This study implements input-output analysis in supporting the policy to accelerate PEN through determining the priority of sectors that have the most significant impact on increasing economic growth, employment, and increasing incomes. This study indicates that encouraging an increase in final demand in the electricity, gas, and water sector will have the most significant multiplier impact on economic growth in the Province of NTT. To increase employment opportunities, the government needs to focus on increasing the output of the manufacturing sector, which will have the highest employment absorption compared to other sectors. Meanwhile, to increase income, spending on the final demand for the education sector by both the government and households will significantly impact increasing people's income in the NTT Province.
PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK PENENTUAN KLASIFIKASI KEMISKINAN MULTIDIMENSI DI PROVINSI NUSA TENGGARA TIMUR Kristanto Setyo Utomo
Jurnal Statistika Terapan (ISSN 2807-6214) Vol 2 No 01 (2022): Jurnal Statistika Terapan
Publisher : Badan Pusat Statistik Provinsi NTT

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The Covid-19 pandemic has proven to directly impact the percentage of poverty in the Province of East Nusa Tenggara. However, the determination of the size of poverty so far has been carried out using an economic dimension approach, namely the poverty line. This study classifies multidimensional poverty, namely the dimensions of health, education, economy, and life worthiness. In this multidimensional poverty classification, this research utilizes machine learning algorithms. The test results show that the Decision Tree algorithm is the best algorithm for classifying multidimensional poverty in East Nusa Tenggara Province with an accuracy rate of 82.69 percent, precision of 84.08 percent, and recall 97.56 percent. This algorithm shows that the birth attendant indicators on the health dimension and primary education on the education dimension have a high gain value. These two indicators become the primary decision node in the Decision Tree to determine multidimensional poverty that needs serious attention by the East Nusa Tenggara Provincial government.
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