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All Journal Rangkiang Mathematics Journal Indonesian Journal of Statistics and Its Applications
Mawanda Almuhayar
Universitas Andalas
Computer Science & IT Mathematics Other

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Ordinal Logistic Regression Model of Micro, Small, and Medium-Sized Enterprises Income: A Case Study of Micro, Small and Medium-Sized Enterprises in Surabaya Amalia Nur Alifah; Almira Ivah Edina; Mawanda Almuhayar
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
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.v8i2p143-154

Abstract

Micro, Small, and Medium Enterprises (MSMEs) is a business sector that is able to make a significant contribution to economic recovery in Indonesia. In Surabaya, there are many MSMEs with various fields, both food and non-food sectors which include services, trade, etc. MSMEs actually have great potential to boost the economic growth of the people of Surabaya. Especially during the COVID-19 pandemic, MSMEs owners must be able to strategize how their income can be stable or even bigger. Therefore, it is very important to know what factors can boost MSMEs income in Surabaya. In this study, it will be examined what factors can affect the income of MSMEs in Surabaya. The method used in this study is Ordinal Logistic Regression which aims to determine which independent variables or factors affect the dependent variable which in this case is MSMEs income. Based on the results of the analysis, it can be seen that the variables that affect MSMEs income are MSMEs Location, MSME Activities, and MSME Outreach. Keywords: ordinal logistic regression, MSMEs, income.
Comparison of Linear Regression and Polynomial Local Regression in Modeling Prevalence of Stunting Fadhilah Fitri; Mawanda Almuhayar
Rangkiang Mathematics Journal Vol. 4 No. 1 (2025): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v4i1.81

Abstract

Stunting is one of the main focuses of the government in Indonesia. This is because nutritional status is one of the benchmarks of community welfare. Stunting can be influenced by various societal aspects such as health, economy, social status, and education. One factor that is thought to be closely related to stunting is the level of education. Therefore, the prevalence of stunting and the level of education will be modeled; in this case, the mean years of schooling is used. Modeling uses two approaches: parametric through linear regression and nonparametric through local polynomial regression. This study compares both models to see which method better explains the stunting phenomenon. The comparison is made through the determination coefficient value or R2, Root Mean Square Error or RMSE, and the fitted curve plot. The results of R2 and RMSE for both models were obtained. The linear regression model has an R2 of 32.94% and an RMSE of 4.84. Meanwhile, for the local polynomial model, it is R2 43.44% and RMSE 4.32. Based on these results, it can be concluded that local polynomial regression is better at modeling the relationship between the prevalence of stunting and mean years of schooling in Indonesia. This finding confirms that the polynomial local regression method can capture phenomena that occur for data that do not follow a particular pattern.
Co-Authors Alifah, Amalia Nur Almira Ivah Edina Fadhilah Fitri