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All Journal Jurnal Matematika Jurnal Ekonomi Kuantitatif Terapan JOURNAL SCIENTIFIC OF MANDALIKA (JSM)
Eka N Kencana
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Humanities Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Mathematics Other

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Memodelkan Impor Beras Menggunakan Regresi Data Panel Eka N Kencana; Darma Arnawa; Ketut Jayanegara
Jurnal Matematika Vol 10 No 2 (2020)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2020.v10.i02.p130

Abstract

Abstract Rice is one of the world’s most important commodities. The Food and Agri-cultural Organizations estimates about 90 percent of the world’s rice is produced bycountries in the Asian continent with the rice production centers located in the ASEANregion. As an agricultural country, Indonesia is ranked third in the world rice producersafter China and India, and in the first rank of ASEAN rice producers. However, Indone-sia along with other producing countries in ASEAN also import rice. This article aimsto model rice imports from 5 ASEAN countries. Using data from FAO for the period2009–2018, 3 types of Panel Data Regression models were applied to model rice imports.The results of the analysis show Random Effect Model (REM) is the most appropriatemodel for rice imports in 5 ASEAN countries with the difference for two consecutiveyears import, consumption, and rice production was used as explanatory variables .Keywords: import, panel data, random effect, regression, rice.
Memodelkan Indeks Pembangunan Manusia Provinsi Bali dengan Regresi Data Panel Eka N Kencana
Jurnal Ekonomi Kuantitatif Terapan 2019: Vol. 12, No.2, Agustus 2019 (pp. 111-247)
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (549.486 KB) | DOI: 10.24843/JEKT.2019.v12.i02.p11

Abstract

This study is aimed to model Human Development Index (HDI) of Bali Province by applying panel data regression (PDR). HDI’s data of nine cities in Province of Bali for period 2009–2014 were used to demonstrate the ability of PDR to model. This research is directed to (1) determine which panel data regression model – Common Effect Model (CEM) vs Fixed Effect Model (FEM) – is more accurate to model the HDI’s data; and (2) to determine variable(s) that significantly affect the HDI’s. By utilizing 17 variables that are obtained from Statistics Office (BPS) of Bali, best model is developed. We concluded that FEM is more accurate than CEM in modelling HDI’s of Bali Province with coefficient of determination as much as 94.7 percent. In addition, two out of 17 variables i.e. the average length of time schooling and life expectancy proved significantly affect the HDI’s for all of cities in the Province of Bali. Keywords: HDI, life expectancy, panel data, time schooling. JEL Classification: O150, C510, C520.
Regresi Logistik Multinomial terhadap Faktor Pemanfaatan Jaminan Kesehatan Pasien Rawat Jalan Provinsi Bali 2023 Ni Luh Gede Arun Dayanti Purwa; Made Susilawati; IPW Gautama; Ni Luh Putu Suciptawati; Eka N Kencana; Ketut Jayanegara
Journal Scientific of Mandalika (JSM) e-ISSN 2745-5955 | p-ISSN 2809-0543 Vol. 6 No. 12 (2025)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/10.36312/vol6iss12pp4450-4460

Abstract

Public health is one of the indicators used to measure a country's level of welfare. In the 2020–2024 National Medium-Term Development Plan (RPJMN), the government targets Universal Health Coverage (UHC) through health insurance programs. The utilization of health insurance can be influenced by various individual characteristics. This study aims to analyze the factors influencing the utilization of health insurance during outpatient care in Bali Province, using data from the March 2023 Susenas survey. In this study, a multinomial logistic regression model was used, with the dependent variable categorized into not using, BPJS PBI, and BPJS non-PBI. The independent variables analyzed include age, gender, marital status, education level, employment status, regional typology, and outpatient visit frequency. The analysis results showed that the model's accuracy was 61.63%, with significant variables including age, marital status, education level, employment status, regional typology, and frequency of outpatient care. These findings indicate that individual characteristics play a significant role in determining the utilization and type of health insurance used.
Co-Authors Darma Arnawa I KOMANG GDE SUKARSA IA KOMANG MERIANI IPW Gautama Ketut Jayanegara Made Susilawati Ni Luh Gede Arun Dayanti Purwa Ni Luh Putu Suciptawati NI PUTU JULIANINGSIH