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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Economics and Finance in Indonesia Jurnal Pariwisata Nusantara VARIANSI: Journal of Statistics and Its Application on Teaching and Research PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Neli Agustina
Politeknik Statistika STIS
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences

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 1 
The Impact of Human Capital on Shadow Economy in Indonesia Saraswati Saraswati; Neli Agustina
Economics and Finance in Indonesia Volume 66, Number 1, June 2020
Publisher : Institute for Economic and Social Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (203.308 KB) | DOI: 10.47291/efi.v66i1.629

Abstract

Shadow economy is a market for legal and illegal goods and services that escape recording and estimation of GDP. It can cause inaccurate estimation of GDP, declining tax revenue, and less precise economic policies. Improving the quality of human capital, both in education and health dimensions, can reduce shadow economy. The research aims to estimate shadow economy and analyze the influence of the quality of human capital on shadow economy in Indonesia. Applying time series multiple linear regression analysis, the findings show that the average shadow economy in Indonesia is 28.97 percent, changes in life expectancy negatively affect changes in shadow economy, while changes in the gross participation rate of tertiary education have a positive effect.
Comparison of Forecasting Model Using Chen and Lee High Order Fuzzy Time Series (Farmer’s Terms of Trade of Crops Subsector in Nusa Tenggara Timur Province Case) Fais Muzaki; Neli Agustina
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.42000

Abstract

The farmer’s terms of trade of food crops subsector (NTPP) in Nusa Tenggara Timur Province has always been below 100 in 2019-2023. Food crops are a substantial agricultural subsector in which its contribution to the PDRB is significant and concerns the food adequacy of the region. NTPP is a proxy indicator to see farmers’ welfare and its value is expected to grow periodically. Therefore, predictive modeling is required to know future NTPP values and to know the purchasing power of food crop farmers. The aim of this research is to compare the accuracy of Chen and Lee model with the high order fuzzy time series for NTPP forecasting in Nusa Tenggara Timur Province. This research uses monthly data from NTPP Nusa Tenggara Timur from January 2016 to October 2024. The research results show that additions up to the 3rd order increase forecast accuracy and the Lee model is more accurate than the Chen model seen from the smaller RMSE and MAPE values. The MAPE values ​​of the 3rd order fuzzy time series Chen and Lee model are 0.5453% and 0.5088% respectively. Based on the MAPE value, the 3rd order Lee model is the most accurate in forecasting NTPP in Nusa Tenggara Timur Province.    
Analysis of Indonesian Tourism Demand by Foreign Tourist in ASEAN Region Jasmine Abqoriyyah Haryanto; Neli Agustina
Jurnal Pariwisata Nusantara (JUWITA) Vol. 4 No. 3 (2025): Jurnal Pariwisata Nusantara
Publisher : PROGRAM STUDI PARIWISATA SYARAH, FAKULTAS EKONOMI DAN BISNIS ISLAM, UNIVERSITAS ISLAM NEGERI MATARAM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20414/juwita.v4i3.14561

Abstract

Purpose: This study aims to assess the overall pattern of ASEAN tourist visits to Indonesia and investigate the potential driving tourism demand between 2017 to 2022. The study focuses on variables believed to influence ASEAN travelers’ interest in Indonesia, such as per capita income, relative pricing, substitution prices, currency exchange rates, and population size. Method: This study employs panel data covering 9 ASEAN countries—excluding Indonesia and Timor Leste—namely Malaysia, Singapore, Thailand, the Philippines, Laos, Brunei Darussalam, Vietnam, Cambodia, and Myanmar, as the cross-sectional units over the period from 2017 to 2022. The analysis is based on secondary data sourced from BPS, UNCTAD, and the World Bank. The dependent variable is the number of tourists from ASEAN countries. Panel data regression is utilized to identify the factors influencing the demand for tourism in Indonesia by ASEAN visitors during the 2017–2022 Result: The result of the study indicate that the population and per capita income of ASEAN nations have a significant positive impact on ASEAN travelers' demand for travel to Indonesia. In contrast, the fluctuating cost of substituting Indonesian travel with travel to Malaysia, Singapore, and Thailand has a significantly negative impact on the demand for Indonesian tourism. The relative price variable and the Rupiah exchange rate do not significantly impact the demand for Indonesian tourism by ASEAN travelers. Contribution: The main contribution of this research is a recommended government policy aimed at maintaining price stability in Indonesia to enhance its competitiveness within the ASEAN region. Additionally, efforts should be made to promote domestic tourism through social media campaigns and to enhance the quality and service standards of the tourism sector to boost Indonesia’s appeal. Ensuring currency stability involves implementing a combination of economic and monetary policies, such as balancing currency supply and demand, maintaining macroeconomic stability, managing inflation, and fostering both domestic and foreign investment.
ARIMA Modeling for Forecasting Indonesia’s Per Capita CO2 Emissions and Literature Study of Photobioreactor-Based Mitigation Henny Syahrani; Fadli Ardiansyah; Nabila Nur Faiza; Samuel Kevin; Neli Agustina
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 02 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm650

Abstract

Increasing CO2 emissions from fossil fuels, industrial activities, and transportation drives climate change. Although Indonesia has committed to reducing emissions by 29% unconditionally and up to 43% with international support by 2030 through its Nationally Determined Contribution, per capita emissions rise. This study forecasts Indonesia’s per capita CO2 emissions using ARIMA based on data from 1950-2024 from Our World in Data. Data from 1950-2009 served as training and 2010-2024 as testing. The Augmented Dickey-Fuller test indicated non-stationarity at level and stationarity after first differencing. ARIMA(0,1,10) was selected based on the lowest AIC and SC, highest adjusted R², and satisfactory diagnostics. MAPE was 13.19%. Forecasts for 2025-2030 show emissions increasing from 2.95 to 3.75 tons per capita. The study evaluates IMC-PBR, which removes atmospheric through photosynthesis while generating biomass.
Shadow Economy Estimation Across ASEAN Member States: MIMIC Model Approach Ahmad Nadifa Al Agung; Neli Agustina
Joint International Conference 2026: The 2nd Indonesia Forum on Labour and Productivity (IFLP) & The 4th International Conference on Data Science and Official Statistics (ICDSOS) Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.578

Abstract

As a measure of official output, GDP remains incomplete, omitting the substantial economic transactions that occur within the shadow economy. The shadow economy reduces government tax revenues and weakens fiscal capacity. It also contributes to the underestimation of macroeconomic indicators. This study estimates the size of the shadow economy in ASEAN member states (AMS) using the Multiple Indicators and Multiple Causes (MIMIC) model. The model employs three causal variables and two indicator variables to capture the latent construct. Inflation, unemployment rate, and GDP per capita growth are identified as the main causal determinants. Economic growth and M2 growth are validated as significant indicators constructed for the shadow economy. The estimation covers the period from 2000 to 2023 and reveals an upward trend in the shadow economy across ten AMS, with an average size of 37.75 percent of GDP. These findings emphasize the need for policy actions that focus on maintaining price stability, promoting inclusive economic growth, and expanding formal employment opportunities to mitigate the expansion of the shadow economy.
Co-Authors Ahmad Nadifa Al Agung Fadli Ardiansyah Fais Muzaki Henny Syahrani Jasmine Abqoriyyah Haryanto Nabila Nur Faiza Samuel Kevin Saraswati Saraswati