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All Journal Jurnal Matematika Sains dan Teknologi Jurnal Ketenagakerjaan
Alfi Hidayatullah
Politeknik Statistika STIS
Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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What Drives Gig Workers’ Welfare in Indonesia? Evidence from a Panel Data Regression 2018–2023 Andi Muh. Zulfadhil Zareka; Alfi Hidayatullah; Dara Sakina; Bunga Musva Cotva; Budiasih
Jurnal Ketenagakerjaan Vol 20 No 3 (2025): Gig Workers
Publisher : Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47198/jnaker.v20i3.543

Abstract

The rapid growth of digital technology has driven significant transformations in the labor market, marked by the emergence of the gig economy. While offering flexibility, gig workers is often accompanied by legal uncertainties and a lack of social protection, raising concerns about worker welfare. This study aims to identify the factors influencing gig workers' wages in Indonesia from economic, social, and digital perspectives. The analysis uses panel data from 34 provinces over the 2018–2023 period, sourced from BPS. The analytical method employed is the fixed effect model, corrected using the seemingly unrelated regression approach to address issues of heteroskedasticity and inter-regional correlation. The results show that inflation, human development index, open unemployment rate, ICT readiness, and ICT intensity significantly affect gig workers’ wages. ICT readiness has a positive impact, whereas the ICT intensity has a negative effect on wages. Meanwhile, ICT skills do not show a significant influence. This study highlights the need for inclusive digital policies and strengthened worker bargaining power to build a sustainable gig economy ecosystem.
Forecasting National Rice Production Using Autoregressive Distributed LAG and K-Means Clustering Dara Sakina; Alfi Hidayatullah; Yuliana Kartika Permadani; Nasrudin Nasrudin
Jurnal Matematika Sains dan Teknologi Vol. 26 No. 2 (2025)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v26i2.13428.2025

Abstract

Indonesia as an agricultural country faces various obstacles in its development. The increasing population, decreasing agricultural land area, and differences in potential in each province are the biggest problems in rice production. The limitations of research in general in presenting differences in rice production characteristics between provinces are the urgency discussed in this study. The purpose of this study is to develop a rice production forecasting model in Indonesia using the ARDL approach combined with the K-Means clustering technique. The variables used in this study are rice production, harvested area, and farmers’ terms of trade (NTP). Forecasting is carried out for the period 2024 with the aim of obtaining an accurate estimate of rice production in Indonesia. The results show that the ARDL model integrated with K-Means clustering provides highly accurate rice production forecasts, as indicated by low MAPE and RMSE values. In particular, Cluster 2 achieves the best performance with a MAPE of 0.36% and an RMSE of 3,860.40, followed by Cluster 1 (MAPE 2.17%; RMSE 33,192.31) and Cluster 3 (MAPE 5.88%; RMSE 40,577.10). In contrast, the national ARDL model without clustering records much larger errors (MAPE 14.71%; RMSE 582,062.00), confirming that clustering substantially improves forecasting accuracy and produces prediction patterns that closely match actual rice production.
Co-Authors Andi Muh. Zulfadhil Zareka Budiasih Bunga Musva Cotva Dara Sakina Dara Sakina Nasrudin Yuliana Kartika Permadani