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All Journal Jurnal Ekonomi & Studi Pembangunan BAREKENG: Jurnal Ilmu Matematika dan Terapan International Journal Of Computer, Network Security and Information System (IJCONSIST)
Pratama, Muhamad Liswansyah
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Fix effect sur to analyze economic growth in developed and developing countries Pratama, Muhamad Liswansyah; Fitriani, Rahma; Astutik, Suci
Jurnal Ekonomi & Studi Pembangunan Vol 24, No 1: April 2023
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jesp.v24i1.17821

Abstract

This study aims to identify the relationship between population density, inflation, and unemployment rates on the human development index, GNP, export-import, and urbanization in the developed and developing countries category using the Fix Effect Seemingly Unrelated Regression (FE SUR) with a dummy variable as the slope component. This research necessitates the development of the Seemingly Unrelated Regression model, specifically the Panel Seemingly Unrelated Regression (Panel SUR) model with a dummy variable as the slope component, due to the dynamic nature of the data and the fact that the same set of predictor variables explains the five response variables. The Panel, the Seemingly Unrelated Regression model with dummy variables, can accommodate research objectives where the SUR model can explain the influence between variables, differences in characteristics between countries can be explained by fixed effect models, and differences in the effect of population density, inflation, and unemployment rates on the human development index, GNP, exports imports and urbanization in the categories of developed and developing countries can be explained by slope dummy variables. The results showed that 98.46% of the diversity of response variables (human development index, GNP, exports, imports, and urbanization) could be explained by predictor variables (population density, inflation, and unemployment rate), while the other 1.54% was explained by other factors not included in the fixed effect SUR model. In addition, the results show that population density has a significant positive relationship with GNP, imports, and exports. However, there is a significant negative relationship between unemployment and GNP. There are large differences in the relationship between the unemployment rate and GNP in developed and developing countries, whereas in developed countries, there is a larger and negative relationship compared to developing countries.
CLUSTERING WITH SKATER METHODS AND UTILIZATION OF LISA ON UNEMPLOYMENT RATE Abdila, Naufal Shela; Fitriani, Rahma; Pratama, Muhamad Liswansyah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2633-2646

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Spatial cluster analysis is an analysis used to identify a spatial pattern or geographical grouping of data. One method that can be used in spatial cluster analysis is Spatial Cluster Analysis by Tree Edge Removal (SKATER). This research aims to analyze the spatial pattern of the Unemployment Rate in East Java by utilizing the SKATER method. The clustering results are then used to create a weighting matrix, which is used to find local spatial autocorrelation values ​​using the Local Indicators of Spatial Association (LISA) index. The data is taken from BPS East Java with variables including unemployment rate, education level, minimum wage, Human Development Index, and population density. The results show that this approach is able to identify significant local spatial patterns. However, the selection of the number of clusters and input variables proved to be very influential on the results, so care needs to be taken.
Application of the Random Forest Classifier Method in Grouping Patients with Intellectual Disabilities Ainiyah, Nuchaila; Afifudin, Muhammad; Masyhuri, Reyhan Dela; Fardana, Muhamad Hakam; Wahyuningtyas, Sischa; R, Awang Putra Sembada; Pratama, Muhamad Liswansyah
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.162

Abstract

This research explores the effectiveness of the Random Forest Classifier method in grouping mental retardation patients based on their level of severity. Medical record data from mental hospitals is collected and processed to train a classification model. The preprocessing process is applied to ensure data quality before use. Model evaluation is carried out by measuring the accuracy of the scores. The research results showed that the Random Forest Classifier succeeded in classifying mental retardation patients with an accuracy of 84%. These findings show the potential of the Random Forest Classifier method as a clinical tool for doctors in determining appropriate interventions for mental retardation patients based on their level of severity.
Co-Authors Abdila, Naufal Shela Afifudin, Muhammad Ainiyah, Nuchaila Fardana, Muhamad Hakam Masyhuri, Reyhan Dela R, Awang Putra Sembada Rahma Fitriani Suci Astutik Wahyuning Tyas , Sischa